Citation
Modeling Land Surface Fluxes and Microwave Signatures of Growing Vegetation

Material Information

Title:
Modeling Land Surface Fluxes and Microwave Signatures of Growing Vegetation
Creator:
Casanova, Joaquin Jesu
Place of Publication:
[Gainesville, Fla.]
Publisher:
University of Florida
Publication Date:
Language:
english
Physical Description:
1 online resource (105 p.)

Thesis/Dissertation Information

Degree:
Master's ( M.E.)
Degree Grantor:
University of Florida
Degree Disciplines:
Agricultural and Biological Engineering
Committee Chair:
Judge, Jasmeet
Committee Members:
Jones, James W.
Zmuda, Henry
Graduation Date:
12/14/2007

Subjects

Subjects / Keywords:
Biomass ( jstor )
Heat flux ( jstor )
Microwaves ( jstor )
Modeling ( jstor )
Simulations ( jstor )
Soil moisture ( jstor )
Soil science ( jstor )
Soil temperature regimes ( jstor )
Soils ( jstor )
Vegetation ( jstor )
Agricultural and Biological Engineering -- Dissertations, Academic -- UF
Genre:
Electronic Thesis or Dissertation
born-digital ( sobekcm )
Agricultural and Biological Engineering thesis, M.E.

Notes

Abstract:
Soil moisture in the root zone is an important component of the global waterand energy balance, governing moisture and heat fluxes at the land surface and at the vadose-saturated zone interface. Typically, soil moisture estimates are obtained using Soil-Vegetation-Atmosphere Transfer (SVAT) models. However, two main challenges remain in SVAT modeling. First, most models often oversimplify the coupling between vegetation growth and surface fluxes, and second, model errors accumulate due to uncertainty in parameters and forcings, and numerical computation. The ultimate goal of this research is to improve estimates of root-zone soil moisture and ET by linking an SVAT model with a crop growth model, and assimilating remotely-sensed observations sensitive to soil moisture, such as microwave brightness (MB). Toward that goal, a coupled SVAT-Crop model will be developed, calibrated, and linked to an MB model, to comprise the forward model for data assimilation. The models will use observations from three season-long field experiments monitoring growing sweet corn. ( en )
General Note:
In the series University of Florida Digital Collections.
General Note:
Includes vita.
Bibliography:
Includes bibliographical references.
Source of Description:
Description based on online resource; title from PDF title page.
Source of Description:
This bibliographic record is available under the Creative Commons CC0 public domain dedication. The University of Florida Libraries, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.
Thesis:
Thesis (M.E.)--University of Florida, 2007.
Local:
Adviser: Judge, Jasmeet.
Statement of Responsibility:
by Joaquin Jesu Casanova.

Record Information

Source Institution:
University of Florida
Holding Location:
University of Florida
Rights Management:
Copyright Casanova, Joaquin Jesu. Permission granted to the University of Florida to digitize, archive and distribute this item for non-profit research and educational purposes. Any reuse of this item in excess of fair use or other copyright exemptions requires permission of the copyright holder.
Resource Identifier:
662636781 ( OCLC )
Classification:
LD1780 2007 ( lcc )

Downloads

This item has the following downloads:

casanova_j.pdf

casanova_j_Page_040.txt

casanova_j_Page_031.txt

casanova_j_Page_065.txt

casanova_j_Page_078.txt

casanova_j_Page_046.txt

casanova_j_Page_027.txt

casanova_j_Page_063.txt

casanova_j_Page_077.txt

casanova_j_Page_014.txt

casanova_j_Page_103.txt

casanova_j_Page_067.txt

casanova_j_Page_099.txt

casanova_j_Page_054.txt

casanova_j_Page_050.txt

casanova_j_Page_072.txt

casanova_j_Page_088.txt

casanova_j_Page_053.txt

casanova_j_Page_003.txt

casanova_j_Page_101.txt

casanova_j_Page_022.txt

casanova_j_Page_020.txt

casanova_j_Page_095.txt

casanova_j_Page_037.txt

casanova_j_Page_048.txt

casanova_j_Page_097.txt

casanova_j_Page_034.txt

casanova_j_Page_052.txt

casanova_j_Page_021.txt

casanova_j_Page_004.txt

casanova_j_Page_044.txt

casanova_j_Page_059.txt

casanova_j_Page_081.txt

casanova_j_Page_086.txt

casanova_j_Page_023.txt

casanova_j_Page_043.txt

casanova_j_Page_096.txt

casanova_j_Page_076.txt

casanova_j_Page_039.txt

casanova_j_Page_094.txt

casanova_j_Page_069.txt

casanova_j_Page_011.txt

casanova_j_Page_028.txt

casanova_j_Page_085.txt

casanova_j_Page_015.txt

casanova_j_Page_030.txt

casanova_j_Page_098.txt

casanova_j_Page_056.txt

casanova_j_Page_049.txt

casanova_j_Page_089.txt

casanova_j_Page_002.txt

casanova_j_Page_061.txt

casanova_j_Page_017.txt

casanova_j_Page_058.txt

casanova_j_Page_055.txt

casanova_j_Page_033.txt

casanova_j_pdf.txt

casanova_j_Page_009.txt

casanova_j_Page_008.txt

casanova_j_Page_038.txt

casanova_j_Page_047.txt

casanova_j_Page_051.txt

casanova_j_Page_036.txt

casanova_j_Page_016.txt

casanova_j_Page_010.txt

casanova_j_Page_032.txt

casanova_j_Page_082.txt

casanova_j_Page_006.txt

casanova_j_Page_041.txt

casanova_j_Page_075.txt

casanova_j_Page_102.txt

casanova_j_Page_057.txt

casanova_j_Page_045.txt

casanova_j_Page_100.txt

casanova_j_Page_104.txt

casanova_j_Page_092.txt

casanova_j_Page_019.txt

casanova_j_Page_062.txt

casanova_j_Page_066.txt

casanova_j_Page_087.txt

casanova_j_Page_035.txt

casanova_j_Page_005.txt

casanova_j_Page_042.txt

casanova_j_Page_060.txt

casanova_j_Page_007.txt

casanova_j_Page_013.txt

casanova_j_Page_018.txt

casanova_j_Page_084.txt

casanova_j_Page_105.txt

casanova_j_Page_064.txt

casanova_j_Page_073.txt

casanova_j_Page_071.txt

casanova_j_Page_091.txt

casanova_j_Page_026.txt

casanova_j_Page_068.txt

casanova_j_Page_024.txt

casanova_j_Page_080.txt

casanova_j_Page_079.txt

casanova_j_Page_001.txt

casanova_j_Page_074.txt

casanova_j_Page_012.txt

casanova_j_Page_025.txt

casanova_j_Page_083.txt

casanova_j_Page_090.txt

casanova_j_Page_070.txt

casanova_j_Page_093.txt

casanova_j_Page_029.txt


Full Text































(b)


,-O a


r 10

m
E
O 5
m
P
o


DoY 2004


0



0.3



E 0.2

m 0.




0


80 90 100


110 120
DoY 2004


130 140 150


80 90 100 110 120 130 140 150
DoY 2004


80 90 100 110 120 130 140 150
DoY 2004


Comparison of estimations by the coupled LSP-DSSAT and stand-alone
DSSAT model simulation and those observed during MicroWEX-2: (a) dry
biomass, (b) LAI, (c) 5 cm soil moisture, and (d) ET.


Figure 4-4.












*MicroWEX-2 LSP -- -LSP-DSSAT


1000

800-

E 600- a

S400-


S200t, r b


-200
125 126 127 128 129 130 131 132 133 134 135



300-
(b)
200-
100 .


iti -100
-200
-300-
125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)

Figure 4-19. Comparison of sensible heat flux, between DoY 125 to 135, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


The coupled LSP-DSSAT model estimates radiation, fluxes, and soil moisture and

temperature profiles that are very similar to those estimated by the stand-alone LSP

model with observed vegetation parameters for the growing season, as shown in Figures

4-27-4-29 and Tables 4-4-4-6. The RMSDs for the fluxes from the LSP-DSSAT model

are slightly higher (by ~3 W/m2) than those from the LSP model, primarily because

modeled c .Ilr-pi- characteristics used in the LSP-DSSAT model rather than observations.

For instance, LSP-DSSAT overestimates LAI by 0.29, compared to the stand-alone DSSAT

which overestimates by 0.06 (Figure 4-4(c)), increasing calrs..pi- interception and net

radiation.

Overall, both the LSP and LSP-DSSAT models capture the diurnal variations and

phases for net radiation (Figure 4-27(a)) throughout the growing season. The RMSDs



























I I 100f(30.48mh 20ft (60.96m) (00ft(30.48m 100 ff


Dlrection
of plerting


600 ft (182.88m)


(3: Wells :Fooltprint(7,62m 9.14m of 251ft30ft) X : Rainguages


S: CR23x, writh soil moisture", soil temperature*, and soil heat flux :TER

S: CR23x, wth ECS
V.S
S: C~Rt, with soil moisture*, soil temperaltue", and soil heat lux

*: The sensors are installed at depth: 2, 4, 8, 16, 32, 64, and 120 em


S:Radlometer


) .CNR

174mlaina
:Samptling?
Areal


Figure 2-6. Map of the field site during MicroWEX-4.














Flux RMSD MAD Bias RMSD MAD Bias


Depth (cm) RMSD MAD Bias RMSD MAD Bias


Depth (cm) RMSD MAD Bias RMSD MAD Bias


Comparison of surface fluxes (W/m2), foT Stand-alone LSP and coupled
LSP-DSSAT simulations.


Table 4-4.


Stand-Alone LSP


Coupled LSP-DSSAT


Net Radiation
Latent Heat Flux
Sensible Heat Flux
Soil Heat Flux


23.86
46.34
34.48
47.68


16.11
32.03
24.07
26.24


10.38
14.96
15.69
-1.54


25.62
50.69
37. 19
46.54


18.12
35.28
24.79
25.02


12.65
18.84
14.88
-1.83


Table 4-5. Comparison of volumetric soil moisture (m3 m3), foT Stand-alone LSP and
coupled LSP-DSSAT simulations.


Stand-Alone LSP


Coupled LSP-DSSAT


0.047
0.035
0.036
0.032
0.062
0.060


0.044
0.029
0.030
0.031
0.061
0.057


0.044
0.029
0.028
0.031
0.061
0.057


0.046
0.034
0.036
0.032
0.062
0.060


0.043
0.028
0.030
0.031
0.061
0.057


0.043
0.028
0.028
0.030
0.061
0.057


growing stages. The model simulations were conducted using calibrated parameter values

given in Table 4-2. This section discusses statistics for coupled LSP-DSSAT model

simulation, but Tables 4-4-4-6 provide detailed statistics for both the coupled LSP-DSSAT

and the stand-alone LSP model simulation.

Early Season Almost Bare Soil

This period included the first 27 d on oOf the growing season (DoY 78-105), when

it was ,In1bare soil with low vegetation. The canopy height was < 17 cm, LAI

was < 0.2, and vegetation cover was < 0.22. Figures 4-5(a) and (b) show the estimated


Table 4-6. Comparison of soil temperature (K(), for stand-alone LSP and coupled
LSP-DSSAT simulations.


Stand-Alone LSP


Coupled LSP-DSSAT


2.80
2.88
2.6;0
2.03
1.70
1.26


2.22
2.21
2.03
1.56
1.24
0.91


1.90
1.73
1.73
1.40
1.09
0.44


2.43
2.56
2.27
1.76
1.45
1.12


1.91
2.00
1.77
1.41
1.15
0.90


1.37
1.21
1.22
0.93
0.67
0.09









CHAPTER 3
CALIBRATION OF A CROP GROWTH MODEL FOR SWEET CORN

3.1 Introduction

This chapter describes the calibration of a crop growth model for a growing season

of sweet corn in north-central Florida. There are two 1!! lb ~r corn growth models, EPIC

(Erosion Productivity Impact Calculator) [65] and CERES-Maize [28], that simulate

hydrology, nutrient cycling, growth, and development. CER ES-Maize has the advantage

of being part of the well-known Decision Support System for Agrotechnology Transfer

Cropping System Model (DSSAT-CSM). DSSAT has been widely used for a number of

years, with validated models for over 15 crops. It also allows for simulations of multi-year

crop rotations [29].

3.2 CERES-Maize Model

The CERES-Maize model is a part of the crop growth submodule in DSSAT-CSM.

D SSAT-C SM is a modular crop simulation model with modules for soil, soil-plant-atmosphere

crop development and growth, weather, management, etc. A simulation consists of several

stages: season and run initialization, rate calculation, integration, and output generation

[29]. The model determines total dry biomass using the radiation use efficiency method.

Total solar radiation is partitioned into photosynthetically active radiation (PAR), and

the fraction intercepted is calculated from LAI using Beer's law [54]. The dry matter

accumulation rate is a product of radiation use efficiency and a conversion factor. Maize

growth and development is marked by eight events: germination, emergence, end of

juvenile phase, floral induction (tassel initiation), 7 ".' silking, he ginning grain fill,

maturity, and harvest. Transition from one developmental stage to the next is determined

by the growing degree d .n~ (GDD) with a base temperature of 80C. Vegetative growth

stops on '7".' silking, when reproductive growth begins in the form of grain fill. Yield is

the grain fill value at harvest. Threshold GDD for each stage and grain fill parameters are

contained in a cultivar file.










through the canopy, a microwave transmission model for growing vegetation is an

important component of the MB model.

Microwave emission models for dynamic vegetation during the growing season require

accurate estimation of canopy emission and attenuation. Non-scattering attenuation is

described by canopy optical depth (-r) that primarily depends upon the distribution of

moisture in the < Ilr.).i- Several methods have been investigated for determining canopy

optical depth. For example, Ulaby and Wilson[58] modeled -r of the wheat canopy as

a uniform cloud of wet biomass with leaves and stems treated separately. In addition,

polarization dependence was included for stem attenuation. Eom [21] developed a model

for -r applicable to row structured canopies such wheat or corn. The model accounts for

azimuthal anisotropy in -r by modeling the < Ilr..i- as a random collection of dielectric

spheroids. This method matched well with observations but requires a computationally

intensive solution of the radiative transfer equation. Jackson and Sclbnan -~- [51], used

the results of many studies and developed an empirical model for -r. In their model, -r is

estimated as the product of a fre. g. n. s -i-dependent constant b and water column density

(kg/m2) in the can..).li-. The Jackson model is flexible but has little physical basis, with

b often used as a fitting parameter in emission models or estimated empirically [61].

England and Galantowicz [19] developed a refractive model for estimating optical depth of

grass based upon vertical profiles of moisture content within the grass canopy.

In this thesis, an SVAT model, viz. the LSP model, is coupled with a widely-used and

well-tested crop growth model, the Decision Support System for Agrotechnology Transfer

Cropping System Model (DSSAT-CSM) [29]. The models are calibrated using observations

from the Microwave Water and Energy Balance Experiment 2 (11seroWEX-2), one of three

season-long experiments monitoring growing sweet corn (11seroWEXs 2, 4, and 5). A

hiophysically-based canopy transmission model is developed for growing sweet corn, using

data from MicroWEXs 4 and 5. This -r model is included in a simple MB model that is

linked with the LSP-DSSAT model.




















Figure 2-3. The Eddy Covariance System.










Figure 2-4. The net radiometer used during the MicroWEXs.


and 120 cm (100 cm during MicroWEX-2) using Campbell Scientific Water Content

Reflectometers and Vitel Hydra- probes, and thermistors and thermocouples, respectively.

An Eddy Covariance System (Figure 2-3) measured wind speed, direction, and latent and

sensible heat fluxes. REBS CNR net radiometer (Figure 2-4)measured up- and down-

welling short- and longf- wave radiation. Everest Interscience infrared sensor measured

thermal infrared temperature. Four tipping-bucket rain gauges Ic------ 4 precipitation at

four locations East and West of the footprint, and at the East and West sides of the field.

Water table depth was measured using Solinst Level L?-~-;- s in a monitoring well in each

quadrant .

In addition to continuously 1- -- 4 data, there were also weekly vegetation and

twice-weekly soil samplings (during MicroWEX-2 only). Vegetation sampling was

conducted in four areas, one in each quadrant of the field. Samples were selected by

placing a meter stick half-way between two plants and ending the sample at least 1 m

from the starting point and half-way between two plants. The actual row length of the

sample was noted. Stand density, leaf number, canopy height and width, wet and dry









1.1 Thesis Ob jectives

This thesis answers the following research questions:

1. What values for the six corn cultivar coefficients give the best DSSAT model

performance for both biomass and LAI for the MicroWEX-2 growing season?



2. How do the model estimates for biomass and LAI compare with MicroWEX-2

observations? (C'h! Ilter 3)

3. What values of the twelve calibrated parameters give the best LSP model performance

for both latent heat flux and near surface soil moisture for the MicroWEX-2 growing

season? (C'!s Ilter 4)

4. How do the model estimates of soil moisture, temperature, and surface fluxes

compare with MicroWEX-2 observations? (C'h! Ilter 4)

5. What is the impact of coupling on both LSP and DSSAT model estimates of LAI,

biomass, soil moisture, temperature, and surface fluxes? (C'h! Ilter 4)

6. How does a physically-based -r model compare to Jackson's widely-used empirical

model? (C'h! Ilter 5)

7. How do the brightness estimates predicted by the linked LSP-DSSAT-MB model

compare to observations during MicroWEX-2? (C'!s Ilter 5)

1.2 Thesis Format

The C'!s Ilter 2 of this thesis describes the field experiments, MicroWEXs 2, 4, and

5. In C'!s Ilter 3, the DSSAT model's corn submodel, CERES-Maize, is calibrated for the

MicroWEX-2 growing season. In C'!s Ilter 4, the LSP model is calibrated and coupled

with DSSAT model. In ('! .pter 5, a calrpli- transmission model for growing sweet corn is

developed and tested in a simple MB model, linked with the LSP-DSSAT model.










Question 6:"How does a physically-based -r model compare to Jackson's

widely-used empirical model?".

The -r obtained from the biophysical model estimated higher values than the Jackson,

with an RMSD between the two of up to 0.23 Np. The -r values obtained from the two

approximations were used in a microwave emission model at C-band, the model estimates

of Tg matched well with observations using both -r values when w is included, with similar

RMSDs of ~ 5 K(.

Question 7~:"How do the brightness estimates predicted by the linked LSP-

DSSAT-MB model compare to observations during MicroWEX-2?"

Preliminary results indicate the MB model is lacking in two main areas, the roughness

model and the w model. For the first half of the season, where bare soil brightness is more

important, the constant roughness or specular surface models over- and under-estimate

brightness, respectively, both with RMSDs ~ 33 K(. During the later half of MicroWEX-2,

brightness is overestimated with RMSD of ~ 11 K(.

















S300

280
80 85 90 95 100 105

320


S300

280
80 85 90 95 100 105

320


S300

280-
80 85 90 95 100 105

320


S300

280-(d
80 85 90 95 100 105

320


S300

280 -(e)

80 85 90 95 100 105

320
MicroWEX-2 LSP LSP-DSSAT

S300-

280-
80 85 90 95 100 105
DoY 2004 (EST)


Figure 4-10. Comparison of soil temperature estimated by the coupled LSP-DSSAT and
stand-alone LSP model simulation and those observed during 1\icroWEX-2,
between DoY 78 to 105: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f) 100 cm.









CHAPTER 1
INTRODUCTION

Soil moisture in the root zone is an important component of the global water and

energy balance, governing moisture and heat fluxes at the land surface and at the

vadose-saturated zone interface. Typically, soil moisture estimates are obtained using

Soil-Vegetation-Atmosphere Transfer (SVAT) models. SVAT models simulate energy

and moisture transport in soil and vegetation and estimate the fluxes at the land surface

and in the root zone. Some widely-used SVAT models include the Common Land Model

(CLM) [10], the model developed by the National Centers for Environmental Prediction at

Oregon State University, Air Force, and Hydrologic Research Laboratory at the National

Weather Service (NOAH) [46], and the University of Michigan Microwave Geophysics

Group Land Surface Process (LSP) model [38]. However, two main challenges remain in

modeling energy and moisture fluxes using SVAT models.

First, most models often oversimplify the coupling between vegetation growth and

surface fluxes. The interactions between vegetation and the fluxes become increasingly

important as these fluxes affect plant growth and development. Vegetation canopies

impact latent and sensible heat fluxes, precipitation interception, and radiative transfer

at the land-atmosphere interface, affecting soil moisture and temperature profiles in the

vadose zone. These changing interactions during the growing season need to be included

in the SVAT models, in order to provide realistic estimates of the fluxes. Typically, SVAT

models employ observations or empirical functions for vegetation conditions to model

the effects of growing vegetation. For example, CLM uses vegetated grid spaces defined

by patches of pI 10.0 functional types," with parameters for physiological and structural

properties associated with each type, and most of the vegetation parameters are empirical

to meet computational constraints [10]. NOAH simulates soil moisture and temperature

profiles with a sub-daily timestep, and with vegetation properties such as LAI, stomatal

resistance, and roughness length defined by vegetation type classes [46]. Such methods










Sensible Heat Flux


LatenRt Heat Flux


T, 9s


rallyJ r i;lf..X L) E- a


r ...7 rdfX)r(i-) r~dfv







Figure 4-1. Surface resistance network to estimate sensible and latent heat fluxes in the
LSP model.


bare-soil data:

p, = 0.0854e[-maz(0s,-0.0532,0) /n.003i7] + 0.146i50 (4-8)

where Os is the surface volumetric soil moisture (m3 m3)

Longwave Radiation (Rz,c and Rz,s)

The net longwave radiation abosrbed by the c Ilrpi- (Rz,c) and soil (Rz,s) are given by

Kustas and Norman [35]:


Rz,c = (1 nI)Rl,aown + (1 nl)es~sbTs4 2(1 nl)Ec~sbTc4 (4-9)


Rzs=(I) Rl,aown Es~sb? s4 ( 8l) c~sbT)(-0

where asb is the Stefan-Boltzmann constant, Rl,down is the downwelling longwave radiation,

as is the soil emissivity, ec is the canopy emissivity, and T, and T, are the soil and c Ilr1.lii

temperatures in K~elvin. nr is the longwave c lis..pi- transmissivity, the integral over the

hemisphere of direct transmissivity with a as zero.

Sensible Heat Flux~es

Figure 4-1(a) shows the resistance network model used to estimate sensible heat flux

(H) at the surface.The sensible heat fluxes between the soil and air (Hse), soil and canopy










LIST OF TABLES


Table page

3-1 Cultivar coefficient values in the calibrated CERES-Maize model. .. .. .. 29

3-2 Error statistics for crop growth and ET between CERES-Maize estimates and
MicroWEX-2 field observations. ......... ... 32

3-3 model performance statistics for soil moisture and temperature between CERES-Maize
estimates and MicroWEX-2 field observations. ..... .. 37

4-1 Values for soil properties in the LSP model. .... .. .. 49

4-2 Sampling ranges from [24] and calibrated values for parameters in the LSP model. 50

4-3 Comparison of LAI, dry biomass (kg/m2), and ET (mm) for stand-alone DSSAT
and coupled LSP-DSSAT simulations. . .... .. 53

4-4 Comparison of surface fluxes (W/m2), foT Stand-alone LSP and coupled LSP-DSSAT
simulations. ......... ... .. 55

4-5 Comparison of volumetric soil moisture (m3 m3), foT Stand-alone LSP and coupled
LSP-DSSAT simulations. ......... . .. 55

4-6 Comparison of soil temperature (K(), for stand-alone LSP and coupled LSP-DSSAT
simulations. ......... ... .. 55

4-7 Measurement uncertaintities during MicroWEX-2. ... ... .. 56

5-1 Values of the Coefficients in equations 5-6 and 5-7 ... .. .. .. 88

5-2 RMS differences between observed Tg during MicroWEX-5 and those estimated
by the MB model ........... ...... ..... 91

5-3 RMS differences between observed H-pol Tg during MicroWEX-2 and those estimated
by the MB model. ......... . .. 94










A new version of the LSP model was used with a modified radiation flux parameterization

at the land surface. Specifically, the shortwave radiative transfer was altered to a more

physically-based formulation, including both diffuse and direct radiation, and c .Ir-pli

transmissivity described by Camp~bell and Norman [4]. The original version of the LSP

model followed a more empirically-based formulation by V/erseghy et al. [62]. In addition,

the aerodymanic resistances and the surface vapor resistances were changed in the new

version to extend it to tall vegetation and to partially-vegetated terrain [24]. The original

version was developed for homogeneous land cover, such as bare soil or short grass. The

new version of the model also includes adaptive timesteps for computational efficiency and

to allow sudden changes or large fluxes in the sandy soils with high thermal and hydraulic

conductivities. The following section provides a detailed description of the modified LSP

model used in this study. Some fundamental governing equations are also included in the

section for completeness even though they remain unchanged from the original version.

4.2.1 Energy and Moisture Transport at the Land Surface

4.2.1.1 Energy Balance

Combining the radiation and heat flux boundary conditions, the net energy flux into

the canopy (Que,,c), and soil (Qnet,s) (W/m2):


Quet,c = Hse + Rs,c + Rz,c Hea LEtr LEev (4-1)


Quet,s = -Hse + Rs,s + Rz,s Hsa LE, (4-2)

where Hse, Hea, and H,, are the sensible heat fluxes between soil and calrs..pi-, ( .Ir-pli- and

air, and soil and air, respectively; LEtr, LEe,, and LE, are the latent heat fluxes from

transpiration, canopy evaporation, and soil evaporation, respectively; and Rs,c, Rs,s, Rz,c,

Rz,s, are the net solar radiation intercepted by the c Ilr.pi-, intercepted solar radiation by

the soil, net longwave radiation at the canopy, and net longwave at the soil, respectively.

Solar Radiation (Rs,c and Rs,s)









ACKENOWLED GMENTS

This research was supported by the NSF Earth Science Directorate (EAR-0337277)

and the NASA New Investigator Program (NASA-NIP-00050655). I would like to thank

Mr. Orlando Lanni and AMr. Larry Miller for providing engineering support during the

MicroWEXs and patiently tolerating my idiocy; Mr. .Jint Bci- r and his team at PSREIT

for land and crop nianagenient; Dr. Roger De Roo at the University of Michigan for

radionieters and tech support; Mr. K~ai-.Jen Tien, Mr. Tzu-Yun Lin, his. Mi-Young .Jang,

and AMr. Fei Yan for their help in data collection during the MicroWEXs; and to the

University of Florida High-Perforniance Computing Center for providing computational

resources and support that have contributed to the research results reported within this

thesis.


















S0.~1 ~ a




S0.1 =

0 05-
135 140 145 150 155




S0.1


0 05-
135 140 145 150 155




m 0.2~1 ~


0 05-
135 140 145 150 155



mE 0.2 (e)
S0.15

5j 0.1
0 05-
135 140 145 150 155



E 0.2 -(f

0 .15 -- -M co E S S S A

0.05
135 140 145 150 155

Do 2004 (EST


LSP-DSSAT an stn-aln LS moe simulation and ths observedDS
duin MirWX2 ewe oY15t 5:()2cm b m c m





(d)rn 3 irWX2 ewe DY15t 5:(2 cm, (e) 64 cm, and (f 0 cm.


















