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Use of Special Sensor Microwave/Imager (SSM/I) for estimation of precipitation features in a semi-arid, mountain region: A case study of southwest Saudi Arabia

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O rd e r N u m b e r 9300476
U se o f S p ecia l Sensor M icrow ave/Im ager (S S M /I ) for estim a tio n
o f p recip ita tio n featu res in a sem i-arid, m ou n tain region: A case
stu d y o f sou th w est S au d i A rabia
Mashat, Abdul-Wahab Suliman, Ph.D.
Texas A&M University, 1992
UMI
300 N. ZeebRd.
Ann Arbor, MI 48106
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USE OF SPECIAL SENSOR MICROWAVE/IMAGER (SSM/I) FOR ESTIMATION
OF PRECIPITATION FEATURES IN A SEMI-ARID, MOUNTAIN REGION: A
CASE STUDY OF SOUTHWEST SAUDI ARABIA
A Dissertation
by
ABDUL-WAHAB SULIMAN MASHAT
Submitted to the Office of Graduate Studies of
Texas A&M University
in partial fulfillment of the requirements for the degree of
DOCTOR OF PHILOSOPHY
August 1992
Major Subject: Meteorology
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USE OF SPECIAL SENSOR MICROWAVE/IMAGER (SSM/I) FOR ESTIMATION
OF PRECIPITATION FEATURES IN A SEMI-ARID, MOUNTAIN REGION: A
CASE STUDY OF SOUTHWEST SAUDI ARABIA
A Dissertation
by
ABDUL-WAHAB SULIMAN MASHAT
Approved as to style and content by:
1A
vjJohn F. Griffiths
(Chair of Committee)
Gerald R. North
(Member)
Dusan Djuric
(Member)
Marshall T. McFarland
(Member)
Jeffrey J. Mcrfgan
(Member)
Edward J. Zipser
(Head of Department)
August 1992
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ABSTRACT
Use of Special Sensor Microwave/Imager (SSM/I) for Estimation of Precipitation
Features in a Semi-Arid, Mountain Region: A Case Study of Southwest Saudi Arabia.
(August 1992)
Abdul-Wahab Suliman Mashat
B.S., University of Petroleum and Minerals, Dhahran, Saudi Arabia;
M.S., University of Arizona, Tucson, Arizona
Chair of Advisory Committee: Prof. John F. Griffiths
The linear regression method was used to determine the degree of correlation
between soil moisture caused by rainfall and Special Sensor Microwave/Imager
(SSM/I) brightness temperatures or combinations of two brightness temperatures. This
study is concerned with the application of passive microwaves to soil moisture
classifications in a semi-arid, mountain region. The southwest region of Saudi Arabia
was chosen for this study. Two case studies were performed to investigate the response
of SSM/I brightness temperatures to soil moisture. The first case study is at satellite
ascending overpass time (about 6:00 a.m. local solar time), and the second case study is
at satellite descending overpass time (about 6:00 p.m. local solar time). It is shown that
brightness temperatures normalized with respect to ground temperature may be
interpreted in terms of the soil moisture in the surface layer of the soil. Normalized
brightness temperatures are not sensitive to soil moisture when precipitating clouds are
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present. The existence of precipitating clouds over the study area was determined
through an examination of brightness temperatures at 85.5 GHz. It was found that the
normalized brightness temperatures with respect to ground temperature responded to
the change of the soil moisture caused by rainfall. The normalized brightness
temperature in channel HI 9 with respect to ground temperature (H19/T) was the best
single SSM/I channel to use for a surface soil moisture investigation at satellite
descending overpass time, and the normalized brightness temperature in channel H37
with respect to ground temperature (H37/T) was the best single SSM/I channel to use
for a surface soil moisture investigation at satellite ascending overpass time.
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DEDICATION
This dissertation is dedicated to my parents for all their love and support
throughout the years. "My Lord! bestow on them the mercy even as they cherished
in childhood."
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ACKNOWLEDGMENTS
I wish to express my sincere appreciation and gratitude to my advisor Professor
John F. Griffiths for his guidance, encouragement, and patience throughout the study. I
extend my appreciation and acknowledgments to the other members of my advisory
committee, Dr. Gerald R. North, Dr. Dusan Djuric, Dr. Marshall J. McFarland, and Dr.
Jeffrey J. Morgan, for their suggestions and review of this manuscript. Special thanks
are extended to Dr. M. J. McFarland, who helped provide the SSM/I data and whose
suggestions were of great value. I thank my parents, my brothers, and my sisters for all
their encouragement. I also thank the Meteorology and Environmental Protection
Administration, Jeddah, Saudi Arabia; and the Hydrology Division, Department of
Water Resources Development, Ministry of Agriculture and Water, Riyadh, Saudi
Arabia, for providing data necessary for this study. I acknowledge the support of the
Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdul-Aziz
University, Jeddah, Saudi Arabia.
Finally, my thanks belong to my wife, Eman, and to my daughters, Arwa, Ala,
Afhan, and Azhar for their love and support.
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TABLE OF CONTENTS
CHAPTER
I
Page
INTRODUCTION......................................................................... 1
Problem Statement.................................................................... 1
Research Objective.................................................................... 3
II
LITERATURE REVIEW ............................................................. 4
Passive Microwave Emission Physics...................................... 4
Dielectric Constant.................................................................... 7
Thermal and Reflective D epth .................................................. 8
Passive Microwave Remote Sensing of Soil Moisture ............ 9
Roughness Effects .................................................................. 11
Vegetation Effects .................................................................. 12
Soil Texture E ffects................................................................ 12
III
DESCRIPTION OF THE STUDY AREA ................................. 14
Location and Topography ...................................................... 14
Clim ate.................................................................................... 14
IV
DATA COLLECTION AND PREPARATION......................... 24
Ground Truth D a ta.................................................................. 24
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CHAPTER
Page
Precipitation ........................................................................ 24
Evapotranspiration Methods................................................... 26
Estimation of Evapotranspiration........................................ 33
Soil Moisture M odel............................................................ 34
Ground Temperature .......................................................... 38
Satellite Data .......................................................................... 43
Sensor Description.............................................................. 43
SSM/I Brightness Temperatures.......................................... 45
Problems.................................................................................. 46
SSM/I D a ta .......................................................................... 46
Ground D a ta ........................................................................ 46
V
ANALYSIS AND DISCUSSION.............................................. 48
All Data .................................................................................. 48
Correction for Precipitating Clouds........................................ 61
Case Studies............................................................................ 63
Test Case ................................................................................ 79
VI
CONCLUSIONS AND RECOMMENDATIONS .................... 81
Summary of Results................................................................ 81
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ix
Page
Conclusions ............................................................................. 83
Recommendations ................................................................... 84
REFERENCES................................................................................................. 85
APPENDIX A ................................................................................................... 91
APPENDIX B ................................................................................................... 95
APPENDIX C ................................................................................................. 100
APPENDIX D ................................................................................................. 104
APPENDIXE.............................................................................