~oQ









LOO






mo













SO

O









co


~1) 'dwalssau~y6!la lod-H


~1) 'dwalssau~y6!la lod-H









Table 5-3. RMS differences between observed H-pol Tg during MicroWEX-2 and those
estimated by the MB model.
r, model RMSD (K) MAD (K) Bias (K)
Specular soil (DoY < 125) 35.87 28.94 -25.70
Wegmiiller and Matzler (DoY < 125) 32.18 23.78 14.43
Secular soil (DoY > 125) 10.06 8.15 63.58
We miller and Matzler (DoY > 125) 12.43 10.83 9.97


During less than full vegetation cover (before DoY 125), the soil reflectivity strongly

affects the estimation of brightness. This leads to a wide disparity between the specular

and Wegfmiller and Miltzler rough surface model estimates, as can be seen in Figure 5-7.

The lower reflectivity of the rough surface model leads to higher brightness values than

the specular model. Sudden drops in brightness (DoY 98, 100, 104, 107, 109, 112, 114,

119, 122, 123, and 124) due to irrigation or precipitation events are only reached with

the specular model. As vegetation cover increases, the canopy contribution to brightness

increases so the specular and rough surface models' estimates of brightness approach

eachother. Overall, the performance when ve < 1 is poor, as seen by the high RMSDs

Table 5-3. Neither reflectivity model matched sudden drops in brightness, and there is also

an underestimation due to the overestimation of soil moisture by the LSP model (C'h! Ilter

4).

During full vegetation cover (after DoY 125), model estimates of brightness are

dominated by the c Ilnopi- contribution and thus by -r and w. Both surface reflectivity

models give similar results and overestimate brightness, seen in Figure 5-8 and the RMSDs

in Table 5-3. This could be an indication that the w values found for MicroWEX-5 are

not correct for MicroWEX-2. After modeled ear formation on DoY 139 brightness is less

overestimated as w is increased to 0.075. This late in the season, there is almost no effect

from the soil, and as the -r model is biophysically based, the only way to improve model

estimates here would be to calibrate w before and after ear formation.

5.4 Summary

This chapter answers the research question 6 and 7 outlined in ('! .pter 1.










ignore the interaction between surface fluxes and vegetation growth. Second, SVAT model

estimates of fluxes, soil moisture, and soil temperature diverge from observations due to

uncertainty in parameters, forcing, and initial conditions, and due to accumulated errors

from numerical computation.

SVAT models can be coupled with crop growth models to include dynamic interactions

between the vegetation growth and flux estimates. For example, [23] used a sub-daily

biochemical vegetation model with a land surface hydrology model. They modeled

canopy transpiration and its influence on soil moisture and carbon fluxes. [41] linked

daily process-based crop models for summer maize and winter wheat with an hourly

land surface flux model and a three-lbu;r soil moisture model. Such coupling allows for

inclusion of vegetation effects without in situ observations or empirical growth functions.

Periodic in situ observations of vegetation could be incorporated in the coupled models to

reduce the divergence of model prediction from reality.

Remotely-sensed observations sensitive to soil moisture, such as low frequency (< 10

GHz) microwave brightness (TB) [15, 26, 43, 52] could also be incorporated periodically

to improve model flux estimates. To incorporate or assimilate microwave brightness,

the coupled SVAT-crop model has to be linked to a microwave emission model that

estimates microwave brightness using moisture and temperature profiles in soil and

vegetation estimated by the SVAT-Crop model as shown in Figure 1-1. Simple versions of

SVAT models linked with MB models include the Land Surface Process/Radiobrightness

(LSP/R) [30] and Simple Soil-Plant-Atmosphere Transfer Remote Sensing (SiSPAT-RS)

[12] models.

The total Tg of a terrain is dependent on sky Tg, reflected by the soil (TB,sky),

thermal emission from the soil (TB,soil, and thermal emission from the vegetation canopy

(TB,canopy, all three components are shown in Figure 1-2). Since soil microwave emissions

(dependent on soil moisture and temperature profiles) are attenuated by transmission










4-27 Comparison of fluxes estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2: (a) net radiation,
(b) latent heat flux, (c) sensible heat flux, and 2 cm soil heat flux. .. .. .. 78

4-28 Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during MicroWEX-2:
(a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. .. .. .. 79

4-29 Comparison of soil temperature estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2: (a) 2 cm, (b) 4
cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. ... .. .. .. 80

5-1 Observations of total and ear wet biomass during (a) MicroWEX-4 in 2005 and
(b) MicroWEX-5 in 2006. ......... .. .. 83

5-2 Observations oft Il cpi- height during (a) MicroWEX-4 in 2005 and (b) MicroWEX-5
in 2006. ... ......... .............. 84

5-3 Cloud densities measured during (a) MicroWEX-4 in 2005 and (b) MicroWEX-5
in 2006. The symbols and the lines represent the measurements and the best
curve-fits, respectively. ......... . .. 85

5-4 Moisture mixing ratios measured during (a) MicroWEX-4 in 2005 and (b) MicroWEX-5
in 2006. ... ......... .............. 86

5-5 Comparison of -r calculated using the biophysical -r model (with and without
the gaussian term) and that using the Jackson model during (a) MicroWEX-4
in 2005, and (b) MicroWEX-5 and 2006. ...... .. . 89

5-6 Comparison of the observed Tg at H-pol during MW5 those simulated by the
MB model using -r from the biophysical model and from the Jackson model during
late-season MicroWEX-5. ......... .. .. 90

5-7 Comparison of microwave brightness, estimated by the LSP-DSSAT-MB model
with specular surface (a) and Wegmilller and Matzler (b), and C-band microwave
brightness observed during MicroWEX-2, before DoY 125. .. .. .. .. 92

5-8 Comparison of microwave brightness, estimated by the LSP-DSSAT-MB model
with specular surface (a) and Wegmilller and Miltzler (b), and C-band microwave
brightness observed during MicroWEX-2, after DoY 125. .. .. .. 93










The CER ES-1\aize model determines LAI by tracking the total number of leaves and

calculating a leaf area growth rate, so that the rate of increase of LAI is the product of

leaf area growth and current leaf number. Leaf growth is partly determined by the number

of degree d ex-< between successive leaf tip appearances, called the phyllochron interval. In

addition, a leaf senescence rate is calculated based on water stress.

The soil-plant-atmosphere module estimates ET at the land surface using either

the Ritchie-modified Priestley-Taylor (RPT) method [48] or the Penman-FAO (PFAO)

method [14]. The RPT method depends only on solar radiation and temperature, while

the PFAO method accounts for wind speed and relative humidity as well. Both methods

first determine a total potential ET, which is partitioned into potential soil evaporation

and potential plant transpiration. Potential soil evaporation is based on intercepted solar

radiation reaching the soil surface as a function of temperature, wind speed, radiation,

and humidity. Potential plant transpiration depends on the radiation intercepted by the

canopy and temperature, wind speed, and humidity. Actual evaporation and transpiration

are determined by the minimum of potential ET and the amount of available water. For

soil evaporation, surface soil water is the limiting factor, while for transpiration, root water

uptake is the limiting factor.

The soil is divided into nine l~i-;-rs, each with different constitutive properties.

Soil moisture is calculated using the bucket method [39]. When an upper soil 1 .,-cr is

above the drained upper limit, excess flows to the one below, in addition to computing

estimates for capillary rise. Runoff is calculated using the USDA Soil Conservation Service

runoff number method [53]. Infiltration is equal to excess precipitation after runoff. Soil

temperature is computed using a deep soil boundary condition and an air temperature

boundary condition. The air temperature (oC) is calculated from the average of maximum

and minimum daily temperatures. Soil temperature (ST) varies with soil l.,-;- (L) as [29]:



ST (L) = TAVo + (TWP COS (,LX + ZD) +DT)6Z
2.0



























































*MicroWEX-2 LSP -- -LSP-DSSAT


1000~


500o




80



1000
800
600 (b)






-200
809


1000
800-

E600 (c)
400

S200


-200
809


1000
800 -
600 (d


30 100 110 120 130 140 150


30 100 110 120 130 140 150


30 100 110 120 130 140 150


S400

S200


-200
80 90 100 110 120 130 140 150

Figure 4-27. Comparison of fluxes estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during 1\icroWEX-2: (a) net
radiation, (b) latent heat flux, (c) sensible heat flux, and 2 cm soil heat flux.











TH = TH 26-(ko)M~
(5-4)
Ty = THCOS 80.655

where CH is the H-polarized Fr-esnel coefficient, ko is the vacuum wavenumber, 8 is

the look angle, and o- is the surface roughness height, set to 0.0005 m [27]. Soil dielectric

properties are determined using a four component mixing model following Dobson et al

[13].

5.2.4 Model Comparison and Evaluation

Using the p(z), -r is obtained for the MicroWEX-4 and -5 growing seasons. The

estimates for -r are compared with those obtained using the Jackson model [51] as:


-r = b We (5-5)


where, b is an empirical parameter and We is the water content in the calrs..pi- (kg/m2>

The -r from the biophysical model and from the Jackson model are evaluated in the

MB model during the latter part of the MicroWEX-5 season. The MB model simulated

Tg for ten d on~ (DAP 42-52), with DAP 42-47 during vegetative growth and DAP 47-52

during ear formation. The c Ilr.pi- cover was 100I' during the period of simulation. The

MB model was driven with observed canopy and soil temperatures and moisture values.

The MB model was linked to the LSP-DSSAT model, which provides it with soil

temperature and moisture profiles as well as canopy properties used for calculating canopy

transmission and scattering, such as height, vegetation cover, and vegetative and ear

biomass. Using the same initial conditions, inputs, and parameters obtained for the

calibrated LSP-DSSAT model in OsI Ilpter 4, the LSP-DSSAT-MB model simulated H-pol

Tg for the MicroWEX-2 growing season.





































Figure 4-2. Algforithm for the couplingf of the LSP and DSSAT models.


4.3 Coupling of LSP and DSSAT models

Both the LSP and the DSSAT models are forced with micrometeorological conditions

provided in each model's required format. A flowchart of the model coupling is shown

in Figure 4-2. The soil moisture and temperature profiles are initialized in both models.

The LSP model simulates energy and moisture fluxes using an adaptive timestep. At the

last timestep of each d is-, the daily averages of ET, soil moisture and soil temperature

are calculated and passed on to the DSSAT model. The DSSAT uses these values in

calculating growth rates to obtain the crop variables such as biomass, LAI, etc. using a

daily timestep. The estimates of biomass, root-length densities, LAI, height, and width are

provided to the LSP model for flux estimation on the next d is-.

The main challenge in coupling an SVAT model such as the LSP and a crop model

such as the DSSAT arises from the difference in timestep and thickness of soil nodes









Table 4-1. Values for soil properties in the LSP model.
Parameter Description 0-1.7 ni 1.7-2.7 ni
X Pore-size index 0.27 0.05
,, ~Air entry pressure (ni H2()) 0.076 0.019
K.,a Saturated hydraulic conductivity (nt/s) 2.06 x10-4 8.9:3x 10-5
Or Volumetric wilting point moisture (nt"/nt) 0.0051 0.0040
8,,, H>1unletric saturation moisture (ni /ni) 0.34 0.41
.so H>1unletric sand fraction (ni /ni) 0.894 0.512
.si H>1unletric silt fraction (ni /ni) 0.034 0.08:3
,. Volumetric clay fraction (ni /n ) 0.071 0.405
,, Volumetric organic fraction (ni /n ) 0.000 0.000
Porosity 0.34 0.41


raingauges matched those observed independently at the same field site using collection

cans [16].

Initial conditions were not known during MicroWEX-2 because the sensor installation

was completed 7 d on~ after planting. The first values observed by the soil moisture and

temperature sensors were used as the initial moisture and temperature values for the

simulations.

Soil physical properties were based on texture and retention curve measurements

taken from soil samples in the field at different depths, and are listed in Table 4-1.

4.4.2 Calibration

The DSSAT and the LSP models were calibrated separately for the entire growing

season. In the DSSAT model, six corn cultivar coefficients governing the growth and

development, as described in Cl. .pter :3, were calibrated using Simulated Annealing to

nxinintize the root mean square difference (R MSD) between modeled and observed LAI

and biomass during MicroWEX-2.

In the LSP model, 12 parameters were calibrated using repeated Latin Hypercube

Sampling of the parameter space [40]. Four of these parameters were related to radiation

balance: leaf reflectance, o-, leaf angle distribution, .r, soil entissivity, es, and calr gol

entissivity, e,.. The remaining eight parameters were related to sensible and latent

heat fluxes: canopy base assimilation rate, Fb, photosynthetic efficiency, Frix,,,,, hare









MODELING LAND SURFACE FLUXES AND MICROWAVE SIGNATURES OF
GROWING VEGETATION


















By

JOAQUIN J. CASANOVA


A THESIS PRESENTED TO THE GRADUATE SCHOOL
OF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT
OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF ENGINEERING

UNIVERSITY OF FLORIDA

2007














E 600- a

S400-.


S200


-200
105 110 115 120 125



300-
(b)
200-




~ -100
-200
-300-
105 110 115 120 125
DoY 2004 (EST)

Figure 4-12. Comparison of latent heat flux, between DoY 105 to 125, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


The RMSD of ~40 W/m2 COTTOSpond to those expected from the Pareto front in Figure

4-3.

Similarly low RMSDs and biases are found in sensible heat flux, soil heat flux, and

soil temperature. Sensible heat flux is overestimated, but matches more closely with

observations during this stage than during the bare soil stage (Figure 4-13), with RMSDs

of ~30 W/m2 and biases of ~12 W/m2. Soil heat flux remains overestimated during

the dwi and underestimated at night, similar to the previous stage (Figures 4-14(a) and

(b)). Overall, the 2 cm soil heat flux is underestimated with RMSD of ~39 W/m2 and

biases of ~-6 W/m2 and This is reflected in the soil temperature (Figure 4-16) as a lower

overestimation (RMSD < 1.67 K( and bias < 0.67 K() than in the previous stage for the












is(z) profiles can be integrated over the height of the canopy to obtain -r. The isothermal

assumption was appropriate for a short sweet corn c lis..pi- of 1.5m. The absolute difference

between the temperatures at the top and at the bottom of the c lis..pi- was < 4 K( during

the simulation period.

5.2.3 Microwave Brightness Model

The microwave brightness (\!11) model is a widely-used -r-co model [59], in which the

total brightness temperature of a terrain (TB) is a sum of three contributions: Tes,p (from

the soil), TBc,p (frOm the c lis..pi-), and TBsky,p (frOm the sky).


DAP 63
DAP 68
DAP 77-
-A 8
DAP 84


32 04 06 08


I~ I


O DAP 32
o DAP 53
SDAP 67


32 04 06 08 1 12 14 16 18 2
Canopy Helght (m)


Figure 5-3. Cloud densities measured during (a) MicroWEX-4 in 2005 and (b)
MicroWEX-5 in 2006. The symbols and the lines represent the measurements
and the best curve-fits, respectively.









4-16 Comparison of soil temperature estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2, between DoY
105 to 125: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. 67

4-17 Comparison of net radiation, between DoY 125 to 135, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 68

4-18 Comparison of latent heat flux, between DoY 125 to 135, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 69

4-19 Comparison of sensible heat flux, between DoY 125 to 135, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those observed
during MicroWEX-2: (a) values and (b) residuals .... .. 70

4-20 Comparison of soil heat flux, between DoY 125 to 135, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ...... .. 71

4-21 Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during MicroWEX-2,
between DoY 125 to 135: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f) 100 cm. ......... .. . 72

4-22 Comparison of soil temperature estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2, between DoY
125 to 135: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. 73

4-23 Comparison of net radiation, between DoY 135 to 154, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ...... .. 74

4-24 Comparison of soil heat flux, between DoY 135 to 154, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ...... .. 75

4-25 Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during MicroWEX-2,
between DoY 135 to 154: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f) 100 cm. ......... .. . 76

4-26 Comparison of soil temperature estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2, between DoY
135 to 154: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. 77





















10 20 30 40 50 60 70 80 90










(b)

10 20 30 40 50 60 70 80 90
Days After Planting



Figure 5-4. Moisture mixing ratios measured during (a) MicroWEX-4 in 2005 and (b)
MicroWEX-5 in 2006.




Tas,p = (1 r,3)Teggexp(-7/p)l

TBcs p Tc 1 eXp(-r/p)](1 w)
(5-3)
x [1 + rpexp(--7/p)]



where p is polarization, r, is the reflectivity of the rough soil surface, Te;; is the

effective radiating temperature of the soil calculated using the first order approximation

from [18] (K(), p = cos(0) where 8 is the look angle (500 for MicroWEX-5), To is the

physical temperature of the isothermal canopy (K(), measured during the experiments, w is

the single scattering albedo, and Tsky IS the sky brightness (assumed 5 K( at C-band). In

this model, r, is based upon the semi-empirical model of Wegmilller and Miltzler [63]:



































































MicroWEX-2 LSP LSP-DSSAT
-f
--

-- I


01




S012

0 0.5-






0 05-
125 126 127 128 129 130 131 132 133 134 135



E 0.2
m(d
S0.15


0 05-
125 126 127 128 129 130 131 132 133 134 135



mE 0.2
E 0.15


0 05-
125 126 127 128 129 130 131 132 133 134 135


E 0.2
E 0.15
0.1


0.05 tI I III lll
125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)


Figure 4-21. Comparison of volumetric soil moisture estimated by the coupled
LSP-DSSAT and stand-alone LSP model simulation and those observed

during 1\icroWEX-2, between DoY 125 to 135: (a) 2 cm, (b) 4 cm, (c) 8 cm,

(d) 32 cm, (e) 64 cm, and (f) 100 cm.


















ca





a

LO ~O








oo
1-1


oa
08 O


LO dCO
bt


crb



o







OO
O


o ad


000000
a)cgbhloa)
) hi hi hi hi hi
()1) .dwalssau~y6!la lod-H


~1) .dwalssau~y6!la lod-H










[37] Lin, Tzu-yun, J. Judge, K(. Tien, J. C. andM.Y. Jang, O. Lanni, and L. Miller
(2004), Field observations during the third microwave water and energy balance
experiment (11.croWEX-3): From july 16-december 21, 2004, Tech. Rep. Circular No
1488, Center for Remote Sensing, University of Florida, Available at UF/IFAS EDIS
website at http: //edis.ifas.ufl .edu/AE361.

[38] Liou, Y., J. Galantowicz, and A. England (1998), A land surface
process/radiobrightness model with coupled heat and moisture transport for freezing
soils, IEEE Trans. Geosci. Remote Sennsong, 86(2), 669-677.

[39] Manabe, S. (1969), Clain! II1. and the ocean circulation. 1. The atmosphere circulation
and the hydrology of the earth's surface, M~onth. Weat. Rev., 97(11), 739-774.

[40] McE~ay, M. D., R. J. Beckman, and W. J. Conover (2000), A comparison of three
methods for selecting values of input variables in the analysis of output from a
computer code, Technometrics, 4 (1), 55-61.

[41] Mo, X., S. Liu, Z. Lin, Y. Xu, Y. Xiang, and T. McVicar (2005), Prediction of crop
yield, water consumption and water use efficiency with a svat-crop growth model
using remotely sensed data from the North Chat.~ I plain, E, J.. y..: al M~odelling, 188,
301-322.

[42] Niu, G.-Y., and Z.-L. Yang (2004), Effects of vegetation c Ilr~pi-i processes on snow
surface energy and mass balances, J. Ge -cl reI,, Res., 109, D23,111, doi:10.1029/
2004JD00 L.

[43] Njoku, E.G., and J. K~ong (1977), Theory for passive microwave remote sensing of
near-surface soil moisture, J. Gc *'Ili t; Res., E (20), 3108-18.

[44] Nyusten, J. A., J. R. Front, P. G. Black, and J. C. Wilkerson (1996), A comparison of
automatic rain gauges, Journal of Atmospheric and Oceanic T ..,: ~In..J.i;, 18(1), 62-73.

[45] Omega (2006), Thermistor elements and compatible
instrumentation, Tech. rep., Available at
http: //www.omega. com/Temperature/pdf/44000_THERMIS_ELEMENTS.pdf.

[46] Pan, H.-L., and L. Mahrt (1987), Interaction between soil hydrology and boundary
1.. rde elo me tBo;it1.~t Lt;.? .1..rJ.,i; 8, 185-202.

[47] Philip, J. R., and D. A. de Vries (1957), Moisture movement in porous material under
temperature--- gradients,, Transactions- of America__n G*'lt-.r Union, 88, 222-232.

[48] Ritchie, J. (1972), Model for predicting evaporation from a row crop with incomplete
cover, Water Resources Res., 8, 1204-1213.

[49] Rossi, C., and J. R. Nimmo (1994), Modeling of soil water retention from saturation
to oven dryness, Water Resources Research, SO, 701-708.









Soil properties such as hydraulic conductivity and texture were taken as the default

values for the soil type that most closely corresponds with our field site (11illl!iopper fine

sand) and that is included in the DSSAT soil properties file. The drained lower limit

of the top nine soil 1.i;< rs was set to the minimum soil moisture (0.05) observed during

MicroWEX-2. The initial soil moisture for all the 1 ... r~s was set equal to 0.2. The model

calibration was found to be insensitive to the choice of initial moisture conditions because

an irrigation event that occurred at planting reset the soil moisture profile of sandy soil.

3.3.2 Inputs

Most of the inputs for the model calibration were obtained from the MicroWEX-2

dataset. These included daily incoming solar radiation, precipitation, irrigation,

fertigation, and wind speed. Maximum and minimum daily temperature and relative

humidity were obtained from the micrometeorological dataset collected for the Agricultural

Field-Scale Irrigation Requirement Simulation (AFSIRS) study at a nearby site at the

PSREU [16].

3.3.3 Methodology

To calibrate the CERES-Maize model, a broad grid search was emplo ..1l followed by

simulated annealingf in the area of the global minimum using the six cultivar coefficients.

Each coefficient was incrementally changed, so that a grid of possible combinations of

values was tested to minimize the differences between model estimates and observations

from biomass and LAI, the two most important canopy parameters required by the MB

model. The LAI observation on DoY 1:35 was excluded for calibration, due to its high

standard deviation (Figure :$-la). The objective function (R) was computed as the sum of

square residuals, normalized by variance [54]:


SSSN U +SSRL.4I 2
B L 4I
where SSRB is the sum of square residuals from total biomass, SSRL,4I is the sum

of square residuals from LAI, and v2R and ach, are the variances of biomass and LAI































































- MicroWEX-2 LSP - LSP-DSSAT


S300
E

280


320


S300
E

280


320


S300


80 90 100 110 120 130 140 150


80 90 100 110 120 130 140 150








80 90 100 110 120 130 140 150






(d)

80 90 100 110 120 130 140 150


S300


80 90 100 110 120 130 140 150


JLU ~


300


280


130 140 150


80 90 100 110 120
DoY 2004 (EST)


Figure 4-29. Comparison of soil temperature estimated by the coupled LSP-DSSAT and
stand-alone LSP model simulation and those observed during 1\icroWEX-2:
(a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm.









Table 3-1. Cultivar coefficient values in the calibrated CERES-Maize model.
Cultivar Coefficient Value
P1 157.20
P2 1.000
P5 811.20
G1 853.00
G3 10.4
PHINT 40.33


observations, respectively. The optimum combination of parameter values found by the

grid search was then used as the initial guess in a simulated annealingf optimization

algorithm [2]. The root mean square difference (RMSD), relative root mean square

difference (RRMSD), and Willmott d-index [66] were calculated as for LAI and the

biomass of each component, leaves, stems, and grain:


E(P O,)2
RM~SD = ( )1/2(33


RM~SD
RRM~SD = (3-4)



E(P, O,)2
d = 1 (3-5)
E(|P, P| +i |Oi O|)2
where n is the number of observations, Pi and Oi are the predicted and observed

values, and P and O are the predicted and observed means. Table 3-1 shows the values of

the six cultivar coefficients that minimized R in Equation 3-2.

3.4 Results and Discussion

3.4.1 Crop Growth and Development

To evaluate the CERES-Maize model for crop growth and development, model

estimates are compared of emergence and silking dates, biomass, and LAI to the

observations during MicroWEX-2.









Table 3-2. Error statistics for crop growth and ET between CERES-Maize estimates and
MicroWEX-2 field observations.
Parameter RMSD RRMSD Willmott d
Total biomass (.\!- /ha) 0.91 0.23 0.99
Stem biomass (.\!g/ha) 0.97 0.52 0.90
Leaf biomass (j\!- /ha) 0.47 0.44 0.93
Grain biomass (.\!g/ha) 0.49 1.17 0.96;
LAI 0.22 0.13 0.99
Latent heat flux (W/m2) 42.07 0.39 0.87


3.4.2 Evapotranspiration

To understand model estimates of energy and moisture fluxes at the land surface, the

modeled daily latent heat flux is compared with the observations during MicroWEX-2.

Four comparisons were conducted [50] using two methods (RPT and PFAO) to estimate

ET and two values (0.85 and 0.5) for the c Ilrpi- light extinction coefficient (K(CAN).

Figure 3-2 shows a comparison of the latent heat flux estimates using the four methods.

Even though the RMSD values were low (~40 W/m2), the temporal distribution of

latent heat fluxes was not estimated realistically during the growing season (Figure

3-2a). The latent heat fluxes were underestimated in the early season and overestimated

(~100 W/m2) during late season. The early season underestimation indicates low

evaporation rates from the modeled soil, and the late season overestimation indicates

higher transpiration rates in the modeled vegetation. The flux estimates were not

as sensitive to K(CAN values as the previous studies had found under water-stressed

conditions [50]. In terms of cumulative ET, individual under- or overestimations by the

model effectively cancel each other, so that the fit for cumulative ET is better than for

daily values (Figure 3-2b).










Question 3:"What values of the twelve calibrated parameters give the

best LSP model performance for both latent heat flux and near surface soil

moisture for the MicroWEX-2 growing season?"

The calibrated values of the twelve parameters are given in Table 4-2.

Question 4:"How do the model estimates of soil moisture, temperature, and

surface fluxes compare with MicroWEX-2 observations?"

The RMSD for VSM at 2 cm is ~ 0.04 m3 m3. For both the LSP and LSP-DSSAT

model simulations, the VSMs at all 1 e. ris exhibit positive bias that increases during the

season. The seasonal RMSDs for temperature decrease with depth with a maximum of

2.43 K(.

The RMSDs between the modeled and observed net radiation were ~ 24 W/m2. LE

estimates had an RMSD of ~ 48 W/m2, the sensible heat flux estimates had an RMSD of

~ 36 W/m2, and the 2 cm soil heat flux estimates had RMSD of ~ 47 W/m2

Question 5:"What is the impact of coupling on both LSP and DSSAT model

estimates of LAI, biomass, soil moisture, temperature, and surface fluxes"

The estimates from the DSSAT and LSP-DSSAT differed by <0.2 for LAI and <0.6

kg/m2 for dry biomass, with the coupled LSP-DSSAT model estimating higher values than
the stand-alone DSSAT. The differences between the LSP and LSP-DSSAT estimates of

soil moisture, soil temperature, and surface fluxes are all small.












*MicroWEX-2 LSP -- -LSP-DSSAT


S200 II -5 I fl ~ t t 1 ,



-200
80 85 90 95 100 105



300-
(b)
200-

100

1~ 1 00 *
-200
-300-
80 85 90 95 100 105
DoY 2004 (EST)

Figure 4-7. Comparison of sensible heat flux, between DoY 78 to 105, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


resistance using Equation 4-31. In both the coupled and the standalone models, LE is

overestimated with RMSDs of ~54 W/m2 and biases of ~18 W/m2. These RMSDs are

higher than the sensor uncertainty of 17-36 W/m2 (Table 4-7) but are comparable with

those expected from Figure 4-3 using the Pareto front from the early season (see Section

4.5.1.2).