V ITA ................................................................................................................. 114
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Ill
LIST OF TABLES
Table
2.1
Page
The real and imaginary parts of dielectric constants for
some materials at 20'C................................................................ 8
3.1
Mean monthly temperatures ('C) ................................................ 17
4.1
Average elevation and number of rainfall stations in each
grid cell......................................................................................27
4.2
Class A pan evaporation rate (mm/day) for stations in
southwestern Saudi Arabia (1987).......................................... 28
4.3
Estimated evapotranspiration rates (mm/day) using
Thomthwaite's method (1987)................................................ 30
4.4
Estimated evapotranspiration rates (mm/day) using
Linacre's method (1987)......................................................... 31
4.5
Estimated evapotranspiration rates (mm/day) using
Hargreaves'method (1987)..................................................... 33
4.6
Available overpass SSM/I data for southwestern Saudi
Arabia........................................................................................ 47
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xi
Table
5.1
Page
Statistical analysis for brightness temperatures (K),
surface temperatures (K), normalized brightness
temperatures, polarization differences (K) and
polarization ratios for Nodes A and D for all
available SSM/I data for the period 1 July to 30
September, 1987 over southwestern Saudi Arabia................... 49
5.2
Correlation matrix of SSM/I brightness temperatures,
normalized brightness temperatures, and ground truth
data for Node A for all grid cells (N = 467)........................... 51
5.3
Correlation matrix of SSM/I brightness temperatures,
normalized brightness temperatures, and ground truth
data for Node D for all grid cells (N = 550)......................... 52
5.4
R2 values for each grid cell of normalized brightness
temperatures for Node A...........................................................57
5.5
R2 values for each grid cell of normalized brightness
temperatures for Node D...........................................................59
5.6
Statistical analysis for brightness temperatures (K),
ground temperature (K), normalized brightness
temperatures, polarization differences (K), and
polarization ratios for Grid Cell 16, Node A............................ 69
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xii
Table
5.7
Page
Statistical analysis for brightness temperatures (K),
ground temperature (K), normalized brightness
temperatures, polarization differences (K), and
polarizations ratios for Grid Cell 12, Node D...........................70
5.8
Correlation coefficients between the soil moistures (SMI
and SM2) and several transformations of the SSM/I
brightness temperatures for Grid Cell 16, Node A................... 72
5.9
Correlation coefficients between the soil moistures (SMI
and SMI) and several transformations of the SSM/I
brightness temperatures for Grid Cell 12, Node D................... 74
5.10
Regression analysis.......................................................................... 79
5.11
Comparison of calculated soil moisture (mm) SMI and
SM2 using ground observations with SMm9/T using
normalized brightness temperatures (7/19/7).................
80
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LIST OF FIGURES
Figure
Page
3.1
Location and topography of the study area..................................
15
3.2a
Isothermal map (°C) of study area for January............................
18
3.2b
Isothermal map (°C) of study area for July..................................
19
3.3
The prevailing wind direction and speed, isotachs in
ms'1..................................................................................
20
3.4
Annual rainfall at selected stations...............................................
22
3.5
Isohyets of the average yearly precipitation in
millimeters..............................................................................
4.1
The location of rainfall stations in southwestern Saudi
Arabia.....................................................................................
4.2
23
25
Comparison of estimated daily values of evaporation
rates (mm/day) using Thomthwaite, Linacre, and
Hargreaves methods with measured Class A pan
evaporation rate in southwestern Saudi Arabia for
4.3
1987........................................................................................
35
Scatter plots of soil moisture (mm) SMI versus SM2..................
37
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Figure
4.4
Page
Temperature versus elevation for July in southwestern
Saudi Arabia (1987)............................................................... 40
4.5
Temperature versus elevation for August in southwestern
Saudi Arabia (1987)..............................................................
4.6
41
Temperature versus elevation for September in
southwestern Saudi Arabia (1987)........................................
42
4.7
SSM/I scan geometry.......................................................................44
5.1
Soil moisture using method 1 (SMI) versus the
normalized brightness temperature (VI9/7) for all
grid cells, Node D..................................................................
5.2
Average normalized brightness temperature (VI9/T) for
each grid cell (dry-soil) for Node D......................................
5.3
55
Scatter plots of ground temperatures versus H19 - F85
for Node D.............................................................................
5.4
54
62
Time-series plots of rainfall and the normalized
brightness temperatures for Grid Cell 16, Node A,
between 1 July and 30 September 1987................................
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64
XV
Figure
5.5
Page
Time-series plots of rainfall and the normalized
brightness temperatures for Grid Cell 12, Node D,
between 1 July and 30 September 1987................................
5.6
65
Average of normalized brightness temperatures with
respect to ground temperature for dry soils {SMI = 0
mm) and wet soils {SMI >5.0 mm) for Grid Cell 16,
Node A..................................................................................
5.7
66
Average of normalized brightness temperatures with
respect to ground temperature for dry soils {SMI = 0
mm) and wet soils {SMI > 5.0 mm) for Grid Cell 12,
Node D..................................................................................