Both the coupled and stand-alone models estimate similar sensible heat fluxes, with

RMSDs of ~40 W/m2 and biases of ~16 W/m2 (Figure 4-7). These RMSDs are lower

than those obtained for LE. For the d we~ when LE is positively biased (e.g. DoY 97, 98,

101, 102, and 103), the sensible heat flux is biased negatively, and vice versa. The overall

RMSD for sensible heat fluxes could be due to slightly lower aerodynamic resistance

and/or due to overestimation of soil temperature in both the models (Figure 4-10). The


1000

800-

E 600-





























































- MicroWEX-2 LSP LSP-DSSAT


~____________ __ __ __ _


-i

-s



5

-s


)5 110 115 120 1:


S300
E

280
10

320


S300
E

280
10

320


S300

280
10


110 115 120


110 115 120


125


320


S300 ~ ,

280-(d
105 110 115 120 1:


300


280
105


300-


280-
105


115
DoY 2004 (EST)


Figure 4-16. Comparison of soil temperature estimated by the coupled LSP-DSSAT and
stand-alone LSP model simulation and those observed during 1\icroWEX-2,
between DoY 105 to 125: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f ) 100 cm.










Table 4-2. Sampling ranges from [24] and calibrated values for parameters in the LSP
model.
Parameter Description |Sampling Range Calibrated value


zob







is

Fb
photo
soil
soilb


Bare soil roughness length (m)
Leaf angle distribution parameter
Leaf reflectance
Calrse-pi- emissivity
Soil emissivity
Calrse-pi- drag coefficient
Canopy wind intensity factor
Leaf width (m)
Base assimilation rate (kg CO2 m2S)
Photosynthetic efficiency (kg CO2/J)
Slope parameter for r, (m2S/kg H20)
Intercept parameter for r, (m2S/kg H20)


10-4 10-
10-2 2.0
10-2 0.5
0.95 0.995
0.95 0.995
10-5 1.0
10-3 102
10-3 10-1
-10-s -10-10
10-' 10-s
0.0 5x103
0.0 --6 x 102


0.004
0.819
0.474
0.973
0.953
0.328
67.90
0.0531
-8.20 x 10-9
8.97x 10-7
3700.0
-531.0


soil aerodynamic roughness, zob, leaf Width, i,, wind intensity factor, i,, ( Ilrpi- drag

coefficient, cd, and soil evaporation resistance parameters, soil, and soilb. The calibration

of these parameters was conducted to minimize RMSDs between the modeled and

observed volumetric soil moisture (VSM) at 2 cm and latent heat flux (LE) for the

overall growing season. These two objectives were chosen because VSM is one of the most

important factors governing the moisture and energy fluxes, and in the calibration VSM

and LE were found to be competing objectives.

During the calibration, five thousand points were sampled in the form of twenty

250-point Latin Hypercube Samples within the ranges from Goudrican [24], specified in

Table 4-2, using the University of Florida's High-Performance Computing Center. These

sampled points were ordered by Pareto ranking and the set of points with the lowest

Pareto rank were considered as the optimal parameter set [25].










[50] Sau, F., K(. Boote, W. M. Bostick, J. Jones, and M. I. Minguez (2004), Testing and
improving evapotranspiration and soil water balance of the DSSAT crop models,
Agror:. ,;, J., 96(5), 1243-1257.

[51] Sch~n~iic~ T., and T. Jackson (1992), A dielectric model of the vegetation effects
on the microwave emission from soils, IEEE Trans. Geosci. Remote Sensing, 80 (4),
757-760.

[52] Sch~n~iic~ T., and P. O'Neill (1986), Passive microwave soil moisture research, IEEE
Trans. Geosci. Remote Sensing, GE-24 (1) 12-22.

[53] Soil Conservation Service (1972), National engineering handbook: Section 4:
Hydrology, Tech. rep., USDA.

[54] Thornley, J., and I. Johnson (1990), Plant and Crop M~odeling., Oxford: Oxford
University Press.

[55] Tien, K~ai-Jen, J. Judge, O. Lanni, and L. Miller (2003), Field observations during
the second microwave water and energy balance experiment ( ~lo roWEX-1):
From July 17 December 16, 2003, Tech. Rep. Circular No 1470, Center for
Remote Sensing, University of Florida, Available at UF/IFAS EDIS website at
http://edis.ifas.ufl.edu/AE280.

[56] Twine, T., W. P. K~ustas, J. M. Norman, D. R. Cook, P. R. Houser, T. P. M~ i.' rs,
J. H. Prueger, P. J. Starks, and M. L. Wesely (2000), Correcting eddy-covariance flux
underestimates over a grassland, Age..: ;,lletral and Forest M. /..,al..,;;; 108, 279-300.

[57] Ulaby, F., and M. El-R wes;- (1987), Microwave dielectric spectrum of
vegetation-Partll: Dual- dispersion model, IEEE Trans. Geosci. Remote Sensing,
GE-25, 550-557.

[58] Ulaby, F., and E. Wilson (1985), Microwave attenuation properties of vegetation
canopies, IEEE Trans. Geosci. Remote Sensing, GE-2S, 746-753.

[59] Ulaby, F., R. Moore, and A. Fung (1981), M~icrowave Remote Sensing Active and
Passive, V/ol I, Artech House Inc.: Norwood, MA.

[60] Ulaby, F., R. Moore, and A. Fung (1986), M~icrowave Remote Sensing Active and
Passive, V/ol III, Artech House Inc.: Nn o .--n o~d, MA.

[61] Van de Griend, A. A., and J. Wigneron (2004), The b-factor as a function of
frequency and calre~li type at H-polarization, IEEE Trans. Geosci. Remote Sens-
ing, 4 (4), 786-794.

[62] Verseghy, D. L., N. A. McFarlane, and M. Lazare (1993), Class a Canadian land
surface scheme for GC'jL- II. Vegetation model and coupled runs, Int. J. of C'I::,,.;i. .1-
ogy, 18, 347-370.










estimates and observations of the two objectives, VSM at 2 cm and LE. Even though the

calibrated parameters were obtained for the whole growing season, the growing season was

divided into four periods to understand the differences in Pareto fronts during different

growth stages (Figure 4-:3). These four stages include: almost bare soil (DoY 78-105),

intermediate vegetation cover (DoY 105-125), full vegetation cover (DoY 125-1:35),

and reproductive stage (DoY 1:35-154). A Pareto front could not he generated for the

reproductive stage due to lack of LE observations during this stage. In general, the

fronts show that the model performs best during the intermediate cover stage, with the

front closest to the origin, and worst during the almost bare soil stage, with the front

farthest front the origin. The worst performance during the bare soil stage is primarily

due to fewer observations (<2000) from MicroWEX-2 during this stage compared to the

>4000 observations during vegetated stages, resulting in calibrated parameters biased

towards nxinintizing differences during the vegetated stages. For the stand-alone LSP and

LSP-DSSAT simulations in this study, the Pareto front for the overall season in Figure

4-:3 was used to choose the 12 parameter values corresponding to an R MSD in VSM at

2 cm of 0.04 ni /n noted by an asterisk in the Figure. This choice was based upon the

sensitivity of SVAT models to VSM for hydronieteorological applications [20, :34, :36]. With

the RMSD in VSM of 0.04 ni^/nit there is an expected RMSD in latent heat flux of about

45 W/ni2 for the overall season and about 55, 40, and 50 W/ni2 for the first three stages,

respectively (see Figure 4-3). Table 4-2 lists the calibrated parameter values used in the

LSP and LSP-DSSAT model simulations.

4.5.2 Model Simulation

4.5.2.1 DSSAT

The DSSAT model provided realistic estimates of growth and development of sweet

corn. Both the stand-alone DSSAT and LSP-DSSAT models estimated the emergence date

on DoY 90, compared to DoY 86 observed during MicroWEX-2. Modeled anthesis d #-,










TABLE OF CONTENTS


page


ACKNOWLEDGMENTS

LIST OF TABLES.

LIST OF FIGURES

ABSTRACT

CHAPTER

1 INTRODUCTION

1.1 Thesis Objectives.
1.2 Thesis Format .......... .......

2 MICROWAVE WATER AND ENERGY BALANCE EXPERIMENTS

3 CALIBRATION OF A CROP GROWTH MODEL FOR SWEET CORN ...

3.1 Introduction.
3.2 CERES-Maize Model .......... .....
3.3 Model Calibration .......... ......
3.3.1 Initialization.
3.3.2 Inputs
3.3.3 Methodology. .......... .....
3.4 Results and Discussion.
3.4.1 Crop Growth and Development
3.4.2 Evapotranspiration
3.4.3 Soil Moisture and Temperature
3.5 Summary


4 CALIBRATION OF AN SVAT MODEL AND
MODEL FOR SWEET CORN

4.1 Introduction .........
4.2 LSP Model
4.2.1 Energy and Moisture Transport at
4.2.1.1 Energy Balance .. .
4.2.1.2 Moisture Balance ...
4.2.2 Soil Processes
4.3 Coupling of LSP and DSSAT models .
4.4 Methodology
4.4.1 Inputs and Initial Conditions ..
4.4.2 Calibration. .......
4.5 Results and Discussion.


COUPLING WITH A




the Land Surface ..


CROPD














Ln
O

cb

cc~
O
m
r



cb
m

O


k
m
e
o
or


w E
o
~
oo

d
HC~



'5 a
r
" cb

Cb E
E o
,o
mC~



r
cb


E "
~Ln

d
ocY3
m

.~
cb


uiO
o
Wc~
~4'Ln

o


~E
O O
~Ln

"Ln
'fl
cb ~`

E
o E
O o


C1~3


5D


- )


-j


0

L









N E



o v


o


a










N E

o d
co

a

a

o


comm


commec


3
y





c


?~ F



a so


a~ o


a




a



a


IS
OlDOIDOID
00000 or
MooNNN c


IS I
OlDOIDOI
000000oi
MoooNNN


OLDLDOLDOD
COOWNNN


oncomeo
comme











*MicroWEX-2 LSP -- -LSP-DSSAT


1000
800-
E 600- a

S400 -


S200-


-200
105 110 115 120 125



300-
(b)
200-

100

1~ -100
-200
-300-
105 110 115 120 125
DoY 2004 (EST)

Figure 4-13. Comparison of sensible heat flux, between DoY 105 to 125, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


stand-alone LSP, and an underestimation (RMSD < 1.47 K( and a negative bias > -0.91

K() in the case of the LSP-DSSAT model.

Late Season V/egetative Stage

This period included the next ten d we~ Of the growing season, when the corn was

in the vegetative growth stage and at full vegetation cover (DoY 125-135). The calrpli

height was 73-162 cm, LAI was 1.82-2.49, and vegetation cover was 1.00.

In the previous stage, as vegetation cover increased, residuals for net radiation

decreased. Because of full vegetation cover during this stage, net radiation (Figure 4-17)

matches very closely with observations, with RMSDs of ~16 W/m2 and biases of ~8

W/m2, leSS than the estimated sensor uncertainty (Table 4-7). LE is overestimated with

RMSD of ~49 W/m2 and bias of ~16 W/m2 (Figure 4-18). The RMSD of ~49 W/m2










wr,max = 0.2LAI


(4-27)


where Wr,max is the maximum possible interception, and W, is the intercepted moisture by

the canopy [62]. rby is the leaf boundary 1.v. -r moisture resistance. rl, and r, are surface

vapor transport resistances for the leaves and soil, respectively, where 1,, is leaf width. The

leaf resistance is based on calrs..pi- assimilation [24]:


Tby = 0.93Tbh (4-28)

a Coo
ri = 783Tbh (4-29)
1.66F,

F, = (1 eRS,ctphotolFm)(Fm Fd) + Fd (4-30)

where ACoo, is the concentration difference of CO2 between the leaf and air, in kg/m3,

photo is the photosynthetic efficiency, F, is the net assimilation (kg CO2 m2S), Fd is the

base assimilation rate, determined by a Qio relationship from parameter Fb, and Fm, the

maximum assimilation rate, is estimated as 10Fd.

Soil surface resistance is a linear function of surface moisture deficit [3],


r, = so-9 AD + soilb 41


where moisture deficit (aO) is the difference between saturated moisture content and

actual moisture content.

4.2.1.2 Moisture Balance

The net infiltration of moisture at the soil surface (Iet~,s) is given by:


Inet~s = PfB + D R E, (4-32)


where P is the precipitation, D is the canopy drainage from the c lis..pi- to the soil, R is

the runoff, and E, is the soil evaporation. D given by W, Wr,max. The rate of change in

moisture intercepted by the c lis..pi- is given by









Table 4-3. Comparison of LAI, dry biomass (kg/m2), and ET (mm) for stand-alone
DSSAT and coupled LSP-DSSAT simulations.
Stand-Alone DSSAT Coupled LSP-DSSAT
RMSD MAD Bias RMSD MAD Bias
LAI (-) 0.38 0.26; 0.06; 0.43 0.39 0.29
Total Biomass (kg/m2) 0.90 0.63 -0.59 0.52 0.40 0.05
ET (mm) 1.63 1.36 0.31 1.64 1.25 0.62


when '7 "' of the corn has silked, was DoY 139, while '7 "' silkingf was observed on DoY

135.

Figure 4-4 and Table 4-3 show the comparison of estimates of LAI and dry biomass

by the stand-alone DSSAT model, by the LSP-DSSAT model, and those observed

during MicroWEX-2. Estimates from both model simulations compared well with the

observations with RMSDs of <0.5 for LAI and <1.0 kg/m2 for dry biomass.

The estimates from the two models differed by <0.2 for LAI and <0.6 kg/m2 foT

dry biomass, with the coupled LSP-DSSAT model estimating higher values than the

stand-alone DSSAT. These relatively small differences could be due to higher daily

averages of soil moisture in the LSP-DSSAT than those in the stand-alone DSSAT's

bucket model, by > 0.02 m3 m3 (Figure 4-4(c)). The higher soil moisture values would

permit increased growth resulting in higher LAI and dry biomass in the coupled model.

The high moisture estimates also result in higher daily ET in the coupled model compared

to the DSSAT (Figure 4-4(d)). The LSP-DSSAT predicts <0.5 mm/day higher ET than

DSSAT alone, with the RMSD between the daily estimates of ET by the LSP-DSSAT and

observations of 1.69 mm.

4.5.2.2 LSP-DSSAT Model

The performance of the coupled LSP-DSSAT model was evaluated by comparing

its estimates of surface fluxes, soil moisture, and temperature profiles to those observed

during MicroWEX-2, and to those estimated by the stand-alone simulation of the LSP

model. These comparisons are discussed for the four growth stages and during the entire

growing season separately to provide detailed insight into modeled fluxes during different













Ec 800
600-
S400 I
rr200


-200
105 110 115 120 125



300-

E^ 200 -(b)



B 100

S-200
-300-
105 110 115 120 125
DoY 2004 (EST)

Figure 4-11. Comparison of net radiation, between DoY 105 to 125, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


0.2-1.82, and fractional vegetation cover was 0.22-1.00. Overall, the model performance

is better during this growth stage compared to the previous stage, as expected from the

Pareto fronts (Figure 4-3 and Section 4.5.1.2).

As the vegetation cover increased during this period, the residuals in net radiation

decrease significantly, indicating the decreasing influence of soil albedo on radiation

balance. The dwitime residuals decrease from ~80 W/m2 before DoY 115 to <30 W/m2

after DoY 115 (Figure 4-11). Due to the improved net radiation estimates (RMSD ~27

W/m2), and the decreasing influence of soil surface resistance, RMSDs in LE are lower

during this stage than during the bare soil stage (compare Figures 4-6 and 4-12) even

though VSM remains overestimated by similar amounts (compare Figures 4-9 and 4-15).










source of uncertainty in the model is the precipitation input, model estimates of soil

moisture are likely to improve from a data assimilation method which takes into account

the uncertainty in inputs and parameters, such as K~alman filtering.

Before the LSP-DSSAT-MB model can he used in a data assimilation scheme, the

rough soil reflectivity model in the MB model needs to be improved. As is shown in

Section 5.3.3, from the high brightness R MSDs during the first half of the season, either

the specular (Fresnel) reflectivity or the rough surface reflectivity alone are insufficient

to match the sudden drops in brightness associated with precipitation events. This is a

challenge because of the extremely limited bare soil brightness data during MicroWEXs

2, 4, and 5. To fill the dearth of hare soil data, hare soil tests were conducted during

May and June 2007 in which extensive soil roughness measurements were taken along

with brightness measurements, before, during, and after irrigation events. This data

would be useful in the future for the development of a more rigorous rough surface

reflectivity model. A moisture-dependent roughness model could capture the sudden drops

in brightness associated with precipitation events but needs to be refined.

In addition, the LSP-DSSAT-MB model overestimated brightness in the later

half of the MicroWEX-2 season, indicating that the w values need to be higher for the

MicroWEX-2, and that they should be calibrated. The optimal w values for MicroWEX-2

and MicroWEX-5 are apparently different, indicating that there is some difference between

the canopies of the two seasons that would produce a difference in w. Future research

could find some physical relationship between canopy characteristics such as leaf or stem

biomass and w, similar to the physical relationship found for -r.














k
rCc~
c~ O
cb
m
O
m
O

~3t~
k
01 ~
m
Xe
W o

Or
k
oa
a
A cb
bD"

k
rr
i
-d ~
m m
m
CbH



"Ca
cc~


O "


k
~~cb
m c~
ecc~rb
oo~
~,~cb
~oE
C~ m ~
~~ C~
akm
~cb~'
cb e~cc~
mEO
c~oO

aria
~ V k
c~
m
W
cb
.N E ~
cb O,
H '~ a
C~ e,
via,
W
~4`kCb
W
"01
O "
OX
~aW
c, cb ~
O O
k
O
ag,
fl E
~ bD
a t;.fi
E~"
OCC~~
O
C~H
cb~
v a cl]


Cr3

k

5D


- \I --" I


\*

oo







x o
\*


*C
csd


a)cDb

(ey/6yy) ssewo!g ~a


hi O


x~-o














E 600 (a)

S400-
S200-


-200
125 126 127 128 129 130 131 132 133 134 135



300-
(b)
200-





i -300 -~'


125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)

Figure 4-20. Comparison of soil heat flux, between DoY 125 to 135, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


between the LSP-DSSAT and observed net radiation are ~ 24 W/m2. These differences

are close to the sensor uncertainty of 22 W/m2 in Table 4-7. The biases are ~ 17 W/m2

indicate an overestimation. LE RMSDs of ~48 W/m2 arT What can be expected from

the Pareto front in Figure 4-3. Sudden increases in LE on DoY 93, 109, 119, and 127,

as shown in Figure 4-27(b), are due to high evaporation after rainfall or irrigation. The

RMSDs of ~ 36 W/m2 foT SenSible heat flux (Figure 4-27(c)) are lower than those for LE.

The model overestimates the diurnal amplitude for 2 cm soil heat flux (Figure 4-27(d)),

which has LSP-DSSAT RMSDs of ~ 47 W/m2, due to dwitime overestimation of net

radiation and nighttime overestimation of latent and sensible heat fluxes.

The RMSD for VSM at 2 cm (Figure 4-28 and Table 4-5) is similar to our choice

of 0.04 m3 m3 on the overall season Pareto front (Figure 4-3). For both the LSP and










4.5.1 Calibration ...
4.5.1.1 DSSAT
4.5.1.2 LSP
4.5.2 Model Simulation.
4.5.2.1 DSSAT
4.5.2.2 LSP-DSSAT
4.6 Conclusion. .. ...


Model.


5 CANOPY MICROWAVE MODEL

5.1 Introduction.
5.2 Methodology
5.2.1 Moisture Distribution Measurements
5.2.2 Canopy Opacity.
5.2.3 Microwave Brightness Model.
5.2.4 Model Comparison and Evaluation
5.3 Results and Discussion.
5.3.1 Moisture Distribution Function
5.3.2 Canopy Opacity.
5.3.3 Microwave Brightness.
5.4 Summary

6 CONCLUSION.


Summary
Contributions.
Recommendations for Future Research


REFERENCES ...... .........

BIOGRAPHICAL SKETCH ...........






























o



Lo

-



d



b)Nor-0 )
c r- 0

000








..












lo






0~lL~0 0


E
a












O


m

a


1




c


L





























0~lL~0 0


a co





Hd
ed a













co



to










O'







cb


00 0


C
C-









,7 _






-r



L. 1




J -- -~

LOhlLO~L00
h10 O0
d d d

(EW/EW) VUSA


X2 X




















80 90 100 110 120 130 140 150


80 90 100 110 120 130 140 150


80 90 100 110 120 130 140 150







80 90 100 110 120 130 140 150







80 90 100 110 120 130 140 150

MicroWEX-2 LSP LSP-DSSAT
(f)


130 140 150


80 90 100 110 120
DoY 2004 (EST)


Figure 4-28. Comparison of volumetric soil moisture estimated by the coupled
LSP-DSSAT and stand-alone LSP model simulation and those observed
during MicroWEX-2: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and
(f) 100 cm.










Table 5-2. RMS differences between observed Tg during MicroWEX-5 and those estimated
by the MB model
RMSD (K)
-r model DAP < 47 DAP > 47 DAP 42-52
Jackson (w = 0.00) 5.84 12.50 9.74
Jackson (w = 0.06 for DAP > 47) 5.84 3.635 4.88
Biophysical w = 0.05 for 42 Bio hsical w = 0.075 for DAP > 47 5.00 5.22 5.13


decreased by 30 K( due to an irrigation event. The Tg at 6.7 GHz were sensitive to soil

moisture changes even when the c Ilr..pi- cover was 100' .~ and biomass was 2.7 kg/m2 (SeO

Figure 5-1).

A small value for single-scattering albedo (w) was included in the MB model when

using the '1;i nphi--;1 11 -r estimates. The value of 0.05 before ear formation (DAP 47) and

0.075 after DAP 47 provided the least RMSD (see Table 5-2).

In the Jackson model, b = 0.25, similar to the literature-based values for corn [61],

provided the lowest RMSD. Typically w is set to zero in the Jackson model [51], but it was

found that the Tg using the Jackson model was overestimated after ear formation, with an

RMSD of 12.50 K(. Including w = 0.06 after ear formation in the Jackson model reduced

the RMSD to 3.65 K(,as shown in Table 5-2. The values of w needed to provide the least

RMSD for both -r models were small, < 0.1, implying that single scattering is sufficient to

provide realistic Tg estimates for the mature sweet corn ( Ilr1.ipi and multiple scattering is

not needed. The overall RMSD between observed Tg and the modeled Tg using the two

-r models were similar, with 5.13 K( for the biophysical model and 4.88 K( for the Jackson

model (see Table 5-2).










(Hse), and c Ilr1.ipi and air (Hea), are calculated as:


T, T
Hsa =, pcp, s fs (4-11)

T, T
H,, = pacp, fy (4-12)
rse + Tbh

Hm, = pacp, fy (4-13)
rc + Tbh

where T,, T,,and T, are the air, soil, and canopy temperatures (K(), respectively, p, is the

air density (kg/m3 Cp, is the specific heat (J/kg K(), fy and fs are the vegetation and

bare soil cover fractions, respectively.

The aerodynamic resistances rs (soil-air) and rc (canopi-- .Ir~) are determined

assuming a log wind profile above the c lis..pi- or bare soil [24]:


ras =(4-14)
ku*


rac =(4-15)
ku*
kU(z)
U* (4-16)
In (z-a qMz

where u* is the friction velocity, W is the Businger-Dyer stability function [17], k is von

K~arman's constant (0.4), z is the measurement height, d is the vegetation displacement

height (taken as 0.63he, he is the plant canopy height), zov is the vegetation roughness

length (0.160), and zob is the bare soil roughness length.

For the aerodynamic resistance between the soil and the calrs..pi-, the log profile is not

valid due to momentum absorption by the c Il .pi- elements, so an exponential wind profile

in the calrs..pi- is used [24], with the under-c Il .pi- resistance, rse, from Nin and Yang [42]:


rs =he[ea(1-zoblhc) ea(1-zon/Ac)] 47
a~ch









between the two models. The LSP model uses short timesteps (on the order of seconds)

and a user-defined number of nodes (35 in the top 1.8 m for this study). DSSAT uses

daily timesteps, with 9 nodes in the top 1.8 m. In the coupling, the LSP model essentially

replaces the soil and soil-plant-atmosphere modules of the DSSAT model. To account for

the timestep difference, the soil moisture and temperature profiles estimated by the LSP

model are averaged daily. The latent heat fluxes are accumulated daily and converted from

W/m2 to mm/d or-, treating soil and vegetation latent heat fluxes separately so that it can

match the DSSAT requirements. To account for the difference in thickness of soil nodes,

the daily averages of soil moisture and temperature profiles from the LSP were spatially

averaged to match the soil nodes in the DSSAT. In addition, the root length density for

the 9 DSSAT nodes are interpolated/extrapolated to match the LSP nodes. Because the

LSP model does not include nitrogen transport in c Ilr~li- and soil, the DSSAT model

is run assuming there is no nitrogen stress. This is a reasonable assumption for heavily

fertigated soils, such as those during MicroWEX-2.

4.4 Methodology

In this study, the model simulations were conducted using two scenarios. First,

using a stand-alone LSP simulation forced with vegetation parameters observed during

MicroWEX-2 and second, using the coupled LSP-DSSAT model.

4.4.1 Inputs and Initial Conditions

Both the LSP and LSP-DSSAT models were run from planting on DoY 78, to

harvest on DoY 154, 2004. Micrometeorologfical forcingfs were obtained from observations

during MicroWEX-2, and from a nearby weather station, installed as part of the Florida

Automated Weather Network (FAWN). The precipitation/irrigation observations exhibited

most variability between the four raingauges (Figure 2-5). To obtain forcing for the model

simulations, we confirmed that raingauge data coincided with the observed soil moisture

increases. The data were scaled such that the daily accumulated observations from the
















S300

280 (a
135 140 145 150 15
320


S300

280 (b


(c)

35 140 145 150 li





(d)


MicroWEX-2 LSP - LSP-DSSAT

300 _ -


135


140


145


150


320


280
135


280
135


145
DoY 2004 (EST)


Figure 4-26. Comparison of soil temperature estimated by the coupled LSP-DSSAT and
stand-alone LSP model simulation and those observed during 1\icroWEX-2,
between DoY 135 to 154: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f ) 100 cm.





















10 20 30 40 50 60 70 80 90





(b)


10 20 30 40 50
Days After Planting



Figure 5-2. Observations of canopy height during (a)
MicroWEX-5 in 2006.


60 70 80 90



MicroWEX-4 in 2005 and (b)


where & is canopy height (m), ko is vacuum wavenumber (m l), and s(z) = -Im~nt(z)}

(Np/m) is the absorption coefficient of the canopy. Im~nt(z)} is the imaginary part of the

complex refractive index, estimated as the sum of volume fraction of components,


p(z)


nt(z) = 1 + vense


(5-2)


where, ve is the volume fraction of the wet vegetation (m3 m3) n, wcis the refractive

index of the wet vegetation and p, is the density of wet vegetation (697.72 kg/m3 for this

study [7]). Ulaby and El-R li-o -' model [57] estimates nc as a function of frequency (6.7

GHz for this study) and moisture mixing ratio (11(,). ii i, is defined as the ratio of weight

of water in the c Ilrei- to the weight of wet canopy. Figure 5-4 shows the mixing ratio

during MicroWEX-4 and -5. In this model, an isothermal canopy is assumed, so that the


























S100ft(30.48m


200ft (60.96m) ,00H3143 48md


600 ft (182.88m)


O: wells :ctroot(ance2mS1m mousanxsomH x: R,,,&,es

SCR23x, wlth soil molsdure*, sell temperature*, and sell heat flux I .TIR

E E CR23x. wilh Eddy Coveriance System
V.S
S: CR10, with soil molisture*. soril lem~perature*, and soil heat 1ux

*: The sensors are installed at dseph: 2, 4, 8, 16, 32, 64, and 100 cm


a :Aadiemetre


O :CNR

vegetauonl
. -Sampling
Area


Figure 2-5. Map of the field site during MicroWEX-2.


100 ft











Directio~n
of piasnting










[26] Jackson, T., and T. Schma i- -~ (1989), Passive microwave remote sensing system for
soil moisture: Some supporting research, IEEE Trans. Geosci. Remote Sensing, 27(2),
225-235.

[27] Jang, M.Y., K(. Tien, J.Casanova, and J.Judge (2005), Measurements of soil
surface roughness during during the fourth microwave water and energy balance
experiment: From April 18-June 13, 2005, Tech. Rep. Circular No 1488, Center
for Remote Sensing, University of Florida, Available at UF/IFAS EDIS website at
http://edis.ifas.ufl.edu/AE393.

[28] Jones, C., and J. K~iniry (Eds.) (1986), CERES-M~aize: A Simulation M~odel of M~aize
Growth and Development, Texas A&M University Press, College Station, Texas.

[29] Jones, J. W., G. Hoogenboom, C. Porter, K(. Boote, W. B. L. Hunt, P. Wilkens,
U. Singh, A. Clii-nar lr and J. Ritchie (2003), The DSSAT cropping system model,
European J. Agror:.-tera 18(S-4), 235-265.

[30] Judge, J., A. England, C. L. W. Crosson, B. Hornbuckle, D. Boprie, E. K~im, and
Y. Lion (1999), A growing season land surface process/radiobrightness model for
wheat-stubble in the southern great plains, IEEE Trans. Geosci. Remote Sensing,
87(5), 2152-2158.

[31] Judge, J., L. Abriola, and A. England (2003), Development and numerical validation
of a summertime land surface processand radiobrigfhtness model, Advances in Water
Resources, 26(7), 733-746.

[32] Judge, J., A. W. England, J. R. Metcalfe, D. McNichol, and B. E. Goodison (2007),
Calibration of an integrated land surface process and radiobrightness (LSP/R) model
during summertime, Advances in Water Resources, In Press.

[33] Judge, Jasmeet, J. Casanove, T. Lin, K(. Tien, M. Jang, J. Judge, O. Lanni, and
L. Miller (2004), Field observations during the second microwave water and energy
balance experiment (11.croWEX-2): From March 17-June 3, 2004, Tech. Rep.
Circular No 1480, Center for Remote Sensing, University of Florida, Available at
UJF/IFAS EDIS website at http://edis.ifas.ufl.edu/AE360.

[34] K~err, Y. H., P. Waldteufel, J. Wigneron, J. Martinuzzi, J. Font, and M. Berger (),
Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS)
mission, IEEE Transactions on Geoscience and Remote Sensing, 89.

[35] K~ustas, W. P., and J. M. Norman (2000), A two-source energy balance approach
using directional radiometric temperature observations for sparse canopy covered
surfaces, Agron. J., 92, 847-854.

[36] Leese, J., T. Jackson, A. Pitman, and P. Dirmeyer (2001), GEWEX/BAHC
international workshop on soil moisture monitoring, analysis, and prediction for
hydrometeorological and hydroclimatological applications, Bull. Amer. M~eteorol. Soc.,
\ '(7), 1423-1430.






















































(e)




80 85 90 95 100 1


MicroWEX-2 LSP LSP-DSSAT





80 85 90 95 100 1
DoY 2004 (EST)


E 0.2
E 0.15
0.1
0.05



E 0.
E 0.15
S0.15




S01

0.05








mE 0.2
E 0.15


0 05



E 0.2
E 0.15
0.1

0.05


80 85 90 95 100 105


80 85 90 95 100 105


(d)


Figure 4-9.


Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during
1\icroWEX-2, between DoY 78 to 105: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32
cm, (e) 64 cm, and (f) 100 cm.













Ec 800
600-
m 400
rr200


-200
135 140 145 150 155



300-

E^ 200 -(b)
10 -~


S-100
S-200
-300-
135 140 145 150 155
DoY 2004 (EST)

Figure 4-23. Comparison of net radiation, between DoY 135 to 154, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


LSP-DSSAT model simulations, the VSMs at all 111-;- rs exhibit positive bias that increases

during the season. A bias of ~0.02 m3 m3 COuld be introduced at the beginning of the

simulation due to improper initial conditions (Section 4.4.1) and significant uncertainty in

raingauge observations. During MicroWEX-2, the differences between daily accumulations

from the four raingauge observations and those observed independently by using

collection cans were up to 10s of mm/d?-,. Previous studies have also found similarly

high uncertainties in precipitation, at 12 mm/h, using such raingauges [44].

The VSM bias of ~ 0.06 m3 m3 for the lIn-;-rs 0.64 m and below (Figures 4-28 (e) and

(f)) could be due to the improper retention curve parameters in the clay lIn-;-r (below 1.7

m). The parameters were based only on one soil sample from that lIn-;-r and could have

resulted in lower flux estimates at the lower boundary and higher biases for the deeper






































































280~ -
125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)


Figure 4-22. Comparison of soil temperature estimated by the coupled LSP-DSSAT and
stand-alone LSP model simulation and those observed during 1\icroWEX-2,
between DoY 125 to 135: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,

and (f ) 100 cm.


- MicroWEX-2 LSP LSP-DSSAT


___ ~_ _ ___ ___ _______


320


,300~ 1


280 (a

125 126 127 128 129 130 131 132 133 134 135


125 126 127 128 129 130 131 132 133 134 135

320


I


UL


()(


S300


280 ~ 1
125 126 127 128 129 130 131 132 133 134 135


I


320


S300


-' -=-i I----~--p~ ~


280~ i :
125 126 127 128 129 130 131 132 133 134 135

320


S300

280 -(e)

125 126 127 128 129 130 131 132 133 134 135


320


S300






















10 20 30 40 50 60 70 80 90










(b)

10 2d0 30 40 50 60 70 80 90
Days After Planting



Figure 5-1. Observations of total and ear wet biomass during (a) MicroWEX-4 in 2005
and (b) MicroWEX-5 in 2006.


obtain accurate moisture distribution. The density of vegetation material for each lI .,-c

was measured by volume displacement in a graduated cylinder.

Density of wet vegetation and air, p(x), called the cloud density, was calculated for

each lI.-< c as a ratio of wet biomass of each lI gs. r and the thickness of the 1... -r (10 cm).

The mass of air is negligible. To obtain seasonal pattern, the cloud density of each lI .,-c

was plotted as a function of height of the 1.,-< r, shown in Figure 5-3.

5.2.2 Canopy Opacity

The < Iln, pi- opacity (-r) is estimated as [59, 60]:


o r)d (5-1)









REFERENCES


[1] Boland, J., L. Scott, and M. Luther (2001), Modelling the diffuse fraction of global
solar radiation on a horizontal surface, Environmetrics, 12, 103-116.

[2] Busetti, F. (2004), Simulated annealing overview, Tech. rep., Available at
www.geocities.com/francorbusetti/saweb.pdf

[3] Camillo, P., and R. J. Gurney (1986), A resistance parameter for bare soil
evaporation models, Soil Science, 141, 95-105.

[4] Campbell, G. S., and J. M. Norman (1998), An Introduction to Environmental
B.:u 'iInI; -.: 2nd ed., 286 pp., Springer Science+Business Media, New York.

[5] Campbell Scientific (2006), CS616 and CS625 Water Content Reflectometers, Tech.
rep., Available at http://www.campbellsci.com/documents/manuasc66pf
Logan, Utah.

[6] Casanova, J., T. Lin, M. Jang, K(. Tien, J. Judge, O. Lanni, and L. Miller (2005),
Field observations during the fourth microwave water and energy balance experiment
(11.. coWEX-4): From March 10-June 14, 2005, Tech. Rep. Circular No 1482, Center
for Remote Sensing, University of Florida, Available at UF/IFAS EDIS website at
http://edis.ifas.ufl.edu/AE>.21~

[7] Casanova, J., M. Jang, and J. Judge (2006), Vertical distribution of moisture
in a sweet corn c me-pli-, Tech. Rep. Circular No 1492, Center for Remote
Sensing, University of Florida, Available at UF/IFAS EDIS website at
http://edis.ifas.ufl.edu/AE395.

[8] Casanova, J., F. Yan, K(. Tien, J. Judge, O. Lanni, and L. Miller (2006), Field
observations during the fifth microwave water and energy balance experiment
(11.. coWEX-5): From march 9-may 26, 2006, Tech. Rep. Circular No 1514, Center
for Remote Sensing, University of Florida, Available at UF/IFAS EDIS website at
http://edis.ifas.ufl.edu/AE407.

[9] Changlb, Y. (2007), A snow-soil-vegetation-atmosphere-transfer/aibghns model
for wet snow, Ph.D. thesis, University of Michigan.

[10] Dai, Y., et al. (2003), The Common Land Model (CLM), Bull. Amer. Meteor Soc.,
84 (8), 1013-1023.

[11] de Vries, D. A. (1963), Thermal properties of soils, in Ph ;,;-: ; of Plant Environ-
ment, edited by W. R. van Wijk, North-Holland Publishing Company, Amsterdam,
Netherlands .

[12] Demarty, J., C. Ottle, I. Braud, and J. Frangi (2002), Comparison of measured and
SISPAT-RS simulated brightness temperatures and reflectances at field scale during
ReSeDA experiment, Proceedings of SPIE, the International S -. .:. I r for Op~tical









where a and Ku are the canopy damping coefficient and the aerodynamic conductance for

heat at the top of the canopy [24], given by:

cdLAIlh
a =(4-18)

where

Im = 2 (4-19)
xrLAI

Ku = k~u*(he d) (4-20)

where, Im is the c lis..pi- momentum length, i, is the wind intensity factor, ca is the drag

coefficient, and I, is c Ilr1.ipi width. The leaf boundary 1... -r resistances for heat transport,

Tbh, iS calculated as:

Tbh = (180)11 (4-21)


ac = k~u*ln he d (4-22)

Latent Heat Flux:

Latent heat flux is based upon the resistance network (see Figure 4-1(b)). Three

sources that contribute to the flux are: soil evaporation (LE,), ( Ilr.ipi- transpiration

(LEtr), and evaporation of intercepted precipitation (LEer).


LE, = p s-9)(4-23)


fy(1 XI)1 4 )
LEr=, aq~at-G)(-4


L~ev= Ap~qc~at -Ga)(4-25)

where go, q,, and qc,sat are the specific humidities of the air, soil surface 1... r, and

saturated canopy, respectively, A is the latent heat of vaporization of water, and xl is

the fraction of vegetation covered in intercepted precipitation, calculated by

W,
xl (4-26)














E 600 (a)

S400-

S200-


-200
105 110 115 120 125



300-
(b)
200-
100 -


i -100 *
-200
-300-
105 110 115 120 125
DoY 2004 (EST)

Figure 4-14. Comparison of soil heat flux, between DoY 105 to 125, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


correspond to the RMSD expected from the Pareto front in Figure 4-3. Though the net

radiation matches well, it is still biased high, which would permit lower leaf surface vapor

resistance by Equations 4-29 and 4-30, resulting in overestimated LE from increased

canopy transpiration. Overestimated VSM, shown in Figure 4-21, (RMSD 0.0492 m3 m3

and positive bias 0.0472 m3 m3) COuld also lead to overestimation of LE by increasing soil

evaporation.

Sensible heat flux (Figure 4-19) is overestimated with RMSDs of ~43 W/m2 biases of

~22 W/m2. This overestimation could be due to overestimated vegetation aerodynamic

roughness length.

The 2 cm soil heat flux (Figure 4-20) is slightly overestimated with RMSD of ~44

W/m2 and bias of ~0.70 W/m2. Since during full cover, the net flux going into the soil









CHAPTER 2
MICROWAVE WATER AND ENERGY BALANCE EXPERIMENTS

The MicroWEXs are a series of experiments conducted by the Center for Remote

Sensing at the University of Florida during growing seasons of corn and cotton [6, 8, 33,

37, 55, 67]. The objective of the experiments are to understand microwave signatures of

agricultural crops during different stages of growth. MicroWEX-2 was conducted during

the sweet corn growing season, from March 18 through June 2 in 2004 [33]. MicroWEX-4

was conducted during the sweet corn growing season, from March 10 through June 2 in

2005 [6]. MicroWEX-5 was conducted during the subsequent corn season from March 9

through May 26 in 2006 [8]. All experiments were conducted at the same 37,000 m2 Site

in UF/IFAS Plant Science Research and Education Uni in Citra, FL (29.41 N, 82.18

W). The soils at the site are Lake Fine Sand with about 90 .~ sand and a bulk density of

1.55 g/cm3. ROW Spacing was 76 cm, with approximately eight plants per square meter.

Irrigation and fertigation were conducted via a linear move system.

Data collected during the MicroWEXs included soil moisture, temperature and

heat flux, latent and sensible heat flux, wind speed and direction, upwelling and

downwellingf short and longfwave radiation, precipitation, irrigation, water table depth,

and vertically and horizontally polarized microwave brightness at 6.7 GHz (A = 4.47 cm),

every fifteen minutes using the tower-mounted University of Florida C-band Microwave

Radiometer (UFC11Rl, Figure 2-1). Additional horizontally polarized microwave brightness

observations at 1.4 GHz (A = 21.4 cm) were conducted during MicroWEX-5 using the

UF L-Band Microwave Radiometer (UFLMR, Figure 2-2). The radiometer frequencies, at

6.7 GHz and at 1.4 GHz, correspond to the lowest frequency of the Advanced Scanning

Microwave Radiometer (AMSR-E) [22], and the frequency of the planned Soil Moisture

and Ocean Salinity (SMOS) mission [34], respectively.

The soil moisture, heat fluxes, and temperatures were observed at three locations

in the field. Soil moisture and soil temperature were observed at 2, 4, 8, 16, 32, 64,










[63] Wegmilller, U., and C. Miltzler (1999), Rough bare soil reflectivity model, IEEE
Trans. Geosci. Remote Sensing, 87(3), 1391-1396.

[64] Whitfield, B., J. Jacobs, and J. Judge (2006), Intercomparison study of the land
surface process model and the common land model for a prairie wetland in Florida,
Jouna o H./..;;./..rJ.,i; (6), 1247-1258.

[65] Williams, J., P. Dyke, and C. Jones (1983), EPIC: A model for assessing the effects of
erosion on soil pr ..1;,. 1.;.:It; Elsevier, Amsterdam.

[66] Willmott, C. J. (1982), Some comments on the evaluation of model performance,
Bulletin of the American M.~ I..r J..y.~~ .: arl 8... ..~ it; 68, 1309-1313.

[67] Yan, Fei, J. Casanova, K(. Tien, J. Judge, O. Lanni, and L. Miller (2006), Field
observations during the sixth microwave water and energy balance experiment
(11u, coWEX-6): From June 19-October 31, 2006, Tech. Rep. Circular No 1515,
Center for Remote Sensing, University of Florida, Available at UF/IFAS EDIS
website at http://edis.ifas.ufl.edu/AE409.









CHAPTER 4
CALIBRATION OF AN SVAT MODEL AND COUPLING WITH A CROP MODEL
FOR SWEET CORN

4.1 Introduction

This chapter describes the coupling of an SVAT model with a crop growth simulation

model to estimate land surface fluxes in growing vegetation and evaluate the performance

of the coupled model for estimating root-zone soil moisture and ET observations from

an extensive field experiment. Both categories of models benefit from two decades of

development and testing by their respective research communities. The SVAT model,

viz. Land Surface Process (LSP) model, simulates one-dimensional energy and moisture

transport as well as radiative, sensible and latent heat fluxes at the land surface. The

cropping system model, viz. the Decision Support System for Agrotechnology Transfer

(DSSAT), is a widely-used and tested modular suite of crop models that simulate crop

growth biomasss accumulation) and development (vegetative and reproductive growth

stages). Neither model is structurally changed and an interface is created to link the two

models. In the coupled LSP-DSSAT model, the DSSAT model provides the LSP model

with vegetation characteristics that influence heat, moisture, and radiation transfer at the

land surface and in the vadose zone and the LSP model provides the DSSAT model with

estimates of soil moisture and temperature profiles and evapotranspiration (ET).

4.2 LSP Model

The LSP model was originally developed by the Microwave Geophysics Group at

the University of Michigan [:38]. The model simulates 1-d coupled energy and moisture

transport in soil and vegetation, and estimates energy and moisture fluxes at the land

surface and in the vadose zone. It is forced with micrometeorologfical parameters

such as air temperature, relative humidity, downwelling solar and longwave radiation,

irrigation/precipitation, and windspeed. The original version has been rigorously tested

[:31] and extended to wheat stubble [:30] and brome-grass [:32], prairie wetlands in Florida

[64], and tundra in the Arctic [9].


































*This thesis focuses on the
vegetation component of the
MB model


Figure 1-1. Outline of the data assimilation scheme and the forward model.


Tb = Tb,sail + Tb,canopy + Tb,sky











Figure 1-2. Contributions to microwave brightness Tg from sky, soil, and c Ilrspi.









CHAPTER 5
CANOPY MICROWAVE MODEL

5.1 Introduction

In this chapter a refractive model is developed for vegetation opacity of growing

sweet corn based upon moisture distribution in the < my .li- and incorporated into a

simple microwave brightness model linked with the coupled LSP-DSSAT model. The

refractive model developed by England and Galantowicz [19] was extended for sweet corn

using observed moisture distribution during the Fourth and Fifth Microwave Water and

Energy Balance Experiments (11ul coWEX-4 and -5). The -r estimated by the model is

compared with that estimated using the Jackson model. The -r values obtained from the

two approximations were used in a microwave emission model at C-band and the model

estimates of brightness were compared with field observations.

5.2 Methodology

5.2.1 Moisture Distribution Measurements

Five measurements of moisture distribution were conducted during MicroWEX-4:

May 12 (Day After Planting (DAP) 6:3), 31 li- 17 (DAP 68), May 26 (DAP 77), June 2

(DAP 84), and June 6 (DAP 88). The samples collected on DAP 6:3 and 88 consisted of

plants in vegetative stage, i.e., before ear formation, while those collected on other d .n~

consisted of plants at various reproductive stages. Additional plant sample was obtained

on DAP 88 to determine the density of wet vegetation (solid). Three measurements of

moisture distribution were conducted during MicroWEX-5: April 10 (DAP :32), ?l li- 1

(DAP 5:3), and May 15 (DAP 67). The samples collected on DAP 5:3 and 67 consisted of

plants in reproductive stages.

All representative plant samples were cut every 10 cm and weighed wet. The samples

were dried at 70" C for at least 48 hours and weighed to obtain dry biomass. The samples

on DAP 6:3, 2005 were cut every 5 cm to ensure that finer samples were not needed to










4-5 Comparison of net radiation, between DoY 78 to 105, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 56

4-6 Comparison of latent heat flux, between DoY 78 to 105, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 57

4-7 Comparison of sensible heat flux, between DoY 78 to 105, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 58

4-8 Comparison of soil heat flux, between DoY 78 to 105, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 59

4-9 Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during MicroWEX-2,
between DoY 78 to 105: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f) 100 cm. ............ .......... 60

4-10 Comparison of soil temperature estimated by the coupled LSP-DSSAT and stand-alone
LSP model simulation and those observed during MicroWEX-2, between DoY
78 to 105: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm, and (f) 100 cm. .61

4-11 Comparison of net radiation, between DoY 105 to 125, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 62

4-12 Comparison of latent heat flux, between DoY 105 to 125, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 63

4-13 Comparison of sensible heat flux, between DoY 105 to 125, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those observed
during MicroWEX-2: (a) values and (b) residuals ... ... .. 64

4-14 Comparison of soil heat flux, between DoY 105 to 125, estimated by the coupled
LSP-DSSAT and stand-alone DSSAT model simulation and those observed during
MicroWEX-2: (a) values and (b) residuals ..... .. . 65

4-15 Comparison of volumetric soil moisture estimated by the coupled LSP-DSSAT
and stand-alone LSP model simulation and those observed during MicroWEX-2,
between DoY 105 to 125: (a) 2 cm, (b) 4 cm, (c) 8 cm, (d) 32 cm, (e) 64 cm,
and (f) 100 cm. ............ .......... 66










The modeled and observed emergence dates were on DoY 90 and DoY 86, respectively.

Modeled anthesis dei (when '7 ".' of the corn has silked) was DoY 1:39, while '7 ".' silking

was observed by DoY 1:35. The model estimated realistic total dry biomass using the

parameters determined by the grid search, as shown in Figure :3-1. The R MSD for

biomass was 0.90 hig/ha with a low RRMSD of 0.2:3 and a correspondingly high Willmott

d-index of 0.99, as shown in Table :3-2. Figure :$-1b shows a scatter plot of estimated

and observed total biomass. The biomass was increasingly underestimated by the model

as the season progressed, with the nmaxiniun difference of 1.41 Mg/ha at the end of the

season. The partitioning of the modeled biomass into leaf and stent biomass did not match

the observations (Figure :$-la), as indicated by the high RMSD and RRMSD in Table

:3-2. Partitioning of total biomass into stent biomass was underestimated by the model

during later vegetative stages of growth (DoY 127 to DoY 1:34). The partitioning into

leaf biomass was more realistic, with a slight overestiniation during later growth stages

(DoY 1:32 to DoY 142). The model's estimate of the beginning of grain fill at DoY 140

matched closely with the observed grain fill at DoY 1:39 (Figure :$-la). The best fit for

LAI and total biomass did not produce the best fit for grain fill. In order to compensate

for the underestimated stent biomass, grain weight must he overestimated. The model

estimated realistic LAI, as seen in Figure 3-1, with a low RMSD and RRMSD of 0.22 and

0.1:3, respectively, and a high Willmott d-index of 0.99, as shown in Table :3-2. Figure :$-id

shows the scatter plot of the model and observed LAI.





























(b)







80 85 90 95 100 1C
DoY 2004 (EST)


80 85 90 95 100


-300


Figure 4-5.


Comparison of net radiation, between DoY 78 to 105, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


Table 4-7. Measurement uncertaintities during MicroWEX-2.


Sensor
Raingauge
TDR
Thermistor
Soil heat flux
Net radiation
Latent heat flux
Sensible heat flux


Uncertainty
12 mm/h
0.025 VSM
0.1 K(
15 W/m2
22 W/m2
17-36 W/m2
21 W/m2


Reference




































S2007 Joaquin J. Casanova










in ears once corn reaches the reproductive stage. As a result, Figure 5-5 shows a sharp

increase in optical depth at the onset of ear development, at DAP 62 for MicroWEX-4 and

DAP 47 for MicroWEX-5. By the end of the seasons, the optical depth is doubled when

ears are included.