5.8
67
Relation between normalized brightness temperature
{HillT) and soil moisture {SMI) for Grid Cell 16,
Node A................................
5.9
76
Relation between normalized brightness temperature
{H19/T) and soil moisture {SMI) for Grid Cell 12,
Node D..................................................................................
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78
1
CHAPTER I
INTRODUCTION
Problem Statement
The hydrologic cycle is a dynamic system that is an integral component of the
Earth's environment. In this hydrologic cycle, soil moisture plays a role in mass and
energy exchange processes near the Earth's surface. Soil moisture information over
large areas is an important physical variable for meteorological, hydrological and
agricultural applications. In meteorology, information about soil moisture is needed
for providing a boundary condition variable for dynamic atmospheric models. In
hydrology and agriculture, information about soil moisture can be used in flood
forecasting, crop yield forecasting, and irrigation scheduling. Thus, accurate
estimations of soil moisture for large areas are helpful when obtained within short time
periods. However, direct measurement of soil moisture over large areas is difficult
because of the cost of installation and operation of instruments. Remote sensing
methods, therefore, which are based on the change in the electromagnetic properties
when water is added to the soil, provide a way to determine the spatial distribution of
soil moisture over large areas within short time periods.
In the 1970s, with the advent of satellite passive microwave remote sensing, it
was thought that near-surface soil moisture patterns over large areas could be
This dissertation follows the style of Monthly Weather Review.
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2
estimated with reasonable accuracy. Numerous specialists have conducted many
studies, some of which are cited in the literature review, during the following years.
They have shown that the obtaining of soil moisture from passive microwave imagery
is not as straightforward as originally thought.
Previous studies have been limited to flat terrain with reasonably uniform
vegetation but, even with these restrictions, the most successful results have led only to
the classification or categorization of soil moisture amount. This research is an attempt
to move into a more challenging topographical environment such as a semi-arid,
mountain region in which vegetation varies from lush to non-existent. In mountain
regions, the variations in vegetation cover, soil type, precipitation amount, ground
temperature, and topographic characteristics are large within small areas. For this
reason, it is more difficult to determine soil moisture distribution over mountain regions
than over smooth, bare surfaces or over uniform agricultural areas.
Encouraging progress has occurred since the launching of the Special Sensor
Microwave/Imager (SSM/I) with its seven channels scanning capabilities and better
resolution. With the advent of the SSM/I, opportunities now exist to improve estimates
or classifications of soil moisture over mountain regions if the effects of surface
inhomogeneities can be eliminated or reduced. The main hypotheses of this study is
that the soil moisture over southwestern Saudi Arabia can be determined when small
areas such as the satellite footprint are studied individually and elevation corrections for
ground temperature are made.
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3
The impetus for this study is the urgent need for the water resources of
southwestern Saudi Arabia to be monitored and understood. The region has a great
agricultural potential; but, as is well known, precipitation patterns in mountain areas are
not easily assessed by rain gauges. If some clues can be obtained from satellite
imagery, then an important step forward will have been made.
Research Objective
The goal of this research is to use the observed microwave brightness
temperatures from the Special Sensor Microwave/Imager (SSM/I) to obtain estimates of
soil moisture in near-surface layers for the southwestern region of Saudi Arabia. The
specific tasks necessary to achieve the goal of this research are
1. to estimate the soil moisture patterns, the "ground-truth," by means of daily
precipitation amounts and empirical evapotranspiration formulae;
2. to identify the brightness temperatures best related to the "ground-truth";
3. to incorporate in the method any factor(s) dependent upon the physical
features (relief, altitude, vegetation) of the region; and
4. to test the method from an independent sample.
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4
CHAPTER E
LITERATURE REVIEW
Passive Microwave Emission Physics
Everything having a temperature above absolute zero emits radiation. The
radiation intensity of a blackbody (a perfect emitter) in all directions Bf is given by
Planck's radiation function (Liou 1980):
B^ T)
c2[exp(hflkT) - 1] ’
( 1)
where k is Boltzmann's constant (k = 1.3806 * 10'23 J K'1) , h is the Planck's constant (h
= 6.6262 x 10'34J s), c is the speed of light,/is the frequency of radiation, and T is the
temperature (in kelvins).
In the microwave region of the electromagnetic spectrum h f « kT, the
Rayleigh-Jeans approximation to Planck's function is given as
(2)
This yields the radiation intensity of a blackbody in all directions. The microwave
radiometers are sensitive to radiation that exists in one polarization plane. Thus, the
polarized radiation intensity of a blackbody measured by a microwave radiometer
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is
where Tb is the brightness temperature (equivalent blackbody temperature), and the
subscript p refers to the vertical or horizontal polarizations of the wave. The vertical
polarization is that in which the electric field of the wave lies in the plane that contains
the local normal and the line of view (plane of incidence). The horizontal polarization
is that in which the electric field lies in die plane that is peipendicular to the incidence
plane.
fk
Let Ci = — ■
>then Eq. (3) can be written as
C/
^ ( 7 ) = c,r*.
(4)
For a terrestrial surface that is not a perfect radiator,
Bjj,{T) = ECiT,
(5)
where e is the emissivity, which is defined as the ratio of the radiation emitted by the
real surface to that emitted by the blackbody at the same temperature and wavelength.
The emitted radiation measured by the passive microwave sensor is expressed as
a brightness temperature. It is the sum of three components: sky radiation, atmospheric
radiation, and surface radiation. The equation for brightness temperature observed by a
radiometer as given by McFarland (1976) is
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6
T ^tirT ^+ eT ^ d + T^,,
(6)
where t is the atmospheric transmission coefficient, r is the surface reflectivity (1 - e ) ,
is the reflected sky brightness temperature, T„a is the temperature of the emitting
layer at the surface, and
is the brightness temperature of the atmosphere.
In the microwave region, the atmospheric effects are small (except when there
are precipitating clouds) and can be neglected. Paris (1971) showed that the effects of
emission by the atmosphere are small except in the presence of relatively large particles
such as rain drops. Harder (1984) estimated the maximum value of the first term in Eq.