5.3.3 Microwave Brightness

Figure 5-6 shows the comparison of the horizontally polarized (H-pol) Tg observed

during MicroWEX-5 with those simulated by the MB model at C-band, with -r estimates

using the 'l;.1!i--1 1. 1 model and the Jackson model. Only H-pol brightness is examined

here because H-pol brightness is more sensitive to changes in soil moisture than V-pol, at

the incidence angle of 500, that is close to the Brewster angle at microwave frequencies

[59]. The observed Tg increased during the drydown from DAP 42 to DAP 46.7 and then













M m d l ou
Bm dl(Jcsn1
-" MirW X- berain



43 44 45 46 47 48 49 50 51 5
Day Afte PlnigET





~~~~~~~~~~~~~MB model usn rmtebohsia oe n rmteJackson mode




during late-season MicroWEX-5.









CHAPTER 6
CONCLUSION

In this chapter, the results and contributions from this thesis are summarized and

recommendations for future research are provided.

6.1 Summary

This thesis provides important insights into crop, SVAT, and microwave brightness

modeling for growing vegetation. Three season-long extensive field experiments (1becroWEX-2,

4, and 5) were conducted, monitoring radiobrightness, soil moisture, soil temperature,

surface fluxes, and crop growth for sweet corn. These experiments provided the datasets

used in forcing, d. i.; I~l .ph and calibrating the models.

First, a crop model and SVAT model were calibrated and coupled. The crop model

(CERES-Maize) was calibrated with simulated annealing using field observations for the

MicroWEX-2 site. The calibration was performed by minimizing the residuals for LAI

and biomass, the two most important canopy parameters in determining the microwave

signature of a vegetation < Ilnupi- The SVAT model (LSP) was calibrated with Latin

Hypercube Sampling to provide the least R MSD in LE with an R MSD in VSM at 2 cm of

~0.04 m"/m", using observations during MicroWEX-2. The LSP and DSSAT models were

coupled such that the LSP replaced the DSSAT soil and soil-plant-atmosphere modules

while DSSAT provides LSP with LAI, biomass, height, width, and root length density.

Model estimates of surface fluxes, VSM, and soil temperature were very similar using both

the coupled LSP-DSSAT and stand-alone LSP that used observed vegetation parameters.

Second, a microwave transmission model was developed and compared with the

widely-used empirical Jackson model, and with observations of of microwave brightness

for a period during MicroWEX-5. This -r model was incorporated in a MB linked with

the coupled LSP-DSSAT model, and tested for the MicroWEX-2 growing season.

The -r obtained from the biophysical model estimated higher values than the Jackson,

with an R MSD between the two of up to 0.23 Np. The -r values obtained from the two










3.4.3 Soil Moisture and Temperature

T> understand the model performance regarding moisture and energy transport

in soil, modeled daily soil moisture and temperature profiles are compared to the

observed average daily values during MicroWEX-2 (Figures :3-3, :3-4, and :3-3). T> compare

observations at 2, 4, 8, 16, :32, 64, and 100 cm to model estimates of the top six 1 .;< rs,

the average of 2 and 4 cm observations are compared to estimates of 0-5 cm, 8 and 16

cm observations to estimates of 5-15 cm, 16 and :32 cm observations to estimates of 15-:30

cm, average of :32 cm observations to estimates of :30-45 cm, :32 and 64 cm observations to

estimates of 45-60 cm, and 64 and 100 cm observations to estimates of 60-90 cm.














































MicroWEX-2 LSP LSP-DSSAT
(f)
_ _


E 0.2-
E 0.15
0.1
0.05 -
105

E 0.2-
E 0.15-

0.05 -
105


mE 0.2
E 0.15-
0.
0.05 -
105

S0.2
S0.15

0.05 -
105


mE 0.2
E 0.15 -
0. -
0.05 -
105

E 0.2
E 0.15 -
0 1,
0.05 -
105


110 115 120 125


110 115 120 125


110 115 120 125



( -


(e)
'Y


115
DoY 2004 (EST)


Figure 4-15. Comparison of volumetric soil moisture estimated by the coupled
LSP-DSSAT and stand-alone LSP model simulation and those observed
during 1\icroWEX-2, between DoY 105 to 125: (a) 2 cm, (b) 4 cm, (c) 8 cm,
(d) 32 cm, (e) 64 cm, and (f) 100 cm.









Table 5-1. Values of the Coefficients in equations 5-6 and 5-7
Coefficients Values
a 2.054
b -2.054
a~c -114.32
asc 5.87
a~e -1.23
Pc 25.69
Pd -1.37
Pe 0.34
70 8.41
Yd 0.29
TYe -0.07


5.3 Results and Discussion

5.3.1 Moisture Distribution Function

As shown in Figure 5-3, the cloud density function consists of two terms, a linear

term representing the vegetative stage of the plant and a gaussian term representing the

moisture in the ear during the reproductive stages, as:


(5-6)


where a, b, c, d, and e are fitted parameters, h, = z/h is the normalized height, and B,

and Be are the wet biomass of vegetation (stem and leaves) and ear (kg/m2), TOSpectively.

Figure 5-3 also shows the best curve-fits obtained for each sample. The parameters c, d,

and e, governing the gaussian term, were estimated as quadratic functions of dry biomass

of the ear (Dear) as:


c = ac, + PcDear +7cD ear

d = as + PdDear + TaD ar (5

e = ae, + PeDear + 7eD ar

he a and b parameters and the coefficients in equation 5-7.


7)


Table 5-1 gives the values of tl


B, B ep 1 &, -d)"
p(z) = (a + bb,,) + c eep"
h & 2 e










Abstract of Thesis Presented to the Graduate School
of the University of Florida in Partial Fulfillment of the
Requirements for the Degree of Master of Engineering

MODELING LAND SITRFACE FLITXES AND MICROWAVE SIGNATURES OF
GROWING VEGETATION

By

.Joaquin .J. Casanova

December 2007

CI. ur~: .Jasmeet .Judge
Major: Agricultural and Biological Engineering

Soil moisture in the root zone is an important component of the global water and

energy balance, governing moisture and heat fluxes at the land surface and at the

vadose-saturated zone interface. Typically, soil moisture estimates are obtained using

Soil-Vegetation-Atmosphere Transfer (SVAT) models. However, two main challenges

remain in SVAT modeling. First, most models often oversintplify the coupling between

vegetation growth and surface fluxes, and second, model errors accumulate due to

uncertainty in parameters and forcing, and numerical computation. The ultimate goal

of this research is to improve estimates of root-zone soil moisture and ET by linking an

SVAT model with a crop growth model, and assimilating remotely-sensed observations

sensitive to soil moisture, such as microwave brightness (ill:). Toward that goal, a coupled

SVAT-Crop model will be developed, calibrated, and linked to an MB model, to comprise

the forward model for data assimilation. The models will use observations front three

season-long field experiments monitoring growing sweet corn.









BIOGRAPHICAL SKETCH

Joaquin Casanova was born on December 25, 1984, in Gainesville, Florida. Some stuff

happened, then in 2006 he got his BS in Agricultural and Biological Engineering from UF.














Ec 800
600-

S400

rr200


-200
125 126 127 128 129 130 131 132 133 134 135



300-

E^ 200 -(b)
100-


S-100
S-200
-300-
125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)

Figure 4-17. Comparison of net radiation, between DoY 125 to 1:35, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


is dominated by the flux between the soil and the canopy, the overestimation of soil heat

flux indicates that soil-canopy flux is underestimated. This overestimation in soil heat flux

leads to overestimation in soil temperature (Figure 4-22), moreso than during intermediate

vegetation cover, with a positive bias < 2.68 K( and R MSD < :3.32 K(.

Reproductive Stage

The last 19 d on oOf the growing season, DoY 1:35 154, comprised the reproductive

stage, beginning with silk formation. During this period, the < Il 4 e- height was 162-200

cm, LAI was 2.49-2.75, and vegetation cover was 1.00. The biomass growth during this

stage was primarily due to ear growth.














E 600- a

S400-

S200-



-200
135 140 145 150 155



300-
(b)
200-


E~ -100


-200
-300-
135 140 145 150 155
DoY 2004 (EST)

Figure 4-24. Comparison of soil heat flux, between DoY 135 to 154, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


1.v. -r The decrease in drainage could also cause positive bias in VSM for the upper

1.>.r-is, closer to the land surface.

Overall, soil temperatures (Figure 4-29) for both model simulations match closely

with the MicroWEX-2 observations. During the bare soil period, soil temperature exhibits

positive bias of < 1.40 K( and this bias is reduced during the intermediate vegetation cover

period to < 0.91 K( due to a net reduction of soil heat flux estimates. As the soil heat flux

bias increases, the temperature bias increases to < 2.7 K( after DoY 125. The seasonal

RMSDs decrease with depth with a maximum of 2.43 K( (Table 4-6).

4.6 Conclusion

This chapter answers research questions 3, 4, and 5 given in Chapter 1.










LIST OF FIGURES


Figure page

1-1 Outline of the data assimilation scheme and the forward model. .. .. .. .. 15

1-2 Contributions to microwave brightness Tg from sky, soil, and < Ilr 0-i. .. .. 15

2-1 The University of Florida C-band Microwave Radiometer. .. .. .. 19

2-2 The University of Florida L-hand Microwave Radiometer. .. .. .. 19

2-3 The Eddy Covariance System. .. ... ... 20

2-4 The net radiometer used during the MicroWEXs. .... .. 20

2-5 Map of the field site during MicroWEX-2. ...... .. 22

2-6 Map of the field site during MicroWEX-4. ...... .. 2:3

2-7 Map of the field site during MicroWEX-5. ...... .. 24

:3-1 (a) Comparison of the CERES-Maize estimates and the observations of biomass
during MicroWEX-2, (b) scatter plot of estimated and observed biomass, (c)
comparison of the CERES-Maize estimates and the observations of LAI during
MicroWEX-2, and (d) scatter plot of estimated and observed LAI. .. .. .. :31

:3-2 Comparison of the latent heat flux estimates front CERES-Maize model using
four methods with the observations during MicroWEX-2 by (a) daily heat flux
and (b) cumulative ET. .. ... . 3:3

:3-3 Comparison of the CERES-Maize soil moisture estimates with MicroWEX-2
observations at depths of (a) 0-5 cm, (b) 5-15 cm, (c) 15-:30 cm, (d) :30-45 mi,
(e) 45-60 cm, and (f) 60-90 cm. . .. ... .. :35

:3-4 Comparison of the CERES-Maize soil temperature estimates with MicroWEX-2
observations at depths of (a) 0-5 cm, (b) 5-15 cm, (c) 15-:30 cm, (d) :30-45 mi,
(e) 45-60 cm, and (f) 60-90 cm. . .. ... .. :36

4-1 Surface resistance network to estimate sensible and latent heat fluxes in the LSP
model ........ .... . .... 42

4-2 Algorithm for the coupling of the LSP and DSSAT models. .. .. .. 47

4-3 Pareto fronts front calibration of the stand-alone LSP model. The asterisk represents
the point on the Pareto front where the total seasonal R MSD for 2 ent VSM is
0.04nt3/nt ............ ............ 51

4-4 Comparison of estimations by the coupled LSP-DSSAT and stand-alone DSSAT
model simulation and those observed during MicroWEX-2: (a) dry biomass, (b)
LAI, (c) 5 cm soil moisture, and (d) ET. ...... .. 54










approximations were used in a microwave emission model at C-band, the model estimates

of Tg matched well with observations during MicroWEX-5, using both -r values when w is

included, with similar R MSDs.

The brightness temperatures predicted by the linked LSP-DSSAT-MB model

during the first half of the MicroWEX-2tested for MicroWEX-2, for the first half of

the MicroWEX-2 season were higher than those observed when the Wegfmiller and

Maitzler reflectivity model was used and were significantly lower than observations when

a specular model was used. In addition, overestiniation of moisture hv the LSP model

lead to underestimation of brightness. Later in the MicroWEX-2 season, brightness is

overestimated when using the w values found for MicroWEX-5, -II---- -r l.-:: they are too

low.

6.2 Contributions

One of the 1 in .ini contributions of this thesis is the development and calibration of

the coupled SVAT-Crop model as well as the development of the physically-based canopy

transmission model for sweet corn. The techniques used to couple the LSP and DSSAT

models can he extended to other SVAT-Crop combinations; likewise, the methodology for

developing the optical depth model for sweet corn can he extended to other plant types, as

has been done for cotton during MicroWEX-6 [67].

Other significant contributions are the extensive datasets of soil temperature, soil

moisture, vegetation, surface fluxes, and radiobrightness collected during MicroWEX-2,

4, and 5. They provide season-long and high temporal resolution observations to allow

interdisciplinary studies.

6.3 Recommendations for Future Research

As seen in (I Ilpter 4, the LSP-DSSAT model overestiniates soil moisture, largely

due to uncertainty in soil hydrologic properties and in precipitation. This indicates that

the model estimates would improve with a calibration of the soil hydrologic parameters

in addition to the twelve parameters calibrated in OsI Ilpter 4. In addition, since a in lin t-










EngineeringProceedings of SPIE, the International S8... .:. I r for Op~tical Engineering,
4542.

[13] Dobson, M., F. Ulaby, M. Hallikainen, and M. El-R .v. a~ (1985), Microwave dielectric
behavior of wet soil-part II: Dielectric mixing models, IEEE Trans. Geosci. Remote
Sensing, GE-2S, 35-46.

[14] Doorenbos, J., and W. Pruitt (1977), Guidelines for predicting crop water
requirements, Tech. Rep. Irrigation and drainage paper No. 24, United N li;. a~-
FAO .

[15] Du, Y., F. Ulaby, and M. Dobson (2000), Sensitivity to soil moisture by active and
passive microwave sensors, IEEE Trans. Geosci. Remote Sensing, 88(1), 105-114.

[16] Dukes, M. (2004), Update of the AFSIRS crop water use simulation model.
Micrometeorological dataset, Tech. rep., Available at http://afsirs.ifas.edu.

[17] Dyer, A. J. N. (1974), A review of flux-profile relationships, Bo;,,../.;<;;-Ltr;;. Meteo-
e. J..,t;, 7(S), 363-372.

[18] England, A. (1990), Radiobrightness of diurnally heated, freezing soil, IEEE Trans.
Geosci. Remote Sensing, _'\(4), 464-76.

[19] England, A., and J. Galantowicz (1995), Moisture in a grass canopy from SSM/I
radiobrightness, in Proc. .'st./ Tropical Symp~osium on Combined Op~tical-M~icrowave
Earth and Almos. Sensing, vol. Atlanta, GA, pp. 12-14.

[20] Enthekabi, D., et al. (2004), The Hydrosphere (HYDROS) Satellite Mission: an Earth
system pathfinder for global mapping of soil moisture and land freeze thaw, IEEE
Transactions on Geoscience and Remote Sensing, 42(10), 2184-2195.

[21] Eom, H.J. (1992), A thermal microwave emission model for row-structured vegetation,
Int. J. Remote Sensing, 18(16), 2975-2982.

[22] EOS (2003), Advanced microwave scanning radiometer for cos, overview, sensor, and
orbit, Tech. rep., Available at http: //www.ghcc.msfe.nasa.gov-/AMSR/.

[23] Garcia-Oniii Ilo,, J. F., and A. P. Barros (2005), Incorporating physiology into a
hydrological model: photosynthesis, dynamic respiration, and stomatal sensitivity,
E, JI ~.:y.. l Modeling, 185, 29-49.

[24] Goudriaan, J. (1977), Crop M~icromete. .J,~it~~;, A Simulation Sits.;;, 1st ed., 249 pp.,
Centre for Agricultural Publishing and Documentation, Wageningen, the Netherlands.

[25] Gupta, H. V., L. A. Bastidas, S. Sorooshian, W. J. Shuttleworth, and Z. L. Yang
(1999), Parameter estimation of a land surface scheme using multieriteria methods,
Journal of G II.**ri ..l Research, 1 04(D16), 19,491-19,503.



































I dedicate this to my cats.





























100ft(30 48m)


P00ft(30A~m 1c00ft~


200ft 60.96m)


Direction
ot planning


600 ft(182 B8m)



O ^" oolpnyt(3m 6 0m (LBaan o X Ralnguages

a CR23x, with solm noisture*,spoil tem perature*, and soil heat Ilux g TIR

E CR23x, with EC5
V.S.A
S: CR10. with soil malsture sail temperature*, and soil heat flux

*: Th e sensors are installed at depth: 1. 4. 8, 16. 3". 64. and 120 crn


~ Radiometer


( CNR

Vegetation
: Sam pilng
Area


Figure 2-7. Map of the field site during MicroWEX-5.














E 600 (a)

S400-

200~ **. i
0)rI L;-~i

-200
125 126 127 128 129 130 131 132 133 134 135



300-
(b)
200-


-100 -


~ -300-


125 126 127 128 129 130 131 132 133 134 135
DoY 2004 (EST)

Figure 4-18. Comparison of latent heat flux, between DoY 125 to 135, estimated by the
coupled LSP-DSSAT and stand-alone DSSAT model simulation and those
observed during MicroWEX-2: (a) values and (b) residuals


Similar to the previous stage, net radiation (Figure 4-23) matches very closely with

observations, with RMSDs of ~17 W/m2 and biases of ~2.6 W/m2. The LE and H

comparison could not be presented due to missing observations during this period.

The 2 cm soil heat flux (Figures 4-24) is slightly overestimated with RMSDs of ~55

W/m2 and biases of ~2.3 W/m2, foT Similar reasons as during the non-reproductive

full cover period. The overestimation in soil heat flux leads to overestimation in soil

temperature (Figure 4-26), with RMSD < 3.39 K( and a positive bias < 3.39 K(.

VSM (Figure 4-25) is overestimated with RMSD 0.0632 m3 m3 and a positive bias

0.0623. The overestimation could be due to incorrect precipitation inputs, or accumulated

moisture because of underestimated hydraulic conductivity in the bottom clay 1 in T.

Growing Season Planting to Harvest













m



o




LO C LO
I I I LO
/ ai II II z z
o o < o

~~ g 00




dO I














o oo





b Mb






co





0" c













cbo


o










weights of leaves, stems, and ears were measured. Two LAI measurements were taken

in each sampling area using the Licor LAl-2000 Comp~~li- All lli. r ~. Vertical distribution

of moisture in the canopy was measured five times during MicroWEX-4 and three times

during MicroWEX-5 [7]. During soil sampling, soil moisture and temperatures were

observed in-row and in-furrow at depths of 2, 4, and 8 cm along eight transects at ten

to thirteen locations, using the Delta-T ThetaProbe soil moisture sensor and a digital

thermometer to quantify the spatial variability of the field. Vegetation and soil nitrogen

(as NH,+ and NO,) were measured in each of the four sampling areas. Root length density

was measured in the vadose zone at tasseling.
































Figure 2-1. The University of Florida C-band Microwave Radiometer.


Figure 2-2. The University of Florida L-band Microwave Radiometer.




















model (with ears)
rmodel (without ears)
rson model
10 20 30 40 50 60 70 80 90













10 20 30 40 50 60 70 80 90
Days After Planting



Figure 5-5. Comparison of -r calculated using the 'I;.1.10 i--; I1 model (with and without
the gaussian term) and that using the Jackson model during (a) MicroWEX-4
in 2005, and (b) MicroWEX-5 and 2006.


5.3.2 Canopy Opacity

Figure 5-5 shows the -r estimated using the '.I;nt.1si--;I I1 model and the Jackson model

with b = 0.25. The Jackson model estimates lower opacities throughout the growing

season compared to those obtained using the '.I;nt.1si--;I I1 model with root mean square

differences (RMSD) between the two models of 0.16 Np during MicroWEX-4 and 0.23 Np

during MicroWEX-5. However, the Jackson model matched better when the change in

the moisture distribution due to ear formation was not included, with RMSDs of 0.10 Np

during MicroWEX-4 and 0.11 Np during MicroWEX-5. The contribution of moisture in

the ears to the optical depth is significant because they comprise a significant portion of

the total biomass (see Figure 5-1), with the increase in biomass primarily due to growth










where D T is the difference between the average of the daily average temperatures

during the previous five d .1-< and the yearly average (oC), ZD is depth (cm), T4M/P is

amplitude of yearly temperature (oC), and ALX is the difference in d .1-< from the current

d~i- to the hottest do-- of the year.

3.3 Model Calibration

The CERES-Maize model was ported to the Linux OS and calibrated using data from

MicroWEX-2. This section describes the calibration procedure.

3.3.1 Initialization

CERES is the crop submodule for cereal crops, including maize. CERES-Maize uses

three files for determining growth and development characteristics: the species file, the

ecotype file, and the cultivar file. The species file contains defining characteristics of

corn, including root growth parameters, seed initial conditions, nitrogen and water stress

response coefficients, nitrogen uptake parameters, base and optimum temperatures for

grain fill and photosynthesis, and radiation and CO2 parameterS governing photosynthesis.

The ecotype file specifies thermal time development, radiation use efficiency, and light

extinction coefficients for three main types of corn. The cultivar file specifies the cultivar

coefficients that describe the growth and development characteristics for different maize

cultivars. These are:

Pl: degree d .1-< between emergence and end of juvenile stage.

P2: development dl li- for each hour increase in photoperiod past optimum

photoperiod.

P5: degree d .1-< from silking to maturity.

G2: maximum possible number of kernels per plant.

G3: kernel filling rate during the linear grain filling stage and under optimum

conditions (mg/d~is).

PHINT: phyllochron interval, i.e., the interval in thermal time (degree d .1-<) between

leaf tip appearances.











dW,
r=P fy D Eev (4-33)

4.2.2 Soil Processes

Heat and moisture transport in the soil is determined as the numerical solution to

[47]:
i80
= -Vqm (4-34)
iBT
C,, -Vqh (4-35)

4m = 97 + q, (4-36)

qi = -Do~lV8 DT,IVT + K + S (4-37)

q, = -D~,V8 DT,,VT (4-38)

qh = -KVT + p~q, + CV,,(T To)qm (4-39)

where gi, q,, and ga are liquid, vapor, and heat fluxes, respectively; T and 8 are temperature

and volumetric soil moisture, respectively. Do,l is the diffusivity of liquid under a moisture

gradient; DT,I is the diffusivity of liquid under a temperature gradient; D~,v is the

diffusivity of vapor under a moisture gradient; DT,v is the diffusivity of vapor under a

temperature gradient, from [47]; K is hydraulic conductivity, from [49]; a is thermal

conductivity of soil from [11], S is a sink term (root water uptake), and C,~, is the

volumetric heat capacity of soil. C,~,, p, and A are the heat capacity, density, and heat of

vaporization of water.

The soil profile is defined with lI- rs of different constitutive properties, divided into

computational blocks, with the thickness of blocks increasing exponentially with depth.

The coupled heat and moisture transport equations are solved using a block-centered,

foward-time finite difference scheme. The upper boundary condition is a heat and moisture

flux determined by the meteorological forcing, while the lower boundary condition

assumes free flow of heat and moisture.




Full Text

PAGE 1

1

PAGE 2

2

PAGE 3

3

PAGE 4

ThisresearchwassupportedbytheNSFEarthScienceDirectorate(EAR-0337277)andtheNASANewInvestigatorProgram(NASA-NIP-00050655).IwouldliketothankMr.OrlandoLanniandMr.LarryMillerforprovidingengineeringsupportduringtheMicroWEXsandpatientlytoleratingmyidiocy;Mr.JimBoyerandhisteamatPSREUforlandandcropmanagement;Dr.RogerDeRooattheUniversityofMichiganforradiometersandtechsupport;Mr.Kai-JenTien,Mr.Tzu-YunLin,Ms.Mi-YoungJang,andMr.FeiYanfortheirhelpindatacollectionduringtheMicroWEXs;andtotheUniversityofFloridaHigh-PerformanceComputingCenterforprovidingcomputationalresourcesandsupportthathavecontributedtotheresearchresultsreportedwithinthisthesis. 4

PAGE 5

page ACKNOWLEDGMENTS ................................. 4 LISTOFTABLES ..................................... 7 LISTOFFIGURES .................................... 8 ABSTRACT ........................................ 12 CHAPTER 1INTRODUCTION .................................. 13 1.1ThesisObjectives ................................ 17 1.2ThesisFormat .................................. 17 2MICROWAVEWATERANDENERGYBALANCEEXPERIMENTS ..... 18 3CALIBRATIONOFACROPGROWTHMODELFORSWEETCORN .... 25 3.1Introduction ................................... 25 3.2CERES-MaizeModel .............................. 25 3.3ModelCalibration ................................ 27 3.3.1Initialization ............................... 27 3.3.2Inputs .................................. 28 3.3.3Methodology ............................... 28 3.4ResultsandDiscussion ............................. 29 3.4.1CropGrowthandDevelopment .................... 29 3.4.2Evapotranspiration ........................... 32 3.4.3SoilMoistureandTemperature .................... 34 3.5Summary .................................... 37 4CALIBRATIONOFANSVATMODELANDCOUPLINGWITHACROPMODELFORSWEETCORN ........................... 39 4.1Introduction ................................... 39 4.2LSPModel ................................... 39 4.2.1EnergyandMoistureTransportattheLandSurface ......... 40 4.2.1.1EnergyBalance ........................ 40 4.2.1.2MoistureBalance ....................... 45 4.2.2SoilProcesses .............................. 46 4.3CouplingofLSPandDSSATmodels ..................... 47 4.4Methodology .................................. 48 4.4.1InputsandInitialConditions ...................... 48 4.4.2Calibration ................................ 49 4.5ResultsandDiscussion ............................. 51 5

PAGE 6

................................ 51 4.5.1.1DSSAT ............................ 51 4.5.1.2LSP .............................. 51 4.5.2ModelSimulation ............................ 52 4.5.2.1DSSAT ............................ 52 4.5.2.2LSP-DSSATModel ...................... 53 4.6Conclusion .................................... 75 5CANOPYMICROWAVEMODEL ......................... 82 5.1Introduction ................................... 82 5.2Methodology .................................. 82 5.2.1MoistureDistributionMeasurements ................. 82 5.2.2CanopyOpacity ............................. 83 5.2.3MicrowaveBrightnessModel ...................... 85 5.2.4ModelComparisonandEvaluation .................. 87 5.3ResultsandDiscussion ............................. 88 5.3.1MoistureDistributionFunction .................... 88 5.3.2CanopyOpacity ............................. 89 5.3.3MicrowaveBrightness .......................... 90 5.4Summary .................................... 94 6CONCLUSION .................................... 96 6.1Summary .................................... 96 6.2Contributions .................................. 97 6.3RecommendationsforFutureResearch .................... 97 REFERENCES ....................................... 99 BIOGRAPHICALSKETCH ................................ 105 6

PAGE 7

Table page 3-1CultivarcoecientvaluesinthecalibratedCERES-Maizemodel. ........ 29 3-2ErrorstatisticsforcropgrowthandETbetweenCERES-MaizeestimatesandMicroWEX-2eldobservations. ........................... 32 3-3modelperformancestatisticsforsoilmoistureandtemperaturebetweenCERES-MaizeestimatesandMicroWEX-2eldobservations. ................... 37 4-1ValuesforsoilpropertiesintheLSPmodel. .................... 49 4-2Samplingrangesfrom[ 24 ]andcalibratedvaluesforparametersintheLSPmodel. 50 4-3ComparisonofLAI,drybiomass(kg/m2),andET(mm)forstand-aloneDSSATandcoupledLSP-DSSATsimulations. ........................ 53 4-4Comparisonofsurfaceuxes(W/m2),forstand-aloneLSPandcoupledLSP-DSSATsimulations. ...................................... 55 4-5Comparisonofvolumetricsoilmoisture(m3/m3),forstand-aloneLSPandcoupledLSP-DSSATsimulations. ............................... 55 4-6Comparisonofsoiltemperature(K),forstand-aloneLSPandcoupledLSP-DSSATsimulations. ...................................... 55 4-7MeasurementuncertaintitiesduringMicroWEX-2. ................. 56 5-1ValuesoftheCoecientsinequations 5{6 and 5{7 ................ 88 5-2RMSdierencesbetweenobservedTBduringMicroWEX-5andthoseestimatedbytheMBmodel ................................... 91 5-3RMSdierencesbetweenobservedH-polTBduringMicroWEX-2andthoseestimatedbytheMBmodel. .................................. 94 7

PAGE 8

Figure page 1-1Outlineofthedataassimilationschemeandtheforwardmodel. ......... 15 1-2ContributionstomicrowavebrightnessTBfromsky,soil,andcanopy. ...... 15 2-1TheUniversityofFloridaC-bandMicrowaveRadiometer. ............ 19 2-2TheUniversityofFloridaL-bandMicrowaveRadiometer. ............. 19 2-3TheEddyCovarianceSystem. ............................ 20 2-4ThenetradiometerusedduringtheMicroWEXs. ................. 20 2-5MapoftheeldsiteduringMicroWEX-2. ..................... 22 2-6MapoftheeldsiteduringMicroWEX-4. ..................... 23 2-7MapoftheeldsiteduringMicroWEX-5. ..................... 24 3-1(a)ComparisonoftheCERES-MaizeestimatesandtheobservationsofbiomassduringMicroWEX-2,(b)scatterplotofestimatedandobservedbiomass,(c)comparisonoftheCERES-MaizeestimatesandtheobservationsofLAIduringMicroWEX-2,and(d)scatterplotofestimatedandobservedLAI. ........ 31 3-2ComparisonofthelatentheatuxestimatesfromCERES-MaizemodelusingfourmethodswiththeobservationsduringMicroWEX-2by(a)dailyheatuxand(b)cumulativeET. ............................... 33 3-3ComparisonoftheCERES-MaizesoilmoistureestimateswithMicroWEX-2observationsatdepthsof(a)0-5cm,(b)5-15cm,(c)15-30cm,(d)30-45cm,(e)45-60cm,and(f)60-90cm. ........................... 35 3-4ComparisonoftheCERES-MaizesoiltemperatureestimateswithMicroWEX-2observationsatdepthsof(a)0-5cm,(b)5-15cm,(c)15-30cm,(d)30-45cm,(e)45-60cm,and(f)60-90cm. ........................... 36 4-1SurfaceresistancenetworktoestimatesensibleandlatentheatuxesintheLSPmodel. ......................................... 42 4-2AlgorithmforthecouplingoftheLSPandDSSATmodels. ............ 47 4-3Paretofrontsfromcalibrationofthestand-aloneLSPmodel.TheasteriskrepresentsthepointontheParetofrontwherethetotalseasonalRMSDfor2cmVSMis0.04m3/m3. ...................................... 51 4-4ComparisonofestimationsbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)drybiomass,(b)LAI,(c)5cmsoilmoisture,and(d)ET. ...................... 54 8

PAGE 9

..................... 56 4-6Comparisonoflatentheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 57 4-7Comparisonofsensibleheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 58 4-8Comparisonofsoilheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 59 4-9ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY78to105:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. .................................... 60 4-10ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY78to105:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 61 4-11Comparisonofnetradiation,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 62 4-12Comparisonoflatentheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 63 4-13Comparisonofsensibleheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ................. 64 4-14Comparisonofsoilheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 65 4-15ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY105to125:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. .................................... 66 9

PAGE 10

67 4-17Comparisonofnetradiation,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 68 4-18Comparisonoflatentheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 69 4-19Comparisonofsensibleheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ................. 70 4-20Comparisonofsoilheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 71 4-21ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY125to135:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. .................................... 72 4-22ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY125to135:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 73 4-23Comparisonofnetradiation,betweenDoY135to154,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 74 4-24Comparisonofsoilheatux,betweenDoY135to154,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ..................... 75 4-25ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY135to154:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. .................................... 76 4-26ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY135to154:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 77 10

PAGE 11

........ 78 4-28ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. ....... 79 4-29ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. ................ 80 5-1Observationsoftotalandearwetbiomassduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. .............................. 83 5-2Observationsofcanopyheightduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. ........................................ 84 5-3Clouddensitiesmeasuredduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006.Thesymbolsandthelinesrepresentthemeasurementsandthebestcurve-ts,respectively. ................................ 85 5-4Moisturemixingratiosmeasuredduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. ........................................ 86 5-5Comparisonofcalculatedusingthebiophysicalmodel(withandwithoutthegaussianterm)andthatusingtheJacksonmodelduring(a)MicroWEX-4in2005,and(b)MicroWEX-5and2006. ...................... 89 5-6ComparisonoftheobservedTBatH-polduringMW5thosesimulatedbytheMBmodelusingfromthebiophysicalmodelandfromtheJacksonmodelduringlate-seasonMicroWEX-5. .............................. 90 5-7Comparisonofmicrowavebrightness,estimatedbytheLSP-DSSAT-MBmodelwithspecularsurface(a)andWegmullerandMatzler(b),andC-bandmicrowavebrightnessobservedduringMicroWEX-2,beforeDoY125. ............ 92 5-8Comparisonofmicrowavebrightness,estimatedbytheLSP-DSSAT-MBmodelwithspecularsurface(a)andWegmullerandMatzler(b),andC-bandmicrowavebrightnessobservedduringMicroWEX-2,afterDoY125. ............. 93 11

PAGE 12

12

PAGE 13

10 ],themodeldevelopedbytheNationalCentersforEnvironmentalPredictionatOregonStateUniversity,AirForce,andHydrologicResearchLaboratoryattheNationalWeatherService(NOAH)[ 46 ],andtheUniversityofMichiganMicrowaveGeophysicsGroupLandSurfaceProcess(LSP)model[ 38 ].However,twomainchallengesremaininmodelingenergyandmoistureuxesusingSVATmodels.First,mostmodelsoftenoversimplifythecouplingbetweenvegetationgrowthandsurfaceuxes.Theinteractionsbetweenvegetationandtheuxesbecomeincreasinglyimportantastheseuxesaectplantgrowthanddevelopment.Vegetationcanopiesimpactlatentandsensibleheatuxes,precipitationinterception,andradiativetransferattheland-atmosphereinterface,aectingsoilmoistureandtemperatureprolesinthevadosezone.ThesechanginginteractionsduringthegrowingseasonneedtobeincludedintheSVATmodels,inordertoproviderealisticestimatesoftheuxes.Typically,SVATmodelsemployobservationsorempiricalfunctionsforvegetationconditionstomodeltheeectsofgrowingvegetation.Forexample,CLMusesvegetatedgridspacesdenedbypatchesof\plantfunctionaltypes,"withparametersforphysiologicalandstructuralpropertiesassociatedwitheachtype,andmostofthevegetationparametersareempiricaltomeetcomputationalconstraints[ 10 ].NOAHsimulatessoilmoistureandtemperatureproleswithasub-dailytimestep,andwithvegetationpropertiessuchasLAI,stomatalresistance,androughnesslengthdenedbyvegetationtypeclasses[ 46 ].Suchmethods 13

PAGE 14

23 ]usedasub-dailybiochemicalvegetationmodelwithalandsurfacehydrologymodel.Theymodeledcanopytranspirationanditsinuenceonsoilmoistureandcarbonuxes.[ 41 ]linkeddailyprocess-basedcropmodelsforsummermaizeandwinterwheatwithanhourlylandsurfaceuxmodelandathree-layersoilmoisturemodel.Suchcouplingallowsforinclusionofvegetationeectswithoutinsituobservationsorempiricalgrowthfunctions.Periodicinsituobservationsofvegetationcouldbeincorporatedinthecoupledmodelstoreducethedivergenceofmodelpredictionfromreality.Remotely-sensedobservationssensitivetosoilmoisture,suchaslowfrequency(<10GHz)microwavebrightness(TB)[ 15 26 43 52 ]couldalsobeincorporatedperiodicallytoimprovemodeluxestimates.Toincorporateorassimilatemicrowavebrightness,thecoupledSVAT-cropmodelhastobelinkedtoamicrowaveemissionmodelthatestimatesmicrowavebrightnessusingmoistureandtemperatureprolesinsoilandvegetationestimatedbytheSVAT-CropmodelasshowninFigure 1-1 .SimpleversionsofSVATmodelslinkedwithMBmodelsincludetheLandSurfaceProcess/Radiobrightness(LSP/R)[ 30 ]andSimpleSoil-Plant-AtmosphereTransfer-RemoteSensing(SiSPAT-RS)[ 12 ]models.ThetotalTBofaterrainisdependentonskyTB,reectedbythesoil(TB;sky),thermalemissionfromthesoil(TB;soil,andthermalemissionfromthevegetationcanopy(TB;canopy,allthreecomponentsareshowninFigure 1-2 ).Sincesoilmicrowaveemissions(dependentonsoilmoistureandtemperatureproles)areattenuatedbytransmission 14

PAGE 15

Outlineofthedataassimilationschemeandtheforwardmodel. Figure1-2. ContributionstomicrowavebrightnessTBfromsky,soil,andcanopy. 15

PAGE 16

58 ]modeledofthewheatcanopyasauniformcloudofwetbiomasswithleavesandstemstreatedseparately.Inaddition,polarizationdependencewasincludedforstemattenuation.Eom[ 21 ]developedamodelforapplicabletorowstructuredcanopiessuchwheatorcorn.Themodelaccountsforazimuthalanisotropyinbymodelingthecanopyasarandomcollectionofdielectricspheroids.Thismethodmatchedwellwithobservationsbutrequiresacomputationallyintensivesolutionoftheradiativetransferequation.JacksonandSchmugge[ 51 ],usedtheresultsofmanystudiesanddevelopedanempiricalmodelfor.Intheirmodel,isestimatedastheproductofafrequency-dependentconstantbandwatercolumndensity(kg/m2)inthecanopy.TheJacksonmodelisexiblebuthaslittlephysicalbasis,withboftenusedasattingparameterinemissionmodelsorestimatedempirically[ 61 ].EnglandandGalantowicz[ 19 ]developedarefractivemodelforestimatingopticaldepthofgrassbaseduponverticalprolesofmoisturecontentwithinthegrasscanopy.Inthisthesis,anSVATmodel,viz.theLSPmodel,iscoupledwithawidely-usedandwell-testedcropgrowthmodel,theDecisionSupportSystemforAgrotechnologyTransferCroppingSystemModel(DSSAT-CSM)[ 29 ].ThemodelsarecalibratedusingobserationsfromtheMicrowaveWaterandEnergyBalanceExperiment2(MicroWEX-2),oneofthreeseason-longexperimentsmonitoringgrowingsweetcorn(MicroWEXs2,4,and5).Abiophysically-basedcanopytransmissionmodelisdevelopedforgrowingsweetcorn,usingdatafromMicroWEXs4and5.ThismodelisincludedinasimpleMBmodelthatislinkedwiththeLSP-DSSATmodel. 16

PAGE 17

1. WhatvaluesforthesixcorncultivarcoecientsgivethebestDSSATmodelperformanceforbothbiomassandLAIfortheMicroWEX-2growingseason?(Chapter 3 ) 2. HowdothemodelestimatesforbiomassandLAIcomparewithMicroWEX-2observations?(Chapter 3 ) 3. WhatvaluesofthetwelvecalibratedparametersgivethebestLSPmodelperformanceforbothlatentheatuxandnearsurfacesoilmoisturefortheMicroWEX-2growingseason?(Chapter 4 ) 4. Howdothemodelestimatesofsoilmoisture,temperature,andsurfaceuxescomparewithMicroWEX-2observations?(Chapter 4 ) 5. WhatistheimpactofcouplingonbothLSPandDSSATmodelestimatesofLAI,biomass,soilmoisture,temperature,andsurfaceuxes?(Chapter 4 ) 6. Howdoesaphysically-basedmodelcomparetoJackson'swidely-usedempiricalmodel?(Chapter 5 ) 7. HowdothebrightnessestimatespredictedbythelinkedLSP-DSSAT-MBmodelcomparetoobservationsduringMicroWEX-2?(Chapter 5 ) 2 ofthisthesisdescribestheeldexperiments,MicroWEXs2,4,and5.InChapter 3 ,theDSSATmodel'scornsubmodel,CERES-Maize,iscalibratedfortheMicroWEX-2growingseason.InChapter 4 ,theLSPmodeliscalibratedandcoupledwithDSSATmodel.InChapter 5 ,acanopytransmissionmodelforgrowingsweetcornisdevelopedandtestedinasimpledMBmodel,linkedwiththeLSP-DSSATmodel. 17

PAGE 18

6 8 33 37 55 67 ].Theobjectiveoftheexperimentsaretounderstandmicrowavesignaturesofagriculturalcropsduringdierentstagesofgrowth.MicroWEX-2wasconductedduringthesweetcorngrowingseason,fromMarch18throughJune2in2004[ 33 ].MicroWEX-4wasconductedduringthesweetcorngrowingseason,fromMarch10throughJune2in2005[ 6 ].MicroWEX-5wasconductedduringthesubsequentcornseasonfromMarch9throughMay26in2006[ 8 ].Allexperimentswereconductedatthesame37,000m2siteinUF/IFASPlantScienceResearchandEducationUnitinCitra,FL(29.41N,82.18W).ThesoilsatthesiteareLakeFineSandwithabout90%sandandabulkdensityof1.55g/cm3.Rowspacingwas76cm,withapproximatelyeightplantspersquaremeter.Irrigationandfertigationwereconductedviaalinearmovesystem.DatacollectedduringtheMicroWEXsincludedsoilmoisture,temperatureandheatux,latentandsensibleheatux,windspeedanddirection,upwellinganddownwellingshortandlongwaveradiation,precipitation,irrigation,watertabledepth,andverticallyandhorizontallypolarizedmicrowavebrightnessat6.7GHz(=4.47cm),everyfteenminutesusingthetower-mountedUniversityofFloridaC-bandMicrowaveRadiometer(UFCMR,Figure 2-1 ).Additionalhorizontallypolarizedmicrowavebrightnessobservationsat1.4GHz(=21.4cm)wereconductedduringMicroWEX-5usingtheUFL-BandMicrowaveRadiometer(UFLMR,Figure 2-2 ).Theradiometerfrequencies,at6.7GHzandat1.4GHz,correspondtothelowestfrequencyoftheAdvancedScanningMicrowaveRadiometer(AMSR-E)[ 22 ],andthefrequencyoftheplannedSoilMoistureandOceanSalinity(SMOS)mission[ 34 ],respectively.Thesoilmoisture,heatuxes,andtemperatureswereobservedatthreelocationsintheeld.Soilmoistureandsoiltemperaturewereobservedat2,4,8,16,32,64, 18

PAGE 19

TheUniversityofFloridaC-bandMicrowaveRadiometer. Figure2-2. TheUniversityofFloridaL-bandMicrowaveRadiometer. 19

PAGE 20

TheEddyCovarianceSystem. Figure2-4. ThenetradiometerusedduringtheMicroWEXs. and120cm(100cmduringMicroWEX-2)usingCampbellScienticWaterContentReectometersandVitelHydra-probes;andthermistorsandthermocouples,respectively.AnEddyCovarianceSystem(Figure 2-3 )measuredwindspeed,direction,andlatentandsensibleheatuxes.REBSCNRnetradiometer(Figure 2-4 )measuredup-anddown-wellingshort-andlong-waveradiation.EverestInterscienceinfraredsensormeasuredthermalinfraredtemperature.Fourtipping-bucketraingaugesloggedprecipitationatfourlocationsEastandWestofthefootprint,andattheEastandWestsidesoftheeld.WatertabledepthwasmeasuredusingSolinstLevelLoggersinamonitoringwellineachquadrant.Inadditiontocontinuouslyloggeddata,therewerealsoweeklyvegetationandtwice-weeklysoilsamplings(duringMicroWEX-2only).Vegetationsamplingwasconductedinfourareas,oneineachquadrantoftheeld.Sampleswereselectedbyplacingameterstickhalf-waybetweentwoplantsandendingthesampleatleast1mfromthestartingpointandhalf-waybetweentwoplants.Theactualrowlengthofthesamplewasnoted.Standdensity,leafnumber,canopyheightandwidth,wetanddry 20

PAGE 21

7 ].Duringsoilsampling,soilmoistureandtemperatureswereobservedin-rowandin-furrowatdepthsof2,4,and8cmalongeighttransectsattentothirteenlocations,usingtheDelta-TThetaProbesoilmoisturesensorandadigitalthermometertoquantifythespatialvariabilityoftheeld.Vegetationandsoilnitrogen(asNH+4andNO3)weremeasuredineachofthefoursamplingareas.Rootlengthdensitywasmeasuredinthevadosezoneattasseling. 21

PAGE 22

MapoftheeldsiteduringMicroWEX-2. 22

PAGE 23

MapoftheeldsiteduringMicroWEX-4. 23

PAGE 24

MapoftheeldsiteduringMicroWEX-5. 24

PAGE 25

65 ]andCERES-Maize[ 28 ],thatsimulatehydrology,nutrientcycling,growth,anddevelopment.CERES-Maizehastheadvantageofbeingpartofthewell-knownDecisionSupportSystemforAgrotechnologyTransferCroppingSystemModel(DSSAT-CSM).DSSAThasbeenwidelyusedforanumberofyears,withvalidatedmodelsforover15crops.Italsoallowsforsimulationsofmulti-yearcroprotations[ 29 ]. 29 ].Themodeldeterminestotaldrybiomassusingtheradiationuseeciencymethod.Totalsolarradiationispartitionedintophotosyntheticallyactiveradiation(PAR),andthefractioninterceptediscalculatedfromLAIusingBeer'slaw[ 54 ].Thedrymatteraccumulationrateisaproductofradiationuseeciencyandaconversionfactor.Maizegrowthanddevelopmentismarkedbyeightevents:germination,emergence,endofjuvenilephase,oralinduction(tasselinitiation),75%silking,beginninggrainll,maturity,andharvest.Transitionfromonedevelopmentalstagetothenextisdeterminedbythegrowingdegreedays(GDD)withabasetemperatureof8C.Vegetativegrowthstopson75%silking,whenreproductivegrowthbeginsintheformofgrainll.Yieldisthegrainllvalueatharvest.ThresholdGDDforeachstageandgrainllparametersarecontainedinacultivarle. 25

PAGE 26

48 ]orthePenman-FAO(PFAO)method[ 14 ].TheRPTmethoddependsonlyonsolarradiationandtemperature,whilethePFAOmethodaccountsforwindspeedandrelativehumidityaswell.BothmethodsrstdetermineatotalpotentialET,whichispartitionedintopotentialsoilevaporationandpotentialplanttranspiration.Potentialsoilevaporationisbasedoninterceptedsolarradiationreachingthesoilsurfaceasafunctionoftemperature,windspeed,radiation,andhumidity.Potentialplanttranspirationdependsontheradiationinterceptedbythecanopyandtemperature,windspeed,andhumidity.ActualevaporationandtranspirationaredeterminedbytheminimumofpotentialETandtheamountofavailablewater.Forsoilevaporation,surfacesoilwateristhelimitingfactor,whilefortranspiration,rootwateruptakeisthelimitingfactor.Thesoilisdividedintoninelayers,eachwithdierentconstitutiveproperties.Soilmoistureiscalculatedusingthebucketmethod[ 39 ].Whenanuppersoillayerisabovethedrainedupperlimit,excessowstotheonebelow,inadditiontocomputingestimatesforcapillaryrise.RunoiscalculatedusingtheUSDASoilConservationServicerunonumbermethod[ 53 ].Inltrationisequaltoexcessprecipitationafterruno.Soiltemperatureiscomputedusingadeepsoilboundaryconditionandanairtemperatureboundarycondition.Theairtemperature(C)iscalculatedfromtheaverageofmaximumandminimumdailytemperatures.Soiltemperature(ST)varieswithsoillayer(L)as[ 29 ]: 26

PAGE 27

27

PAGE 28

16 ]. 3-1 a).Theobjectivefunction(R)wascomputedasthesumofsquareresiduals,normalizedbyvariance[ 54 ]: 28

PAGE 29

CultivarcoecientvaluesinthecalibratedCERES-Maizemodel.CultivarCoecient Value P1 157.20P2 1.000P5 811.20G1 853.00G3 10.4PHINT 40.33 observations,respectively.Theoptimumcombinationofparametervaluesfoundbythegridsearchwasthenusedastheinitialguessinasimulatedannealingoptimizationalgorithm[ 2 ].Therootmeansquaredierence(RMSD),relativerootmeansquaredierence(RRMSD),andWillmottd-index[ 66 ]werecalculatedasforLAIandthebiomassofeachcomponent,leaves,stems,andgrain: 3-1 showsthevaluesofthesixcultivarcoecientsthatminimizedRinEquation 3{2 3.4.1CropGrowthandDevelopmentToevaluatetheCERES-Maizemodelforcropgrowthanddevelopment,modelestimatesarecomparedofemergenceandsilkingdates,biomass,andLAItotheobservationsduringMicroWEX-2. 29

PAGE 30

3-1 .TheRMSDforbiomasswas0.90Mg/hawithalowRRMSDof0.23andacorrespondinglyhighWillmottd-indexof0.99,asshowninTable 3-2 .Figure 3-1 bshowsascatterplotofestimatedandobservedtotalbiomass.Thebiomasswasincreasinglyunderestimatedbythemodelastheseasonprogressed,withthemaximumdierenceof1.41Mg/haattheendoftheseason.Thepartitioningofthemodeledbiomassintoleafandstembiomassdidnotmatchtheobservations(Figure 3-1 a),asindicatedbythehighRMSDandRRMSDinTable 3-2 .Partitioningoftotalbiomassintostembiomasswasunderestimatedbythemodelduringlatervegetativestagesofgrowth(DoY127toDoY134).Thepartitioningintoleafbiomasswasmorerealistic,withaslightoverestimationduringlatergrowthstages(DoY132toDoY142).Themodel'sestimateofthebeginningofgrainllatDoY140matchedcloselywiththeobservedgrainllatDoY139(Figure 3-1 a).ThebesttforLAIandtotalbiomassdidnotproducethebesttforgrainll.Inordertocompensatefortheunderestimatedstembiomass,grainweightmustbeoverestimated.ThemodelestimatedrealisticLAI,asseeninFigure 3-1 ,withalowRMSDandRRMSDof0.22and0.13,respectively,andahighWillmottd-indexof0.99,asshowninTable 3-2 .Figure 3-1 dshowsthescatterplotofthemodelandobservedLAI. 30

PAGE 31

(a)ComparisonoftheCERES-MaizeestimatesandtheobservationsofbiomassduringMicroWEX-2,(b)scatterplotofestimatedandobservedbiomass,(c)comparisonoftheCERES-MaizeestimatesandtheobservationsofLAIduringMicroWEX-2,and(d)scatterplotofestimatedandobservedLAI.

PAGE 32