(6) for moist bare soil, by multiplying the maximum value of
(30 K for uniform,
moderate precipitation) by a reflectivity (r) of 0.3 and the maximum value of
atmospheric transmission (t) of one. He found that the maximum value of this term is
less than about 10 K. Therefore, Eq. (6) can be written
Tt —
e
T jou
(7)
Equation (7) can also be derived from Eqs. (4) and (5). This equation was used
by McFarland (1976), McFarland and Blanchard (1977), Harder (1984), Wilke (1984),
and Lambert (1987) to estimate soil moisture using passive microwave radiometers.
They calculated the emissivity (normalized brightness temperature) by taking the ratio
of the brightness temperature measured by a passive microwave radiometer to the air
temperature estimated from meteorological data.
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7
Dielectric Constant
The intensity of radiation from the soil depends on the dielectric constant and
the temperature of the soil. The dielectric constant is a complex quantity that is a
measure o f the response of the object to an applied electromagnetic wave. The real part
of the dielectric constant determines the propagation properties of the wave, whereas
the imaginary part determines the energy losses or absorption. The dielectric constant
varies with frequency, moisture content, and temperature (Table 2.1). A number of soil
dielectric constant measurements have been made as functions of frequency,
temperature, soil moisture, and soil type (Cihlar and Ulaby 1974). At microwave
frequency, the dielectric constant is a function of soil moisture. The dielectric constant
of dry soil is much smaller (the real part less than 5) than that of water (the real part
about 80). When water is added to soil, the dielectric constant increases (Schmugge
1980). Emissivities of materials are inversely related to their dielectric constants. The
change in the dielectric constant would change the emissivity from about 0.9S for dry
layers to about 0.6 or less for wet layers. Therefore, the brightness temperature is much
lower for moist soil than for dry soil at the same surface temperature. The relation
between dielectric constant and water content depends on soil type or texture
(Schmugge 1980) and temperature (Poe et al. 1971).
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8
Table 2.1. The real and imaginary parts of dielectric constants for some
materials at 20* C.
Real part
Imaginary part
Ice (pure)
3.2
0.1
Air
Rock
1.0
0.0
5.5
2.8
0.2
0.0
1.4
Water at 20’C
19.6
35.0
4.8
40.0
1.4
20.0
Water
56.4
34.9
10.7
Water
Water
73.3
77.9
21.7
12.6
5.0
Water
80.0
4.5
Material
Dry soil
Wet soil
Frequency (GHz)
2.7
1.0
Data obtained from Mo et al. (1980) and Schmugge (1985).
Thermal and Reflective Depth
Wilheit (1978) defined thermal and reflective sampling depths. Thermal
sampling depth is defined as the layer whose temperature determines the amount of
upwelling radiation from the soil, whereas reflective sampling depth is defined as the
layer whose moisture content determines the emissivity of the soil. He showed
theoretically that the thermal depth is on the order of one wavelength, whereas the
reflective depth is on the order of a tenth to a few hundredths of a wavelength (range
from 0.073 to 0.032 of a wavelength). The thickness of the reflectivity sampling depth
decreases with increasing soil moisture. Newton (1977) and Newton et al. (1982)
showed experimentally that 1.4 and 10.6 GHz can be used to estimate average soil
moisture within two depths. The depth is a function of the frequency of the emitted
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9
radiation. At 1.4 GHz, average moisture can be estimated to depths of up to 20 cm,
whereas at 10.6 GHz average moisture in the 2-cm surface layer can be estimated. Mo
et al. (1980) determined that the radiometric sampling depth is between 0.06 and 0.10
times the wavelength.
Passive Microwave Remote Sensing of Soil Moisture
During the last two decades, progress has been made in the study of the
relationships between near-surface soil moisture content and brightness temperature as
measured by microwave radiometers. Theoretical (e.g., Njoku and Kong 1977; Wilheit
1978; Burke et al. 1979) and experimental (e.g., Newton 1977; Schmugge 1978;
Newton and Rouse 1980; Njoku and O'Neill 1982) studies have shown a strong
correlation between soil moisture content and brightness temperature measured
remotely by using passive microwave sensors. Previous research results (Schmugge et
al. 1979; Schmugge 1983) have shown that passive microwave remote sensing can be
used to classify near-surface soil moisture.
Poe et al. (1971) investigated the relationship of the moisture content of a bare
soil to the emissivity. They found that the emissivity of a smooth, bare area varies from
0.9 or greater for dry soil to about 0.5 for wet soil. Their results were based on
microwave radiometric measurements at wavelengths of 0.81,2.2, 6.0, and 21.4 cm.
Schmugge et al. (1974) reported that microwave radiometers are sensitive for
monitoring soil moisture content over depths on the order of a few centimeters. The
emission is a function of soil moisture and radiometer frequency. They found that in
aircraft tests at 1.55 cm (19.35 GHz) little or no variation is present in soil emissions if
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10
the soil moisture is less than 15% water content by weight. The emission decreases
linearly by approximately 3 K for each percentage point increase above 15% to about
40% in soil moisture. In contrast, the emission at 21 cm (1.4 GHz) is a linear function
of soil moisture content over the 0%-35% moisture range. Burke and Paris (1975) used
measured brightness temperatures at vertical (Tv) and horizontal (7%) polarizations to
estimate the soil moisture of the top centimeter of soil. They showed that the Stokes
parameters [Tv+Th\!2 and [Tv-Th] can be used to distinguish between moisture and
surface roughness effects. Eagleman and Lin (1976) compared the observed brightness
temperatures from Skylab over Texas, Oklahoma, and Kansas with soil moisture based
on a combination of actual ground measurements and estimated soil moistures using a
climatic water balance model. They found a strong correlation (-0.96) between the 21
cm (1.4 GHz) brightness temperature and the estimated soil moisture of the upper
2.5-cm soil layer.
The antecedent precipitation index (API), which is a soil moisture model that
requires only the precipitation amount as input, has been used to indicate soil moisture
conditions and correlate it with microwave brightness temperature. By assuming that
reflectivity from the sky and clouds is negligible and that atmospheric transmissivity is
1.0, McFarland (1976) has shown a relationship between the Skylab 21-cm (1.4 GHz)
brightness temperature for data obtained over Texas and Oklahoma and API. Using the
same assumptions and passive microwave data, McFarland and Blanchard (1977),
Blanchard et al. (1981), McFarland and Harder (1982), Wilke (1984), and Wilke and
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11
McFarland (1986) presented strong correlations between API and the normalized
brightness temperature.
Roughness Effects
Surface roughness, vegetation cover, and soil texture have an effect on the
ability of passive microwave radiometers to detect soil moisture near the surface. The
effect of surface roughness on the passive microwave response was studied by several
investigators such as Lee (1974), Newton (1977), Choudhury et al. (1977), Wang et al.
(1980), and Tsang and Newton (1982). All of these investigations have testified that
roughness effects are more significant at shorter wavelengths than at longer
wavelengths. The effect of surface roughness is to increase the surface emissivity,
which will decrease the sensitivity o f the microwave emission measurement to soil
moisture (Newton and Rouse 1980). The study by Newton et al. (1974) showed that
the effects of surface roughness vary according to frequency and polarization. They
found that at 10.69 GHz no simple relationship exists between surface roughness and
radiometric soil temperature. However, at 1.4 GHz the effects of roughness on
horizontally polarized data are significant. The difference between vertical and
horizontal polarization can be related to soil surface roughness (Newton, 1977;
Choudhury et al., 1979). Furthermore, brightness temperature increases as surface
roughness increases. Choudhury et al. (1977,1979) studied the surface roughness
effect on the microwave emission from soils. Their results indicate that roughness
effects are larger for wet soils, where the difference between smooth and rough surfaces
can be as much as 50 K. They showed that surface roughness increases emissivity by
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12
Ae = r0[ 1 - exp(-/t)],
(8)
where r0 is the reflectivity for the smooth surface and h is the roughness parameter,
which is proportional to the root-mean-square height variation of the surface with h = 0
for a smooth surface.
Vegetation Effects
The effect of vegetation on the sensitivity of the microwave brightness
temperature to soil moisture depends on the vegetation mass and the frequency of the
emitted radiation. Vegetation emits its own radiation and weakens the microwave
emission from the surface. Lee (1974), using a dual radiometer, found that the effect of
vegetation is highly significant. The 10.69 GHz frequency is more affected by thick
vegetation than the 1.41 GHz frequency. Burke and Schmugge (1982), using data
collected near Phoenix, Arizona, found the vegetation effect more significant at longer
frequencies than at shorter frequencies. Ulaby et al. (1983), using data collected in
1978 near Colby, Kansas, found that for com fields the radiometric sensitively to soil
moisture decreases with an increasing angle of incidence from the nadir and with an
increasing frequency of radiation emitted from the surface.
Soil Texture Effects
The term "soil texture" is an expression of the range in size of the soil particles.
The effects of soil texture on passive microwaves can be explained by the hypothesis
presented in Schmugge (1980). The large dielectric constant of water is a result of the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
13
molecule's ability to align its dipole moment along an applied field. Therefore,
anything that will slow the rotation of water molecules, such as freezing or tight
binding to a soil particle, will decrease the dielectric constant of water. When a sm all
amount of water (less than some transitional levels) is added to the soil, it is tightly
bound to a particle's surface and will contribute only a small increase to the soil's
dielectric constant. When more water is added to exceed the transition level, the
additional water molecules will be free to rotate and thus increase the soil's dielectric
constant. For example, sandy soils have less of the tightly bound water than clay soils;
thus, the transition moisture levels are lower in sand than in clay soils.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
14
CHAPTER m
DESCRIPTION OF THE STUDY AREA
Location and Topography
The area of study, southwestern Saudi Arabia, is bounded on the south by
Yemen; on the north by latitude 20°N, on the east by longitude 43*30*E, and on the
west by the Red Sea (Fig. 3.1). The western coastal plain, abutting the Red Sea, is
called Tihama. It rises eastward to the foothills and then to the Sarawat Mountains
(Jubal Al-Sarawat), which parallel the Red Sea. These, in turn, are followed by the
Najed Plateau and the Empty Quarter in the east. The mountains vary in width,
reaching a maximum near the Yemen border, and rise to a height of approximately
3300 m near Al-Suda.
A region partially or completely surrounded by mountains is called a "wadi."
The sizes and physical characteristics of the wadis vary but generally are somewhat flat
with one or more drainage areas where flash floods may occur.
Climate
The climate of southwest Saudi Arabia is greatly influenced by the local
circulation, caused by the topography of the region, and by the general circulation.
Because of the short period over which meteorological records have been taken, limited
information about the climate of the region is available. The meteorological
information has been extracted from the records of the Meteorology and Environmental
Protection Administration (MEPA), Jeddah, Saudi Arabia; and from the hydrological
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Latitude
15
41.50
Longitude
Figure 3.1. Location and topography of the study area (contour in m).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
16
publications of the Hydrology Division, Department of Water Resources Development,
Ministry of Agriculture and Water, Riyadh, Saudi Arabia.
During the summer months (June, July, August), the m axim um temperature
may exceed 44.7’C (e.g., Kwash, July 1984). Table 3.1 shows that the mean monthly
temperature, calculated over the years 1970 to 1986, ranges from 35* C for station
SA001 (Malaki) to 20.3°C for station A007 (Alnamas). Higher stations display lower
temperature because of their high elevation. During the winter months (December,
January, February), the mean monthly temperature varies from 10°C (A lnam as,
December) to 27.2*C (Malaki, February). The coastal stations show smaller increases
in temperature between the winter and sum m er averages than the inland stations.
During the spring months (March, April, May), the mean monthly temperature varies
from 13.l'C (Alnamas, March) to 33.9°C (Malaki, May). During the fall months
(September, October, November), the mean monthly temperature ranges from 12*C
(Alnamas, November) to 33.7° C (Sabya, SA002, September). Isothermal maps (Figs.