ErrorstatisticsforcropgrowthandETbetweenCERES-MaizeestimatesandMicroWEX-2eldobservations.Parameter RMSD RRMSD Willmottd 0.91 0.23 0.99Stembiomass(Mg/ha) 0.97 0.52 0.90Leafbiomass(Mg/ha) 0.47 0.44 0.93Grainbiomass(Mg/ha) 0.49 1.17 0.96LAI 0.22 0.13 0.99Latentheatux(W/m2) 42.07 0.39 0.87 50 ]usingtwomethods(RPTandPFAO)toestimateETandtwovalues(0.85and0.5)forthecanopylightextinctioncoecient(KCAN).Figure 3-2 showsacomparisonofthelatentheatuxestimatesusingthefourmethods.EventhoughtheRMSDvalueswerelow(40W/m2),thetemporaldistributionoflatentheatuxeswasnotestimatedrealisticallyduringthegrowingseason(Figure 3-2 a).Thelatentheatuxeswereunderestimatedintheearlyseasonandoverestimated(100W/m2)duringlateseason.Theearlyseasonunderestimationindicateslowevaporationratesfromthemodeledsoil,andthelateseasonoverestimationindicateshighertranspirationratesinthemodeledvegetation.TheuxestimateswerenotassensitivetoKCANvaluesasthepreviousstudieshadfoundunderwater-stressedconditions[ 50 ].IntermsofcumulativeET,individualunder-oroverestimationsbythemodeleectivelycanceleachother,sothatthetforcumulativeETisbetterthanfordailyvalues(Figure 3-2 b). 32

PAGE 33

ComparisonofthelatentheatuxestimatesfromCERES-MaizemodelusingfourmethodswiththeobservationsduringMicroWEX-2by(a)dailyheatuxand(b)cumulativeET.

PAGE 34

3-3 3-4 ,and 3-3 ).Tocompareobservationsat2,4,8,16,32,64,and100cmtomodelestimatesofthetopsixlayers,theaverageof2and4cmobservationsarecomparedtoestimatesof0-5cm,8and16cmobservationstoestimatesof5-15cm,16and32cmobservationstoestimatesof15-30cm,averageof32cmobservationstoestimatesof30-45cm,32and64cmobservationstoestimatesof45-60cm,and64and100cmobservationstoestimatesof60-90cm. 34

PAGE 35

ComparisonoftheCERES-MaizesoilmoistureestimateswithMicroWEX-2observationsatdepthsof(a)0-5cm,(b)5-15cm,(c)15-30cm,(d)30-45cm,(e)45-60cm,and(f)60-90cm.

PAGE 36

ComparisonoftheCERES-MaizesoiltemperatureestimateswithMicroWEX-2observationsatdepthsof(a)0-5cm,(b)5-15cm,(c)15-30cm,(d)30-45cm,(e)45-60cm,and(f)60-90cm.

PAGE 37

modelperformancestatisticsforsoilmoistureandtemperaturebetweenCERES-MaizeestimatesandMicroWEX-2eldobservations.RMSD Layer SoilMoisture SoilTemperature(K) 5-15cm 0.0204 2.53415-30cm 0.0344 1.42630-45cm 0.0164 1.48545-60cm 0.0117 2.77560-90cm 0.0083 3.648 TheCERES-Maizemodelsimulatesmoistureatdailytimesteps,whilethehydrologicalchangesnearthesoilsurface(0-5cm)occuratmuchshortertimesteps,makingitchallengingtocomparemodelandobservednear-surfacesoilmoisture.InFigure 3-3 a,thedailymoistureat0-5cmestimatedbytheCERES-Maizemodeliscomparedwithdailyaveragesand15minobservationsofvolumetricsoilmoisture(VSM)duringMicroWEX-2.Deepersoillayersmatchedtheobservedvaluesfairlywell,assuggestedbytheirlowRMSDvaluesinTable 3-3 ,exceptfora2%underestimationduringtheentiregrowingseasonforthe15-30cmlayer.ThisiswithintheexperimentalerroroftheobservationsmadebytheTDRprobes.Overall,themodeldidnotcapturethechangesinsoiltemperaturesrealisticallyduringthegrowingseason.Itestimatedtemperaturesatdepthsof15-45cmfairlywell,asindicatedbytheirlowRMSDvaluesinTable 3-3 .Thetemperaturesatdeeperlayerswereunderestimatedthroughoutthegrowingseason,withincreasingdierencesastheseasonprogressed.Fortheupperlayers,themodeldidnotcapturethestronguctuationsintemperatureclosertothesurface. 1 .Question1:"WhatvaluesforthesixcorncultivarcoecientsgivethebestDSSATmodelperformanceforbothbiomassandLAIfortheMicroWEX-2growingseason?"

PAGE 38

3-1 .Question2:"HowdothemodelestimatesforbiomassandLAIcomparewithMicroWEX-2observations?"TheRMSDforbiomasswas0.90Mg/ha.Thebiomasswasincreasinglyunderestimatedbythemodelastheseasonprogressed,withthemaximumdierenceof1.41Mg/haattheendoftheseason.ThemodelestimatedrealisticLAIwithalowRMSDof0.22. 38

PAGE 39

38 ].Themodelsimulates1-dcoupledenergyandmoisturetransportinsoilandvegetation,andestimatesenergyandmoistureuxesatthelandsurfaceandinthevadosezone.Itisforcedwithmicrometeorologicalparameterssuchasairtemperature,relativehumidity,downwellingsolarandlongwaveradiation,irrigation/precipitation,andwindspeed.Theoriginalversionhasbeenrigorouslytested[ 31 ]andextendedtowheatstubble[ 30 ]andbrome-grass[ 32 ],prairiewetlandsinFlorida[ 64 ],andtundraintheArctic[ 9 ]. 39

PAGE 40

4 ].TheoriginalversionoftheLSPmodelfollowedamoreempirically-basedformulationbyVerseghyetal.[ 62 ].Inaddition,theaerodymanicresistancesandthesurfacevaporresistanceswerechangedinthenewversiontoextendittotallvegetationandtopartially-vegetatedterrain[ 24 ].Theoriginalversionwasdevelopedforhomogeneouslandcover,suchasbaresoilorshortgrass.Thenewversionofthemodelalsoincludesadaptivetimestepsforcomputationaleciencyandtoallowsuddenchangesorlargeuxesinthesandysoilswithhighthermalandhydraulicconductivities.ThefollowingsectionprovidesadetaileddescriptionofthemodiedLSPmodelusedinthisstudy.Somefundamentalgoverningequationsarealsoincludedinthesectionforcompletenesseventhoughtheyremainunchangedfromtheoriginalversion. 4.2.1.1EnergyBalanceCombiningtheradiationandheatuxboundaryconditions,thenetenergyuxintothecanopy(Qnet;c)andsoil(Qnet;s)(W/m2):

PAGE 41

1 ].Thedirectfractioniseithertransmitted,reected,orabsorbed.Thenetsolarradiationabsorbedbythecanopyandsoilare 4 ]: 1+K(x;)1p 24 ]: 41

PAGE 42

SurfaceresistancenetworktoestimatesensibleandlatentheatuxesintheLSPmodel. bare-soildata: 35 ]: 4-1 (a)showstheresistancenetworkmodelusedtoestimatesensibleheatux(H)atthesurface.Thesensibleheatuxesbetweenthesoilandair(Hsa),soilandcanopy 42

PAGE 43

24 ]: zob+H zov+H zo+M(4{16)whereuisthefrictionvelocity,istheBusinger-Dyerstabilityfunction[ 17 ],kisvonKarman'sconstant(0.4),zisthemeasurementheight,disthevegetationdisplacementheight(takenas0:63hc,hcistheplantcanopyheight),zovisthevegetationroughnesslength(0:1hc),andzobisthebaresoilroughnesslength.Fortheaerodynamicresistancebetweenthesoilandthecanopy,thelogproleisnotvalidduetomomentumabsorptionbythecanopyelements,soanexponentialwindproleinthecanopyisused[ 24 ],withtheunder-canopyresistance,rsc,fromNiuandYang[ 42 ]: 43

PAGE 44

24 ],givenby: 2(180)r zov(4{22)LatentHeatFluxLatentheatuxisbasedupontheresistancenetwork(seeFigure 4-1 (b)).Threesourcesthatcontributetotheuxare:soilevaporation(LEs),canopytranspiration(LEtr),andevaporationofinterceptedprecipitation(LEev). 44

PAGE 45

62 ].rbvistheleafboundarylayermoistureresistance.rlvandrsaresurfacevaportransportresistancesfortheleavesandsoil,repectively,wherelwisleafwidth.Theleafresistanceisbasedoncanopyassimilation[ 24 ]: 3 ], 45

PAGE 46

47 ]: @t=rqm(4{34) @t=rqh(4{35) 47 ];Kishydraulicconductivity,from[ 49 ];isthermalconductivityofsoilfrom[ 11 ],Sisasinkterm(rootwateruptake),andCv;sisthevolumetricheatcapacityofsoil.Cv;w,,andaretheheatcapacity,density,andheatofvaporizationofwater.Thesoilproleisdenedwithlayersofdierentconstitutiveproperties,dividedintocomputationalblocks,withthethicknessofblocksincreasingexponentiallywithdepth.Thecoupledheatandmoisturetransportequationsaresolvedusingablock-centered,foward-timenitedierencescheme.Theupperboundaryconditionisaheatandmoistureuxdeterminedbythemeteorologicalforcings,whilethelowerboundaryconditionassumesfreeowofheatandmoisture. 46

PAGE 47

AlgorithmforthecouplingoftheLSPandDSSATmodels. 4-2 .Thesoilmoistureandtemperatureprolesareinitializedinbothmodels.TheLSPmodelsimulatesenergyandmoistureuxesusinganadaptivetimestep.Atthelasttimestepofeachday,thedailyaveragesofET,soilmoistureandsoiltemperaturearecalculatedandpassedontotheDSSATmodel.TheDSSATusesthesevaluesincalculatinggrowthratestoobtainthecropvariablessuchasbiomass,LAI,etc.usingadailytimestep.Theestimatesofbiomass,root-lengthdensities,LAI,height,andwidthareprovidedtotheLSPmodelforuxestimationonthenextday.ThemainchallengeincouplinganSVATmodelsuchastheLSPandacropmodelsuchastheDSSATarisesfromthedierenceintimestepandthicknessofsoilnodes 47

PAGE 48

2-5 ).Toobtainforcingsforthemodelsimulations,weconrmedthatraingaugedatacoincidedwiththeobservedsoilmoistureincreases.Thedatawerescaledsuchthatthedailyaccumulatedobservationsfromthe 48

PAGE 49

ValuesforsoilpropertiesintheLSPmodel.Parameter Description 0-1.7m 1.7-2.7m 0.27 0.050 0.076 0.019Ksat 2.06104 0.0051 0.0040sat 0.34 0.41sa 0.894 0.512si 0.034 0.083c 0.071 0.405o 0.000 0.000 0.34 0.41 raingaugesmatchedthoseobservedindependentlyatthesameeldsiteusingcollectioncans[ 16 ].InitialconditionswerenotknownduringMicroWEX-2becausethesensorinstallationwascompleted7daysafterplanting.Therstvaluesobservedbythesoilmoistureandtemperaturesensorswereusedastheinitialmoistureandtemperaturevaluesforthesimulations.Soilphysicalpropertieswerebasedontextureandretentioncurvemeasurementstakenfromsoilsamplesintheeldatdierentdepths,andarelistedinTable 4-1 3 ,werecalibratedusingSimulatedAnnealingtominimizetherootmeansquaredierence(RMSD)betweenmodeledandobservedLAIandbiomassduringMicroWEX-2.IntheLSPmodel,12parameterswerecalibratedusingrepeatedLatinHypercubeSamplingoftheparameterspace[ 40 ].Fouroftheseparameterswererelatedtoradiationbalance:leafreectance,,leafangledistribution,x,soilemissivity,s,andcanopyemissivity,c.Theremainingeightparameterswererelatedtosensibleandlatentheatuxes:canopybaseassimilationrate,Fb,photosyntheticeciency,photo,bare 49

PAGE 50

Samplingrangesfrom[ 24 ]andcalibratedvaluesforparametersintheLSPmodel.Parameter Description SamplingRange Calibratedvalue 104-102 102-2.0 0.819 102-0.5 0.474c 0.95-0.995 0.973s 0.95-0.995 0.953cd 105-1.0 0.328iw 103-102 103-101 -108--1010 107-105 0.0-5103 0.0--6102 soilaerodynamicroughness,zob,leafwidth,lw,windintensityfactor,iw,canopydragcoecient,cd,andsoilevaporationresistanceparameters,soilaandsoilb.ThecalibrationoftheseparameterswasconductedtominimizeRMSDsbetweenthemodeledandobservedvolumetricsoilmoisture(VSM)at2cmandlatentheatux(LE)fortheoverallgrowingseason.ThesetwoobjectiveswerechosenbecauseVSMisoneofthemostimportantfactorsgoverningthemoistureandenergyuxes,andinthecalibrationVSMandLEwerefoundtobecompetingobjectives.Duringthecalibration,vethousandpointsweresampledintheformoftwenty250-pointLatinHypercubeSampleswithintherangesfromGoudriaan[ 24 ],speciedinTable 4-2 ,usingtheUniversityofFlorida'sHigh-PerformanceComputingCenter.ThesesampledpointswereorderedbyParetorankingandthesetofpointswiththelowestParetorankwereconsideredastheoptimalparameterset[ 25 ]. 50

PAGE 51

Paretofrontsfromcalibrationofthestand-aloneLSPmodel.TheasteriskrepresentsthepointontheParetofrontwherethetotalseasonalRMSDfor2cmVSMis0.04m3/m3. 4.5.1Calibration 4.5.1.1DSSATTable 3-1 providesthecalibratedvaluesofthesixcultivarcoecientsintheDSSATmodel.Thesevalueswereusedforsimulationsusingbothstand-aloneDSSATandcoupledLSP-DSSATmodels. 25 ].Figure 4-3 showstheParetofrontsfortheoverallgrowingseasonwithRMSDsbetweenthemodel 51

PAGE 52

4-3 ).Thesefourstagesinclude:almostbaresoil(DoY78-105),intermediatevegetationcover(DoY105-125),fullvegetationcover(DoY125-135),andreproductivestage(DoY135-154).AParetofrontcouldnotbegeneratedforthereproductivestageduetolackofLEobservationsduringthisstage.Ingeneral,thefrontsshowthatthemodelperformsbestduringtheintermediatecoverstage,withthefrontclosesttotheorigin,andworstduringthealmostbaresoilstage,withthefrontfarthestfromtheorigin.Theworstperformanceduringthebaresoilstageisprimarilyduetofewerobservations(<2000)fromMicroWEX-2duringthisstagecomparedtothe>4000observationsduringvegetatedstages,resultingincalibratedparametersbiasedtowardsminimizingdierencesduringthevegetatedstages.Forthestand-aloneLSPandLSP-DSSATsimulationsinthisstudy,theParetofrontfortheoverallseasoninFigure 4-3 wasusedtochoosethe12parametervaluescorrespondingtoanRMSDinVSMat2cmof0.04m3/m3,notedbyanasteriskintheFigure.ThischoicewasbaseduponthesensitivityofSVATmodelstoVSMforhydrometeorologicalapplications[ 20 34 36 ].WiththeRMSDinVSMof0.04m3/m3,thereisanexpectedRMSDinlatentheatuxofabout45W/m2fortheoverallseasonandabout55,40,and50W/m2fortherstthreestages,respectively(seeFigure 4-3 ).Table 4-2 liststhecalibratedparametervaluesusedintheLSPandLSP-DSSATmodelsimulations. 4.5.2.1DSSATTheDSSATmodelprovidedrealisticestimatesofgrowthanddevelopmentofsweetcorn.Boththestand-aloneDSSATandLSP-DSSATmodelsestimatedtheemergencedateonDoY90,comparedtoDoY86observedduringMicroWEX-2.Modeledanthesisday, 52

PAGE 53

ComparisonofLAI,drybiomass(kg/m2),andET(mm)forstand-aloneDSSATandcoupledLSP-DSSATsimulations. Stand-AloneDSSAT CoupledLSP-DSSAT RMSDMADBias RMSDMADBias LAI(-) 0.380.260.06 0.430.390.29TotalBiomass(kg/m2) 0.900.63-0.59 0.520.400.05ET(mm) 1.631.360.31 1.641.250.62 when75%ofthecornhassilked,wasDoY139,while75%silkingwasobservedonDoY135.Figure 4-4 andTable 4-3 showthecomparisonofestimatesofLAIanddrybiomassbythestand-aloneDSSATmodel,bytheLSP-DSSATmodel,andthoseobservedduringMicroWEX-2.EstimatesfrombothmodelsimulationscomparedwellwiththeobservationswithRMSDsof<0.5forLAIand<1.0kg/m2fordrybiomass.Theestimatesfromthetwomodelsdieredby<0.2forLAIand<0.6kg/m2fordrybiomass,withthecoupledLSP-DSSATmodelestimatinghighervaluesthanthestand-aloneDSSAT.TheserelativelysmalldierencescouldbeduetohigherdailyaveragesofsoilmoistureintheLSP-DSSATthanthoseinthestand-aloneDSSAT'sbucketmodel,by>0.02m3/m3(Figure 4-4 (c)).ThehighersoilmoisturevalueswouldpermitincreasedgrowthresultinginhigherLAIanddrybiomassinthecoupledmodel.ThehighmoistureestimatesalsoresultinhigherdailyETinthecoupledmodelcomparedtotheDSSAT(Figure 4-4 (d)).TheLSP-DSSATpredicts<0.5mm/dayhigherETthanDSSATalone,withtheRMSDbetweenthedailyestimatesofETbytheLSP-DSSATandobservationsof1.69mm. 53

PAGE 54

ComparisonofestimationsbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)drybiomass,(b)LAI,(c)5cmsoilmoisture,and(d)ET. 54

PAGE 55

Comparisonofsurfaceuxes(W/m2),forstand-aloneLSPandcoupledLSP-DSSATsimulations. Stand-AloneLSP CoupledLSP-DSSAT Flux RMSDMADBias RMSDMADBias NetRadiation 23.8616.1110.38 25.6218.1212.65LatentHeatFlux 46.3432.0314.96 50.6935.2818.84SensibleHeatFlux 34.4824.0715.69 37.1924.7914.88SoilHeatFlux 47.6826.24-1.54 46.5425.02-1.83 Table4-5. Comparisonofvolumetricsoilmoisture(m3/m3),forstand-aloneLSPandcoupledLSP-DSSATsimulations. Stand-AloneLSP CoupledLSP-DSSAT Depth(cm) RMSDMADBias RMSDMADBias 2 0.0470.0440.044 0.0460.0430.0434 0.0350.0290.029 0.0340.0280.0288 0.0360.0300.028 0.0360.0300.02832 0.0320.0310.031 0.0320.0310.03064 0.0620.0610.061 0.0620.0610.061100 0.0600.0570.057 0.0600.0570.057 growingstages.ThemodelsimulationswereconductedusingcalibratedparametervaluesgiveninTable 4-2 .ThissectiondiscussesstatisticsforcoupledLSP-DSSATmodelsimulation,butTables 4-4 4-6 providedetailedstatisticsforboththecoupledLSP-DSSATandthestand-aloneLSPmodelsimulation.EarlySeason-AlmostBareSoilThisperiodincludedtherst27daysofthegrowingseason(DoY78-105),whenitwas\almost"baresoilwithlowvegetation.Thecanopyheightwas<17cm,LAIwas<0.2,andvegetationcoverwas<0.22.Figures 4-5 (a)and(b)showtheestimated Table4-6. Comparisonofsoiltemperature(K),forstand-aloneLSPandcoupledLSP-DSSATsimulations. Stand-AloneLSP CoupledLSP-DSSAT Depth(cm) RMSDMADBias RMSDMADBias 2 2.802.221.90 2.431.911.374 2.882.211.73 2.562.001.218 2.602.031.73 2.271.771.2232 2.031.561.40 1.761.410.9364 1.701.241.09 1.451.150.67100 1.260.910.44 1.120.900.09 55

PAGE 56

Comparisonofnetradiation,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals Table4-7. MeasurementuncertaintitiesduringMicroWEX-2.Sensor Uncertainty Reference Raingauge 12mm/h [ 44 ]TDR 0.025VSM [ 5 ]Thermistor 0.1K [ 45 ]Soilheatux 15W/m2 56 ]Netradiation 22W/m2 56 ]Latentheatux 17-36W/m2 56 ]Sensibleheatux 21W/m2 56 ] 56

PAGE 57

Comparisonoflatentheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals andobservednetradiationaswellasresiduals(LSP-DSSATminusobserved)duringthisperiod,respectively.Overall,boththecoupledandthestandalonemodelscapturethephasesofthediurnalvariationinnetradiation.TheRMSDsbetweenthemodelestimatesandobservationsaresimilarforbothmodels'simulations(coupledLSP-DSSATandstand-aloneLSP)at32W/m2.However,thepeakdaytimedierencesareashighas100W/m2onDoY93,95,96,and97.ThiscorrespondstodayswhenthemodelestimatesofVSMat2cmwerehigherthanobserved,withRMSDof0.0374m3/m3andbiasof0.036m3/m3(Figure 4-9 ).ThisoverestimationinVSM,possiblyduetoimproperinitialconditionsand/orimproperprecipitationinputs(seeSections 4.4.1 and 4.5.2.2 ),wouldleadtolowerestimatesofsoilalbedousingEquation 4{8 .TheoverestimationalsoresultsinhigherLEestimates(Figures 4-6 (a)and(b))duetounderestimatedsoilsurface 57

PAGE 58

Comparisonofsensibleheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals resistanceusingEquation 4{31 .Inboththecoupledandthestandalonemodels,LEisoverestimatedwithRMSDsof54W/m2andbiasesof18W/m2.TheseRMSDsarehigherthanthesensoruncertaintyof17-36W/m2(Table 4-7 )butarecomparablewiththoseexpectedfromFigure 4-3 usingtheParetofrontfromtheearlyseason(seeSection 4.5.1.2 ).Boththecoupledandstand-alonemodelsestimatesimilarsensibleheatuxes,withRMSDsof40W/m2andbiasesof16W/m2(Figure 4-7 ).TheseRMSDsarelowerthanthoseobtainedforLE.ForthedayswhenLEispositivelybiased(e.g.DoY97,98,101,102,and103),thesensibleheatuxisbiasednegatively,andviceversa.TheoverallRMSDforsensibleheatuxescouldbeduetoslightlyloweraerodynamicresistanceand/orduetooverestimationofsoiltemperatureinboththemodels(Figure 4-10 ).The 58

PAGE 59

Comparisonofsoilheatux,betweenDoY78to105,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals RMSDsbetweenthemodelsandobservationsforsoiltemperatureare<2.22K.Thispositivebias(<1.7K)insoiltemperatureinthebeginningofthesimulationcouldbeduetoimproperinitialconditions(seeSection 4.4.1 ).Theestimatedsoilheatux(Figure 4-8 )isoverestimatedduringthedayandunderestimatedatnight.Theneteectofwhichis2cmsoilheatuxisslightlyunderestimatedwithRMSDsof48W/m2andbiasesof-3W/m2,becausethemagnitudeofthelatentandsensibleheatuxbiasesexceedsthatofthenetradiationoverestimation.Mid-Season-IntermediateVegetationCoverThisperiodincludedthenext20daysofthegrowingseason,whenthevegetationispartiallycoveringtheterrain(DoY105-125).Thecanopyheightwas17-73cm,LAIwas 59

PAGE 60

ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY78to105:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 60

PAGE 61

ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY78to105:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 61

PAGE 62

Comparisonofnetradiation,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals 0.2-1.82,andfractionalvegetationcoverwas0.22-1.00.Overall,themodelperformanceisbetterduringthisgrowthstagecomparedtothepreviousstage,asexpectedfromtheParetofronts(Figure 4-3 andSection 4.5.1.2 ).Asthevegetationcoverincreasedduringthisperiod,theresidualsinnetradiationdecreasesignicantly,indicatingthedecreasinginuenceofsoilalbedoonradiationbalance.Thedaytimeresidualsdecreasefrom80W/m2beforeDoY115to<30W/m2afterDoY115(Figure 4-11 ).Duetotheimprovednetradiationestimates(RMSD27W/m2),andthedecreasinginuenceofsoilsurfaceresistance,RMSDsinLEarelowerduringthisstagethanduringthebaresoilstage(compareFigures 4-6 and 4-12 )eventhoughVSMremainsoverestimatedbysimilaramounts(compareFigures 4-9 and 4-15 ). 62

PAGE 63

Comparisonoflatentheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals TheRMSDof40W/m2correspondtothoseexpectedfromtheParetofrontinFigure 4-3 .SimilarlylowRMSDsandbiasesarefoundinsensibleheatux,soilheatux,andsoiltemperature.Sensibleheatuxisoverestimated,butmatchesmorecloselywithobservationsduringthisstagethanduringthebaresoilstage(Figure 4-13 ),withRMSDsof30W/m2andbiasesof12W/m2.Soilheatuxremainsoverestimatedduringthedayandunderestimatedatnight,similartothepreviousstage(Figures 4-14 (a)and(b)).Overall,the2cmsoilheatuxisunderestimatedwithRMSDof39W/m2andbiasesof-6W/m2and.Thisisreectedinthesoiltemperature(Figure 4-16 )asaloweroverestimation(RMSD<1.67Kandbias<0.67K)thaninthepreviousstageforthe 63

PAGE 64

Comparisonofsensibleheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals stand-aloneLSP,andanunderestimation(RMSD<1.47Kandanegativebias>-0.91K)inthecaseoftheLSP-DSSATmodel.LateSeason-VegetativeStageThisperiodincludedthenexttendaysofthegrowingseason,whenthecornwasinthevegetativegrowthstageandatfullvegetationcover(DoY125-135).Thecanopyheightwas73-162cm,LAIwas1.82-2.49,andvegetationcoverwas1.00.Inthepreviousstage,asvegetationcoverincreased,residualsfornetradiationdecreased.Becauseoffullvegetationcoverduringthisstage,netradiation(Figure 4-17 )matchesverycloselywithobservations,withRMSDsof16W/m2andbiasesof8W/m2,lessthantheestimatedsensoruncertainty(Table 4-7 ).LEisoverestimatedwithRMSDof49W/m2andbiasof16W/m2(Figure 4-18 ).TheRMSDof49W/m2

PAGE 65

Comparisonofsoilheatux,betweenDoY105to125,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals correspondtotheRMSDexpectedfromtheParetofrontinFigure 4-3 .Thoughthenetradiationmatcheswell,itisstillbiasedhigh,whichwouldpermitlowerleafsurfacevaporresistancebyEquations 4{29 and 4{30 ,resultinginoverestimatedLEfromincreasedcanopytranspiration.OverestimatedVSM,showninFigure 4-21 ,(RMSD0.0492m3/m3andpositivebias0.0472m3/m3)couldalsoleadtooverestimationofLEbyincreasingsoilevaporation.Sensibleheatux(Figure 4-19 )isoverestimatedwithRMSDsof43W/m2biasesof22W/m2.Thisoverestimationcouldbeduetooverestimatedvegetationaerodynamicroughnesslength.The2cmsoilheatux(Figure 4-20 )isslightlyoverestimatedwithRMSDof44W/m2andbiasof0.70W/m2.Sinceduringfullcover,thenetuxgoingintothesoil 65

PAGE 66

ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY105to125:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 66

PAGE 67

ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY105to125:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 67

PAGE 68

Comparisonofnetradiation,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals isdominatedbytheuxbetweenthesoilandthecanopy,theoverestimationofsoilheatuxindicatesthatsoil-canopyuxisunderestimated.Thisoverestimationinsoilheatuxleadstooverestimationinsoiltemperature(Figure 4-22 ),moresothanduringintermediatevegetationcover,withapositivebias<2.68KandRMSD<3.32K.ReproductiveStageThelast19daysofthegrowingseason,DoY135-154,comprisedthereproductivestage,beginningwithsilkformation.Duringthisperiod,thecanopyheightwas162-200cm,LAIwas2.49-2.75,andvegetationcoverwas1.00.Thebiomassgrowthduringthisstagewasprimarilyduetoeargrowth. 68

PAGE 69

Comparisonoflatentheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals Similartothepreviousstage,netradiation(Figure 4-23 )matchesverycloselywithobservations,withRMSDsof17W/m2andbiasesof2.6W/m2.TheLEandHcomparisoncouldnotbepresentedduetomissingobservationsduringthisperiod.The2cmsoilheatux(Figures 4-24 )isslightlyoverestimatedwithRMSDsof55W/m2andbiasesof2.3W/m2,forsimilarreasonsasduringthenon-reproductivefullcoverperiod.Theoverestimationinsoilheatuxleadstooverestimationinsoiltemperature(Figure 4-26 ),withRMSD<3.39Kandapositivebias<3.39K.VSM(Figure 4-25 )isoverestimatedwithRMSD0.0632m3/m3andapositivebias0.0623.Theoverestimationcouldbeduetoincorrectprecipitationinputs,oraccumulatedmoisturebecauseofunderestimatedhydraulicconductivityinthebottomclaylayer.GrowingSeason-PlantingtoHarvest

PAGE 70

Comparisonofsensibleheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals ThecoupledLSP-DSSATmodelestimatesradiation,uxes,andsoilmoistureandtemperatureprolesthatareverysimilartothoseestimatedbythestand-aloneLSPmodelwithobservedvegetationparametersforthegrowingseason,asshowninFigures 4-27 4-29 andTables 4-4 4-6 .TheRMSDsfortheuxesfromtheLSP-DSSATmodelareslightlyhigher(by3W/m2)thanthosefromtheLSPmodel,primarilybecausemodeledcanopycharacteristicsusedintheLSP-DSSATmodelratherthanobservations.Forinstance,LSP-DSSAToverestimatesLAIby0.29,comparedtothestand-aloneDSSATwhichoverestimatesby0.06(Figure 4-4 (c)),increasingcanopyinterceptionandnetradiation.Overall,boththeLSPandLSP-DSSATmodelscapturethediurnalvariationsandphasesfornetradiation(Figure 4-27 (a))throughoutthegrowingseason.TheRMSDs 70

PAGE 71

Comparisonofsoilheatux,betweenDoY125to135,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals betweentheLSP-DSSATandobservednetradiationare24W/m2.Thesedierencesareclosetothesensoruncertaintyof22W/m2inTable 4-7 .Thebiasesare17W/m2indicateanoverestimation.LERMSDsof48W/m2arewhatcanbeexpectedfromtheParetofrontinFigure 4-3 .SuddenincreasesinLEonDoY93,109,119,and127,asshowninFigure 4-27 (b),areduetohighevaporationafterrainfallorirrigation.TheRMSDsof36W/m2forsensibleheatux(Figure 4-27 (c))arelowerthanthoseforLE.Themodeloverestimatesthediurnalamplitudefor2cmsoilheatux(Figure 4-27 (d)),whichhasLSP-DSSATRMSDsof47W/m2,duetodaytimeoverestimationofnetradiationandnighttimeoverestimationoflatentandsensibleheatuxes.TheRMSDforVSMat2cm(Figure 4-28 andTable 4-5 )issimilartoourchoiceof0.04m3/m3ontheoverallseasonParetofront(Figure 4-3 ).ForboththeLSPand 71

PAGE 72

ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY125to135:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 72

PAGE 73

ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY125to135:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 73

PAGE 74

Comparisonofnetradiation,betweenDoY135to154,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals LSP-DSSATmodelsimulations,theVSMsatalllayersexhibitpositivebiasthatincreasesduringtheseason.Abiasof0.02m3/m3couldbeintroducedatthebeginningofthesimulationduetoimproperinitialconditions(Section 4.4.1 )andsignicantuncertaintyinraingaugeobservations.DuringMicroWEX-2,thedierencesbetweendailyaccumulationsfromthefourraingaugeobservationsandthoseobservedindependentlybyusingcollectioncanswereupto10sofmm/day.Previousstudieshavealsofoundsimilarlyhighuncertaintiesinprecipitation,at12mm/h,usingsuchraingauges[ 44 ].TheVSMbiasof0.06m3/m3forthelayers0.64mandbelow(Figures 4-28 (e)and(f))couldbeduetotheimproperretentioncurveparametersintheclaylayer(below1.7m).Theparameterswerebasedonlyononesoilsamplefromthatlayerandcouldhaveresultedinloweruxestimatesatthelowerboundaryandhigherbiasesforthedeeper 74

PAGE 75

Comparisonofsoilheatux,betweenDoY135to154,estimatedbythecoupledLSP-DSSATandstand-aloneDSSATmodelsimulationandthoseobservedduringMicroWEX-2:(a)valuesand(b)residuals layers.ThedecreaseindrainagecouldalsocausepositivebiasinVSMfortheupperlayers,closertothelandsurface.Overall,soiltemperatures(Figure 4-29 )forbothmodelsimulationsmatchcloselywiththeMicroWEX-2observations.Duringthebaresoilperiod,soiltemperatureexhibitspositivebiasof<1.40Kandthisbiasisreducedduringtheintermediatevegetationcoverperiodto<0.91Kduetoanetreductionofsoilheatuxestimates.Asthesoilheatuxbiasincreases,thetemperaturebiasincreasesto<2.7KafterDoY125.TheseasonalRMSDsdecreasewithdepthwithamaximumof2.43K(Table 4-6 ). 1 75

PAGE 76

ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY135to154:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 76

PAGE 77

ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2,betweenDoY135to154:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 77

PAGE 78

ComparisonofuxesestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2:(a)netradiation,(b)latentheatux,(c)sensibleheatux,and2cmsoilheatux. 78

PAGE 79

ComparisonofvolumetricsoilmoistureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 79

PAGE 80

ComparisonofsoiltemperatureestimatedbythecoupledLSP-DSSATandstand-aloneLSPmodelsimulationandthoseobservedduringMicroWEX-2:(a)2cm,(b)4cm,(c)8cm,(d)32cm,(e)64cm,and(f)100cm. 80

PAGE 81

4-2 .Question4:"Howdothemodelestimatesofsoilmoisture,temperature,andsurfaceuxescomparewithMicroWEX-2observations?"TheRMSDforVSMat2cmis0.04m3/m3.ForboththeLSPandLSP-DSSATmodelsimulations,theVSMsatalllayersexhibitpositivebiasthatincreasesduringtheseason.TheseasonalRMSDsfortemperaturedecreasewithdepthwithamaximumof2.43K.TheRMSDsbetweenthemodeledandobservednetradiationwere24W/m2.LEestimateshadanRMSDof48W/m2,thesensibleheatuxestimateshadanRMSDof36W/m2,andthe2cmsoilheatuxestimateshadRMSDof47W/m2.Question5:"WhatistheimpactofcouplingonbothLSPandDSSATmodelestimatesofLAI,biomass,soilmoisture,temperature,andsurfaceuxes"TheestimatesfromtheDSSATandLSP-DSSATdieredby<0.2forLAIand<0.6kg/m2fordrybiomass,withthecoupledLSP-DSSATmodelestimatinghighervaluesthanthestand-aloneDSSAT.ThedierencesbetweentheLSPandLSP-DSSATestimatesofsoilmoisture,soiltemperature,andsurfaceuxesareallsmall. 81

PAGE 82

19 ]wasextendedforsweetcornusingobservedmoisturedistributionduringtheFourthandFifthMicrowaveWaterandEnergyBalanceExperiments(MicroWEX-4and-5).TheestimatedbythemodeliscomparedwiththatestimatedusingtheJacksonmodel.ThevaluesobtainedfromthetwoapproximationswereusedinamicrowaveemissionmodelatC-bandandthemodelestimatesofbrightnesswerecomparedwitheldobservations. 5.2.1MoistureDistributionMeasurementsFivemeasurementsofmoisturedistributionwereconductedduringMicroWEX-4:May12(DayAfterPlanting(DAP)63),May17(DAP68),May26(DAP77),June2(DAP84),andJune6(DAP88).ThesamplescollectedonDAP63and88consistedofplantsinvegetativestage,i.e.,beforeearformation,whilethosecollectedonotherdaysconsistedofplantsatvariousreproductivestages.AdditionalplantsamplewasobtainedonDAP88todeterminethedensityofwetvegetation(solid).ThreemeasurementsofmoisturedistributionwereconductedduringMicroWEX-5:April10(DAP32),May1(DAP53),andMay15(DAP67).ThesamplescollectedonDAP53and67consistedofplantsinreproductivestages.Allrepresentativeplantsampleswerecutevery10cmandweighedwet.Thesamplesweredriedat70oCforatleast48hoursandweighedtoobtaindrybiomass.ThesamplesonDAP63,2005werecutevery5cmtoensurethatnersampleswerenotneededto 82

PAGE 83

Observationsoftotalandearwetbiomassduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. obtainaccuratemoisturedistribution.Thedensityofvegetationmaterialforeachlayerwasmeasuredbyvolumedisplacementinagraduatedcylinder.Densityofwetvegetationandair,(z),calledtheclouddensity,wascalculatedforeachlayerasaratioofwetbiomassofeachlayerandthethicknessofthelayer(10cm).Themassofairisnegligible.Toobtainseasonalpattern,theclouddensityofeachlayerwasplottedasafunctionofheightofthelayer,showninFigure 5-3 59 60 ]: 83

PAGE 84

Observationsofcanopyheightduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. wherehiscanopyheight(m),k0isvacuumwavenumber(m1),and(z)=Imfnt(z)g(Np/m)istheabsorptioncoecientofthecanopy.Imfnt(z)gistheimaginarypartofthecomplexrefractiveindex,estimatedasthesumofvolumefractionofcomponents,nt(z)=1+vwcnwcvwc=(z) 7 ]).UlabyandEl-Rayes'model[ 57 ]estimatesnwcasafunctionoffrequency(6.7GHzforthisstudy)andmoisturemixingratio(Mg).Mgisdenedastheratioofweightofwaterinthecanopytotheweightofwetcanopy.Figure 5-4 showsthemixingratioduringMicroWEX-4and-5.Inthismodel,anisothermalcanopyisassumed,sothatthe 84

PAGE 85

59 ],inwhichthetotalbrightnesstemperatureofaterrain(TB)isasumofthreecontributions:TBs;p(fromthesoil),TBc;p(fromthecanopy),andTBsky;p(fromthesky). Figure5-3. Clouddensitiesmeasuredduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006.Thesymbolsandthelinesrepresentthemeasurementsandthebestcurve-ts,respectively. 85

PAGE 86

Moisturemixingratiosmeasuredduring(a)MicroWEX-4in2005and(b)MicroWEX-5in2006. 18 ](K),=cos()whereisthelookangle(50oforMicroWEX-5),Tcisthephysicaltemperatureoftheisothermalcanopy(K),measuredduringtheexperiments,!isthesinglescatteringalbedo,andTskyistheskybrightness(assumed5KatC-band).Inthismodel,rpisbaseduponthesemi-empiricalmodelofWegmullerandMatzler[ 63 ]: 86

PAGE 87

27 ].SoildielectricpropertiesaredeterminedusingafourcomponentmixingmodelfollowingDobsonetal[ 13 ]. 51 ]as: 4 ,theLSP-DSSAT-MBmodelsimulatedH-polTBfortheMicroWEX-2growingseason. 87

PAGE 88

ValuesoftheCoecientsinequations 5{6 and 5{7 Coecients Values 5.3.1MoistureDistributionFunctionAsshowninFigure 5-3 ,theclouddensityfunctionconsistsoftwoterms,alineartermrepresentingthevegetativestageoftheplantandagaussiantermrepresentingthemoistureintheearduringthereproductivestages,as: 2hnd e2i(5{6)wherea,b,c,d,andearettedparameters,hn=z/histhenormalizedheight,andBvandBearethewetbiomassofvegetation(stemandleaves)andear(kg/m2),respectively.Figure 5-3 alsoshowsthebestcurve-tsobtainedforeachsample.Theparametersc,d,ande,governingthegaussianterm,wereestimatedasquadraticfunctionsofdrybiomassoftheear(Dear)as: 5-1 givesthevaluesoftheaandbparametersandthecoecientsinequation 5{7 88

PAGE 89

Comparisonofcalculatedusingthebiophysicalmodel(withandwithoutthegaussianterm)andthatusingtheJacksonmodelduring(a)MicroWEX-4in2005,and(b)MicroWEX-5and2006. 5-5 showstheestimatedusingthebiophysicalmodelandtheJacksonmodelwithb=0:25.TheJacksonmodelestimatesloweropacitiesthroughoutthegrowingseasoncomparedtothoseobtainedusingthebiophysicalmodelwithrootmeansquaredierences(RMSD)betweenthetwomodelsof0.16NpduringMicroWEX-4and0.23NpduringMicroWEX-5.However,theJacksonmodelmatchedbetterwhenthechangeinthemoisturedistributionduetoearformationwasnotincluded,withRMSDsof0.10NpduringMicroWEX-4and0.11NpduringMicroWEX-5.Thecontributionofmoistureintheearstotheopticaldepthissignicantbecausetheycompriseasignicantportionofthetotalbiomass(seeFigure 5-1 ),withtheincreaseinbiomassprimarilyduetogrowth 89

PAGE 90

5-5 showsasharpincreaseinopticaldepthattheonsetofeardevelopment,atDAP62forMicroWEX-4andDAP47forMicroWEX-5.Bytheendoftheseasons,theopticaldepthisdoubledwhenearsareincluded. 5-6 showsthecomparisonofthehorizontallypolarized(H-pol)TBobservedduringMicroWEX-5withthosesimulatedbytheMBmodelatC-band,withestimatesusingthebiophysicalmodelandtheJacksonmodel.OnlyH-polbrightnessisexaminedherebecauseH-polbrightnessismoresensitivetochangesinsoilmoisturethanV-pol,attheincidenceangleof50o,thatisclosetotheBrewsterangleatmicrowavefrequencies[ 59 ].TheobservedTBincreasedduringthedrydownfromDAP42toDAP46.7andthen Figure5-6. ComparisonoftheobservedTBatH-polduringMW5thosesimulatedbytheMBmodelusingfromthebiophysicalmodelandfromtheJacksonmodelduringlate-seasonMicroWEX-5. 90

PAGE 91

RMSdierencesbetweenobservedTBduringMicroWEX-5andthoseestimatedbytheMBmodel DAP<47 DAP47 DAP42-52 Jackson(!=0.00) 5.84 12.50 9.74 Jackson(!=0.06forDAP47) 5.84 3.65 4.88 Biophysical!=0.05for42
PAGE 92

Comparisonofmicrowavebrightness,estimatedbytheLSP-DSSAT-MBmodelwithspecularsurface(a)andWegmullerandMatzler(b),andC-bandmicrowavebrightnessobservedduringMicroWEX-2,beforeDoY125.

PAGE 93

Comparisonofmicrowavebrightness,estimatedbytheLSP-DSSAT-MBmodelwithspecularsurface(a)andWegmullerandMatzler(b),andC-bandmicrowavebrightnessobservedduringMicroWEX-2,afterDoY125.

PAGE 94

RMSdierencesbetweenobservedH-polTBduringMicroWEX-2andthoseestimatedbytheMBmodel. RMSD(K) MAD(K) Bias(K) 35.87 28.94 -25.70 WegmullerandMatzler(DoY<125) 32.18 23.78 14.43 Specularsoil(DoY125) 10.06 8.15 6.58 WegmullerandMatzler(DoY125) 12.43 10.83 9.97 Duringlessthanfullvegetationcover(beforeDoY125),thesoilreectivitystronglyaectstheestimationofbrightness.ThisleadstoawidedisparitybetweenthespecularandWegmullerandMatzlerroughsurfacemodelestimates,ascanbeseeninFigure 5-7 .Thelowerreectivityoftheroughsurfacemodelleadstohigherbrightnessvaluesthanthespecularmodel.Suddendropsinbrightness(DoY98,100,104,107,109,112,114,119,122,123,and124)duetoirrigationorprecipitationeventsareonlyreachedwiththespecularmodel.Asvegetationcoverincreases,thecanopycontributiontobrightnessincreasessothespecularandroughsurfacemodels'estimatesofbrightnessapproacheachother.Overall,theperformancewhenvc<1ispoor,asseenbythehighRMSDsTable 5-3 .Neitherreectivitymodelmatchedsuddendropsinbrightness,andthereisalsoanunderestimationduetotheoverestimationofsoilmoisturebytheLSPmodel(Chapter 4 ).Duringfullvegetationcover(afterDoY125),modelestimatesofbrightnessaredominatedbythecanopycontributionandthusbyand!.Bothsurfacereectivitymodelsgivesimilarresultsandoverestimatebrightness,seeninFigure 5-8 andtheRMSDsinTable 5-3 .Thiscouldbeanindicationthatthe!valuesfoundforMicroWEX-5arenotcorrectforMicroWEX-2.AftermodeledearformationonDoY139brightnessislessoverestimatedas!isincreasedto0.075.Thislateintheseason,thereisalmostnoeectfromthesoil,andasthemodelisbiophysicallybased,theonlywaytoimprovemodelestimatesherewouldbetocalibrate!beforeandafterearformation. 1 94

PAGE 95

95

PAGE 96

96

PAGE 97

67 ].Othersignicantcontributionsaretheextensivedatasetsofsoiltemperature,soilmoisture,vegetation,surfaceuxes,andradiobrightnesscollectedduringMicroWEX-2,4,and5.Theyprovideseason-longandhightemporalresolutionobservationstoallowinterdisciplinarystudies. 4 ,theLSP-DSSATmodeloverestimatessoilmoisture,largelyduetouncertaintyinsoilhydrologicpropertiesandinprecipitation.ThisindicatesthatthemodelestimateswouldimprovewithacalibrationofthesoilhydrologicparametersinadditiontothetwelveparameterscalibratedinChapter 4 .Inaddition,sinceamajor 97

PAGE 98

5.3.3 ,fromthehighbrightnessRMSDsduringthersthalfoftheseason,eitherthespecular(Fresnel)reectivityortheroughsurfacereectivityaloneareinsucienttomatchthesuddendropsinbrightnessassociatedwithprecipitationevents.ThisisachallengebecauseoftheextremelylimitedbaresoilbrightnessdataduringMicroWEXs2,4,and5.Tollthedearthofbaresoildata,baresoiltestswereconductedduringMayandJune2007inwhichextensivesoilroughnessmeasurementsweretakenalongwithbrightnessmeasurements,before,during,andafterirrigationevents.Thisdatawouldbeusefulinthefutureforthedevelopmentofamorerigorousroughsurfacereectivitymodel.Amoisture-dependentroughnessmodelcouldcapturethesuddendropsinbrightnessassociatedwithprecipitationeventsbutneedstoberened.Inaddition,theLSP-DSSAT-MBmodeloverestimatedbrightnessinthelaterhalfoftheMicroWEX-2season,indicatingthatthe!valuesneedtobehigherfortheMicroWEX-2,andthattheyshouldbecalibrated.Theoptimal!valuesforMicroWEX-2andMicroWEX-5areapparentlydierent,indicatingthatthereissomedierencebetweenthecanopiesofthetwoseasonsthatwouldproduceadierencein!.Futureresearchcouldndsomephysicalrelationshipbetweencanopycharacteristicssuchasleaforstembiomassand!,similartothephysicalrelationshipfoundfor. 98

PAGE 99

[1] Boland,J.,L.Scott,andM.Luther(2001),Modellingthediusefractionofglobalsolarradiationonahorizontalsurface,Environmetrics,12,103{116. [2] Busetti,F.(2004),Simulatedannealingoverview,Tech.rep.,Availableatwww.geocities.com/francorbusetti/saweb.pdf. [3] Camillo,P.,andR.J.Gurney(1986),Aresistanceparameterforbaresoilevaporationmodels,SoilScience,141,95{105. [4] Campbell,G.S.,andJ.M.Norman(1998),AnIntroductiontoEnvironmentalBiophysics,2nded.,286pp.,SpringerScience+BusinessMedia,NewYork. [5] CampbellScientic(2006),CS616andCS625WaterContentReectometers,Tech.rep.,Availableathttp://www.campbellsci.com/documents/manuals/cs616.pdf,Logan,Utah. [6] Casanova,J.,T.Lin,M.Jang,K.Tien,J.Judge,O.Lanni,andL.Miller(2005),Fieldobservationsduringthefourthmicrowavewaterandenergybalanceexperiment(MicroWEX-4):FromMarch10-June14,2005,Tech.Rep.CircularNo1482,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE362. [7] Casanova,J.,M.Jang,andJ.Judge(2006),Verticaldistributionofmoistureinasweetcorncanopy,Tech.Rep.CircularNo1492,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE395. [8] Casanova,J.,F.Yan,K.Tien,J.Judge,O.Lanni,andL.Miller(2006),Fieldobservationsduringthefthmicrowavewaterandenergybalanceexperiment(MicroWEX-5):Frommarch9-may26,2006,Tech.Rep.CircularNo1514,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE407. [9] Chung,Y.(2007),Asnow-soil-vegetation-atmosphere-transfer/radiobrightnessmodelforwetsnow,Ph.D.thesis,UniversityofMichigan. [10] Dai,Y.,etal.(2003),TheCommonLandModel(CLM),Bull.Amer.MeteorSoc.,84(8),1013{1023. [11] deVries,D.A.(1963),Thermalpropertiesofsoils,inPhysicsofPlantEnviron-ment,editedbyW.R.vanWijk,North-HollandPublishingCompany,Amsterdam,Netherlands. [12] Demarty,J.,C.Ottle,I.Braud,andJ.Frangi(2002),ComparisonofmeasuredandSISPAT-RSsimulatedbrightnesstemperaturesandreectancesateldscaleduringReSeDAexperiment,ProceedingsofSPIE,theInternationalSocietyforOptical

PAGE 100

[13] Dobson,M.,F.Ulaby,M.Hallikainen,andM.El-Rayes(1985),Microwavedielectricbehaviorofwetsoil-partII:Dielectricmixingmodels,IEEETrans.Geosci.RemoteSensing,GE-23,35{46. [14] Doorenbos,J.,andW.Pruitt(1977),Guidelinesforpredictingcropwaterrequirements,Tech.Rep.IrrigationanddrainagepaperNo.24,UnitedNationsFAO. [15] Du,Y.,F.Ulaby,andM.Dobson(2000),Sensitivitytosoilmoisturebyactiveandpassivemicrowavesensors,IEEETrans.Geosci.RemoteSensing,38(1),105{114. [16] Dukes,M.(2004),UpdateoftheAFSIRScropwaterusesimulationmodel.Micrometeorologicaldataset,Tech.rep.,Availableathttp://afsirs.ifas.edu. [17] Dyer,A.J.N.(1974),Areviewofux-prolerelationships,Boundary-LayerMeteo-rology,7(3),363{372. [18] England,A.(1990),Radiobrightnessofdiurnallyheated,freezingsoil,IEEETrans.Geosci.RemoteSensing,28(4),464{76. [19] England,A.,andJ.Galantowicz(1995),MoistureinagrasscanopyfromSSM/Iradiobrightness,inProc.2ndTropicalSymposiumonCombinedOptical-MicrowaveEarthandAtmos.Sensing,vol.Atlanta,GA,pp.12{14. [20] Enthekabi,D.,etal.(2004),TheHydrosphere(HYDROS)SatelliteMission:anEarthsystempathnderforglobalmappingofsoilmoistureandlandfreezethaw,IEEETransactionsonGeoscienceandRemoteSensing,42(10),2184{2195. [21] Eom,H.J.(1992),Athermalmicrowaveemissionmodelforrow-structuredvegetation,Int.J.RemoteSensing,13(16),2975{2982. [22] EOS(2003),Advancedmicrowavescanningradiometerforeos,overview,sensor,andorbit,Tech.rep.,Availableathttp://www.ghcc.msfc.nasa.gov-/AMSR/. [23] Garcia-Quijano,J.F.,andA.P.Barros(2005),Incorporatingphysiologyintoahydrologicalmodel:photosynthesis,dynamicrespiration,andstomatalsensitivity,EcologicalModeling,185,29{49. [24] Goudriaan,J.(1977),CropMicrometeorology:ASimulationStudy,1sted.,249pp.,CentreforAgriculturalPublishingandDocumentation,Wageningen,theNetherlands. [25] Gupta,H.V.,L.A.Bastidas,S.Sorooshian,W.J.Shuttleworth,andZ.L.Yang(1999),Parameterestimationofalandsurfaceschemeusingmulticriteriamethods,JournalofGeophysicalResearch,104(D16),19,491{19,503. 100

PAGE 101

Jackson,T.,andT.Schmugge(1989),Passivemicrowaveremotesensingsystemforsoilmoisture:Somesupportingresearch,IEEETrans.Geosci.RemoteSensing,27(2),225{235. [27] Jang,M.Y.,K.Tien,J.Casanova,andJ.Judge(2005),Measurementsofsoilsurfaceroughnessduringduringthefourthmicrowavewaterandenergybalanceexperiment:FromApril18-June13,2005,Tech.Rep.CircularNo1483,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE393. [28] Jones,C.,andJ.Kiniry(Eds.)(1986),CERES-Maize:ASimulationModelofMaizeGrowthandDevelopment,TexasA&MUniversityPress,CollegeStation,Texas. [29] Jones,J.W.,G.Hoogenboom,C.Porter,K.Boote,W.B.L.Hunt,P.Wilkens,U.Singh,A.Gijsman,andJ.Ritchie(2003),TheDSSATcroppingsystemmodel,EuropeanJ.Agronomy,18(3-4),235{265. [30] Judge,J.,A.England,C.L.W.Crosson,B.Hornbuckle,D.Boprie,E.Kim,andY.Liou(1999),Agrowingseasonlandsurfaceprocess/radiobrightnessmodelforwheat-stubbleinthesoutherngreatplains,IEEETrans.Geosci.RemoteSensing,37(5),2152{2158. [31] Judge,J.,L.Abriola,andA.England(2003),Developmentandnumericalvalidationofasummertimelandsurfaceprocessandradiobrightnessmodel,AdvancesinWaterResources,26(7),733{746. [32] Judge,J.,A.W.England,J.R.Metcalfe,D.McNichol,andB.E.Goodison(2007),Calibrationofanintegratedlandsurfaceprocessandradiobrightness(LSP/R)modelduringsummertime,AdvancesinWaterResources,InPress. [33] Judge,Jasmeet,J.Casanove,T.Lin,K.Tien,M.Jang,J.Judge,O.Lanni,andL.Miller(2004),Fieldobservationsduringthesecondmicrowavewaterandenergybalanceexperiment(MicroWEX-2):FromMarch17-June3,2004,Tech.Rep.CircularNo1480,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE360. [34] Kerr,Y.H.,P.Waldteufel,J.Wigneron,J.Martinuzzi,J.Font,andM.Berger(),Soilmoistureretrievalfromspace:theSoilMoistureandOceanSalinity(SMOS)mission,IEEETransactionsonGeoscienceandRemoteSensing,39. [35] Kustas,W.P.,andJ.M.Norman(2000),Atwo-sourceenergybalanceapproachusingdirectionalradiometrictemperatureobservationsforsparsecanopycoveredsurfaces,Agron.J.,92,847{854. [36] Leese,J.,T.Jackson,A.Pitman,andP.Dirmeyer(2001),GEWEX/BAHCinternationalworkshoponsoilmoisturemonitoring,analysis,andpredictionforhydrometeorologicalandhydroclimatologicalapplications,Bull.Amer.Meteorol.Soc.,82(7),1423{1430. 101

PAGE 102

Lin,Tzu-yun,J.Judge,K.Tien,,J.C.andM.Y.Jang,O.Lanni,andL.Miller(2004),Fieldobservationsduringthethirdmicrowavewaterandenergybalanceexperiment(MicroWEX-3):Fromjuly16-december21,2004,Tech.Rep.CircularNo1483,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE361. [38] Liou,Y.,J.Galantowicz,andA.England(1998),Alandsurfaceprocess/radiobrightnessmodelwithcoupledheatandmoisturetransportforfreezingsoils,IEEETrans.Geosci.RemoteSennsong,36(2),669{677. [39] Manabe,S.(1969),Climateandtheoceancirculation.1.Theatmospherecirculationandthehydrologyoftheearth'ssurface,Month.Weat.Rev.,97(11),739{774. [40] McKay,M.D.,R.J.Beckman,andW.J.Conover(2000),Acomparisonofthreemethodsforselectingvaluesofinputvariablesintheananlysisofoutputfromacomputercode,Technometrics,42(1),55{61. [41] Mo,X.,S.Liu,Z.Lin,Y.Xu,Y.Xiang,andT.McVicar(2005),Predictionofcropyield,waterconsumptionandwateruseeciencywithasvat-cropgrowthmodelusingremotelysenseddatafromtheNorthChinaplain,EcologicalModelling,183,301{322. [42] Niu,G.-Y.,andZ.-L.Yang(2004),Eectsofvegetationcanopyprocessesonsnowsurfaceenergyandmassbalances,J.Geophys.Res.,109,D23,111,doi:10.1029/2004JD004884. [43] Njoku,E.G.,andJ.Kong(1977),Theoryforpassivemicrowaveremotesensingofnear-surfacesoilmoisture,J.Geophys.Res.,82(20),3108{18. [44] Nyusten,J.A.,J.R.Front,P.G.Black,andJ.C.Wilkerson(1996),Acomparisonofautomaticraingauges,JournalofAtmosphericandOceanicTechnology,13(1),62{73. [45] Omega(2006),Thermistorelementsandcompatibleinstrumentation,Tech.rep.,Availableathttp://www.omega.com/Temperature/pdf/44000 THERMIS ELEMENTS.pdf. [46] Pan,H.-L.,andL.Mahrt(1987),Interactionbetweensoilhydrologyandboundarylayerdevelopment,BoundaryLayerMeteorology,38,185{202. [47] Philip,J.R.,andD.A.deVries(1957),Moisturemovementinporousmaterialundertemperaturegradients,TransactionsofAmericanGeophysicalUnion,38,222{232. [48] Ritchie,J.(1972),Modelforpredictingevaporationfromarowcropwithincompletecover,WaterResourcesRes.,8,1204{1213. [49] Rossi,C.,andJ.R.Nimmo(1994),Modelingofsoilwaterretentionfromsaturationtoovendryness,WaterResourcesResearch,30,701{708. 102

PAGE 103

Sau,F.,K.Boote,W.M.Bostick,J.Jones,,andM.I.Minguez(2004),TestingandimprovingevapotranspirationandsoilwaterbalanceoftheDSSATcropmodels,AgronomyJ.,96(5),1243{1257. [51] Schmugge,T.,andT.Jackson(1992),Adielectricmodelofthevegetationeectsonthemicrowaveemissionfromsoils,IEEETrans.Geosci.RemoteSensing,30(4),757{760. [52] Schmugge,T.,andP.O'Neill(1986),Passivemicrowavesoilmoistureresearch,IEEETrans.Geosci.RemoteSensing,GE-24(1),12{22. [53] SoilConservationService(1972),Nationalengineeringhandbook:Section4:Hydrology,Tech.rep.,USDA. [54] Thornley,J.,andI.Johnson(1990),PlantandCropModeling.,Oxford:OxfordUniversityPress. [55] Tien,Kai-Jen,J.Judge,O.Lanni,andL.Miller(2003),Fieldobservationsduringthesecondmicrowavewaterandenergybalanceexperiment(MicroWEX-1):FromJuly17-December16,2003,Tech.Rep.CircularNo1470,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE280. [56] Twine,T.,W.P.Kustas,J.M.Norman,D.R.Cook,P.R.Houser,T.P.Meyers,J.H.Prueger,P.J.Starks,andM.L.Wesely(2000),Correctingeddy-covarianceuxunderestimatesoveragrassland,AgriculturalandForestMeteorology,103,279{300. [57] Ulaby,F.,andM.El-Rayes(1987),Microwavedielectricspectrumofvegetation-PartII:Dual-dispersionmodel,IEEETrans.Geosci.RemoteSensing,GE-25,550{557. [58] Ulaby,F.,andE.Wilson(1985),Microwaveattenuationpropertiesofvegetationcanopies,IEEETrans.Geosci.RemoteSensing,GE-23,746{753. [59] Ulaby,F.,R.Moore,andA.Fung(1981),MicrowaveRemoteSensingActiveandPassive,VolI,ArtechHouseInc.:Norwood,MA. [60] Ulaby,F.,R.Moore,andA.Fung(1986),MicrowaveRemoteSensingActiveandPassive,VolIII,ArtechHouseInc.:Norwood,MA. [61] VandeGriend,A.A.,andJ.Wigneron(2004),Theb-factorasafunctionoffrequencyandcanopytypeatH-polarization,IEEETrans.Geosci.RemoteSens-ing,42(4),786{794. [62] Verseghy,D.L.,N.A.McFarlane,andM.Lazare(1993),Class-aCanadianlandsurfaceschemeforGCMsII.Vegetationmodelandcoupledruns,Int.J.ofClimatol-ogy,13,347{370. 103

PAGE 104

Wegmuller,U.,andC.Matzler(1999),Roughbaresoilreectivitymodel,IEEETrans.Geosci.RemoteSensing,37(3),1391{1396. [64] Whiteld,B.,J.Jacobs,andJ.Judge(2006),IntercomparisonstudyofthelandsurfaceprocessmodelandthecommonlandmodelforaprairiewetlandinFlorida,JournalofHydrometeorology,7(6),1247{1258. [65] Williams,J.,P.Dyke,andC.Jones(1983),EPIC:Amodelforassessingtheeectsoferosiononsoilproductivity,Elsevier,Amsterdam. [66] Willmott,C.J.(1982),Somecommentsontheevaluationofmodelperformance,BulletinoftheAmericanMeteorologicalSociety,63,1309{1313. [67] Yan,Fei,J.Casanova,K.Tien,J.Judge,O.Lanni,andL.Miller(2006),Fieldobservationsduringthesixthmicrowavewaterandenergybalanceexperiment(MicroWEX-6):FromJune19-October31,2006,Tech.Rep.CircularNo1515,CenterforRemoteSensing,UniversityofFlorida,AvailableatUF/IFASEDISwebsiteathttp://edis.ifas.u.edu/AE409. 104

PAGE 105

JoaquinCasanovawasbornonDecember25,1984,inGainesville,Florida.Somestuhappened,thenin2006hegothisBSinAgriculturalandBiologicalEngineeringfromUF. 105