3.2a and 3.2b) for January and July have been constructed from the values shown in
Table 3.1.
The prevailing wind direction changes from season to season (Fig. 3.3). During
winter (January), the wind is northerly to northeasterly. The northern part of the region
is affected by the low pressure located at the east of the Mediterranean Sea that moves
across northern Saudi Arabia. During summer (July), die winds are northwesterly to
southwesterly. The southern part of the region is affected by the advancement
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 3.1. Mean monthly temperatures (°C).
Station
code*
A004
A005
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
Dec
18 10 2400.0
12.8
16.4
21.7
22.1
22.7
13.1
17.9
1982-86
20.7
17.7
17.2
14.7
22.0
23.0
21.5
21.5
12.3
17.7
17.0
20.3
18 12 2200.0
13.5
13.4
14.7
12.9
17.4
1970-86
21.3
Long. Lat.
Elev.
D M D M (m)
43 06
42 29
15.5
19.6
Mean Period of
record
16.7
18.7
20.9
21.4
19.6
15.4
13.8
12.6
16.9
1974-86
20.3
19.4
16.2
28.1
29.6
20.5
30.4
20.3
22.8
14.9
25.3
17.8
30.5
27.4
22.7
12.0
19.8
10.0
17.3
15.4
24.2
1970-86
1970-86
18.6
22.3
24.4
27.8
29.5
30.6
30.8
27.4
22.5
20.1
17.4
24.0
1974-86
12.1
13.4
16.2
18.7
21.4
23.9
24.4
23.8
22.9
19.5
15.4
12.9
18.7
1970-86
53.0
25.4
25.7
29.7
31.9
32.8
32.6
32.2
28.3
26.5
29.6
1970-86
A006
42 36
A007
B004
42 09
13.5
15.6
10.1
17.2
10.5
19.2
13.1
42 36
19 06 2600.0
20 01 1020.0
B005
42 32
19 52 1090.0
17.1
B007
41 33
19 52 2400.0
J001
41 03
19 32
18 15 2100.0
12.7
SA001
42 57
17 03
190.0
26.2
27.2
27.4
29.2
31.9
33.9
35.0
32.7
34.5
33.7
33.0
30.2
31.2
29.1
27.2
31.0
1970-86
SA002
42 37
17 10
40.0
26.3
27.1
28.5
31.1
33.4
34.5
34.6
34.3
33.7
31.3
28.9
27.0
30.9
1970-86
SA003
SA004
41 53
350.0
30.0
25.0
25.5
25.6
28.2
34.3
32.7
34.3
32.8
33.6
33.1
32.2
32.9
29.9
30.6
28.7
25.7
26.6
1970-86
27.6
33.0
32.1
30.0
25.7
30.6
30.1
27.7
41 24
19 00
18 44
29.9
1970-86
41084
42 37
19 59 1163.0
18.0
20.1
22.7
25.4
28.3
30.7
32.1
31.9
29.2
25.0
20.3
17.9
25.1
1975-87
41114
42 48
18 18 2057.0
13.4
15.9
17.2
18.4
21.9
23.8
24.3
23.3
22.8
19.2
16.3
19.2
1970-89
41140
42 35
16 56
25.7
26.3
28.0
30.3
31.9
33.2
33.6
33.2
32.9
31.6
29.0
14.3
26.7
30.2
1970-89.
3.0
* For station's name see Appendix B.
18
40.50
Latitude
20.00
41.00
41.50
42.00
42.50
43.00
43.50
20.00
19.50
19.50
19.00
19.00
18.50
18.50
16.00
18.00
17.50
17.50
17.00
17.00
16.50
40.50
41.00
41.50
42.00
42.50
43.00
43.50
Longitude
F igure 3.2a. Isotherm al m ap (°C ) o f study area fo r January.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
19
*0-50
20.00
41.00
41.50
42.00
42.50
43.00
20.00
19.50
19.50
19.00
19.00
?o
18.50
Latitude
18.50
18.00
18.00
17.50
17.50
17.00
17.00
16.50
40.50
41.00
41.50
42.00
42.50
4 3 .0 0
Longitude
F igure 3.2b. Isotherm al m ap (°C ) o f study area for July.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
43.50
20
\'~tT?A}'
|V . I . V . 'A L V '/ 'l '
1 4 i /
'T
\ S'.
\\\\
i 1 1 jJan
an sss
%\ \ m w w.4
t t i i vjoNi
J f i l l | vnt n \
ir ' W
f o s s' ' 'sV
J ^ T
s ^ V u rtv \
[S V
* 4*4/4 4
i r < i l r t s <1
V V S J T v \ \ \ \ t 5 j bA/ 4 ' ' n y f T - t 5 ; 4
u ^ n \n u u i it kh i N
i > /’'yf V
S 'li*
/ --•.•1111414 ia i i rLi^y 20l i i i t i i i i t i i i T V /t -
l-
T^-WOs^ss^s ss S^' -------- :- r i o -N d
I Vf s s s s s s
s .s s s s
S
S
S
<
5
S
-A
»
K
»
-.-S
'
4 t V \ s S s s ' S ■,lC> , / t T
y*
7Tn isi-.iJ.,f
-*
.i I 4 l i \ v\ v / n n
i n H . i l i nuu i in uu v\ s' s- . ' ■£-'?.- - ~1 .=1
4
S / y / / / / t V! I I 4 J / s / . '
(si.111
I i/i 11 iYi i r f
/ / 11 i s i i i i,r\ \ \ n c
»"*\1iTj .
> »i,-s. l* ._ s .\ V s V v i J s V 4 4 s - —'*c ^ 2 s* ’* '**'?*%’'» ^ \ V \ \
^
t A n v * •* /
44 J j \ A
44
i .
. / / l / l ' / V
' s b
.
-
f- S i ' l-y \< > w s \ - ' , s <
• y l\ i y i v » s \\ s \ ^
V — f 4/ 1 V V > s j s . ^
;
>^40/
EV^-'-sis- *■,'iyJ_‘/v"i' j
sxJ-sss
i•.
/ i r n ’i
Oct ^
f i i m n n Cvi r.i. i . . •v >Ms
l U U l f M U 1IMV».l l.V90"-;
ivn
»<
.s^ssys V4-1/1-ju. l \1V
:^ ::o ^ -iK 3 0 N B
'i 4 i l l \ \ \ \
4 4 l U > J U w V - \ 2 - . - T - W ^ 'k 1 1 l K
' . l
l I 4
i iS -tt
/l i- 4 . \ N \ S S N N
■K;v
f ✓♦j
'6 0
L1
■-*-*v
t r / / •^
TOE
J \«V
—
4—
S '- --- ——
>
y\ *\*j^H i v/f
/' 'im
/TX'*
—
V \ \a \ \ y / y r \ } t f
a
\ \ y y r \ t * r r A*
"
^-
Figure 3.3. The prevailing wind direction and speed, isotachs in m s'1(after
Hastenrath et al. 1979).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
21
northward of the moist southwest monsoon and the Intertropical Convergence Zone
(ITCZ). Spring and fall are the transition seasons.
Because of the high mountains and the effect of the Indian Ocean monsoons, the
southwestern region of Saudi Arabia has a large mean annual amount of precipitation in
comparison with other regions in Saudi Arabia. Rain may fall in any month of the year
(Al-Qurashi 1981). The precipitation distribution over the region is not uniform
because of the combination of topographic and climatic factors (Alehaideb 1985). For
example, the annual rainfall at one station, e.g. Alnamas, differs greatly from
year-to-year (see Fig. 3.4). The mean annual rainfall for the southwest is about 300
mm. However, annual rainfall exceeds 700 mm in the foothill regions (SA126,540 m).
The coastal plain stations receive about 100 mm of precipitation per annum (Fig. 3.5).
Over the southwestern region of Saudi Arabia, rainfall distribution varies from season
to season with peaks of rainfall in spring and summer. During the summer, the south
section of the foothills (< 1500 m) receives more rainfall than the mountain region and
the Red Sea coastal plain. The mountain region, on the other hand, receives the largest
amount of rainfall during the spring. During the fall, stations located in the foothills
receive more rainfall than those located in the mountains. During winter, the foothill
and mountain regions have about the same amount of rainfall, with the largest amount
occurring in the north section of the region (see Appendix A).
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22
900
800-
A lnam as, A007
A vg.-518 nun
S t.D .-184 nun
700-
Precipitation
(m m )
600-
500 ■>•
B inhah, B004
Avg.*114 nun
400
S t.D .-9 5 nun
300-
200-
100
1970
1972
1974
1976
1978
1980
1982
1984
1986
Year
Figure 3.4. Annual rainfall at selected stations. St. D. = standard deviation.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
23
40.50
41.00
41.50
42.00
42.50
43.00
20.00
43.50
20.00
19.50
19.00
18.50
a;
S
C
C8
18.00
18.00
17.50
17.50
17.00
17.00
16.50
16.50
41.00
42.00
43.00
Longitude
Figure 3.5. Isohyets of the average yearly precipitation in millimeters.
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24
CHAPTER IV
DATA COLLECTION AND PREPARATION
Ground Truth Data
Soil moisture at any point is affected by a number of factors, including the
amount of soil moisture at an earlier time, precipitation, surface runoff, and actual
evapotranspiration (AE1). Therefore, to estimate soil moisture, which will be
considered as "ground truth," the daily precipitation amounts measured by rain gages,
and the daily AET are needed. AET will be estimated from potential evapotranspiration
(PET), which will be calculated by either the Thomthwaite, Linacre, or Hargreaves
method. Meteorological data used were extracted from the records of the Meteorology
and Environmental Protection Administration (MEPA), Ministry of Defence and
Aviation, Kingdom of Saudi Arabia, and the Hydrology Division, Ministry of
Agriculture and Water, Kingdom of Saudi Arabia.
Precipitation
Precipitation is the primary input quantity in the soil moisture model. Daily
precipitation records for 122 stations in the study area for the period July-September
1987 were found (see Fig. 4.1 and Appendix B). Rainfall was recorded to the nearest
0.1 millimeter (mm). If more than one rain gage was located at the station, the priority
assigned to the gages is (1) the recording rain gage, (2) the standard 8" rain gage, and
(3) the totalizing rain gage.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
25
40.50
41.00
41.50
42.00
42.50
43.00
43.50
20.00
20.00
19.50
19.50
Latitude
10
19.00
19.00
18.50
18.50
20
24
22
23
25
26
18.00
18.00
17.50
17.50
28
17.00
17.00
16.50
40.50
41.00
41 .5 0
42.00
42.50
43.00
43.50
Longitude
F igure 4.1. The location o f rainfall stations in southw estern Saudi A rabia. The
g rid cells are num bered according to the above system .
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
26
Because of the spatial resolution of the satellite, the study area was divided into
small grid cells (0.25* * 0.25*). Grid cells that are at least 0.25* from the sea and have
at least one rainfall station with continuous daily rainfall data for the period from 1 July
to 30 August 1987 were chosen for this study. It was found that thirty-three grid cells in
the study area met the above conditions (Table 4.1). The areal precipitation average for
each cell was defined as the arithmetic mean of the rain gages available in each grid
cell. The cells were numbered from the northwest beginning with number 1 and ending
with number 33 (Fig. 4.1).
Evapotranspiration Methods
Evaporation from the surface occurs when water and energy are available, and a
positive vapor pressure difference exists between the surface and the air above. An
estimate of evaporation (E) or evapotranspiration (ET) is required to estimate soil
moisture. Several physical and empirical methods have been proposed in the literature
for estimating (PET) or (AET) from meteorological data. Most of the methods contain
empirical coefficients that may be regionally specific. In this study, the methods of
Thomthwaite, Linacre, and Hargreaves were used to estimate evapotranspiration, and
the estimates were compared with the measured values of Class A pan.
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27
Table 4.1. Average elevation and the number of rainfall stations in each grid
cell.
Grid cell
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
Average
Number of
elevation fm) rainfall station
2300.0
2
2100.0
2
1500.0
4
1200.0
2
1200.0
1
1600.0
3
800.0
1
1200.0
8
2400.0
2
2000.0
1
600.0
3
800.0
4
2200.0
7
1700.0
2
1800.0
1
1400.0
3
2400.0
2
2100.0
2
2000.0
1
1000.0
1
2300.0
4
2100.0
4
2000.0
2
1600.0
1
2000.0
2
2300.0
2
1200.0
2
1300.0
3
300.0
3
800.0
2
200.0
3
600.0
6
100.0
4
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
28
Class A Pan. The standard Class A pan is 122 cm in diameter and 25 cm deep, and the
water depth is kept up between 17 and 20 cm. The pan is placed 15 cm above the
ground on a spaced-wood platform. Monthly totals of evaporation from the standard
Class A pan are published by the Hydrology Division, Ministry of Agriculture and
Water, Kingdom of Saudi Arabia. The water-level gage is read daily to the nearest 0.1
mm. The Class A pan has been used as an index of PET. Class A pan evaporation
rates are summarized in Table 4.2 for stations in southwestern Saudi Arabia.
Table 4.2. Class A pan evaporation rate (mm/day) for stations in southwestern
Saudi Arabia (1987).
Station
Jan
Feb
Mar
Apr May
Jun
Alnamas
4.1
3.9
3.1
5.6
7.8
11.9 11.8
7.2 10.8
7.9
5.8
2.7
Abha (Agri.)
4.0
5.9
5.7
7.2
4.7
10.0
8.8
5.0
10.7
7.3
6.2
5.4
Biljuarshy
4.2
4.6
5.0
6.7
5.2
10.6
8.8
5.4
8.9
5.8
5.1
3.8
Sir Lasan
5.0
5.9
5.0
5.6
5.7
7.3
7.1
6.2
5.7
5.7
5.1
4.3
Monthly average
4.3
5.1
4.7
6.3
5.8
9.9
9.1
5.9
9.0
6.7
5.6
4.0
Jul Aug
Sep
Oct Nov
Dec
Thomthwaite Method. In 1948, Thornthwaite developed a method to estimate PET
using mean monthly air temperature. Thomthwaite's method works well where
temperature and radiation are strongly correlated. It has been widely used around the
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
29
world because of its simplicity. However, this method may not be valid for climates
much different from the one in which it was originally developed. In arid areas, it tends
to underestimate real values by about 40%-50% (Hashemi and Habibian 1979). The
formula used to estimate PET is
JOT-wffcX&X^)'.
m
where PET is the potential evapotranspiration (mm), L is the actual day length (hour), N
is the number of days in a month, and Ta is the mean monthly air temperature (°C).
The value of R is defined as
R = 6.75 x 10-73-7.71 x 10-*F + 1.79 x 10-J/+0.49,
(10)
where / is the heat index defined from the sum of the twelve monthly indexes i, which
are defined as
K ff.
These relations are valid when Ta > 0‘C. For Ta < 0*C, Thomthwaite assumed that
PET is zero.
This method may not apply for short time periods as short-term mean
temperature is not an appropriate measure of incoming radiation (Pelton et al. 1960).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
30
Estimated evapotranspiration rates (mm/day), using Thomthwaite's method, are
summarized in Table 4.3 for stations in southwestern Saudi Arabia.
Table 4.3. Estimated evapotranspiration rates (mm/day) using Thomthwaite's
method (1987).
Feb Mar Apr May
Jun
Jul Aug
Sep
Oct Nov
5.1
6.4
8.5
9.2
5.9
3.6
1.7
1.2
1.8
2.7
3.3
3.6
3.0
2.9
1.9
1.2
1.0
1.6
2.2
2.8
3.9
4.1
3.3
3.3
2.3
1.5
1.2
0.5
1.2
2.1
2.8
4.4
4.7
3.9
3.5
2.4
1.6
1.2
0.8
1.2
1.3
2.1
2.8
4.1
4.6
3.9
3.6
2.3
1.6
1.0
Gizan
2.7
3.3
5.3
8.1
10.6 13.9 16.0 14.9 13.4 10.3
9.6
4.0
Khamis Mushat
0.9
1.4
1.7
2.4
3.0
4.1
4.4
3.5
3.5
2.4
1.6
1.3
Serat Abida
1.0
1.2
1.3
1.9
2.7
3.7
3.8
3.4
3.0
2.2
1.5
1.2
Sir Lasan
0.9
1.4
1.6
2.3
2.7
3.5
3.8
3.5
2.8
2.1
1.3
1.1
Monthly average
1.1
1.4
2.0
3.0
3.9
5.3
5.9
5.4
4.7
3.3
2.4
1.5
Station
Jan
Heifa
0.8
1.5
2.5
4.1
Alnamas
0.8
1.1
1.3
Abha
0.9
1.4
Abha (Agri.)
1.0
Biljuarshy
Linacre Method. In 1977, Linacre proposed a method to estimate ET using
meteorological data. It requires information about the latitude, elevation, mean
temperature, and dew-point temperature of the location. The formula is
w
ET-
7007W(100-Z) + 15(7li-7H)
(80^ 7b j
5
(12)
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Dec
31
where ET is the evapotranspiration (mm), Tm = Ta + 0.006h, h is the station's
elevation (m), Ta is the mean temperature, L is the latitude (degrees), and Td is the
mean dew-point temperature.
This method may apply in various climates. Linacre (1977) found that ET
estimated by Eq. (12) differed from measured E T by about 0.3 mm/day based on annual
measurements, and 1.9 mm/day based on daily measurements. Estimated
evapotranspiration rates (mm/day), using Linacre's method, are summarized in Table
4.4 for stations in southwestern Saudi Arabia.
Table 4.4. Estimated evapotranspiration rates (mm/day) using Linacre's method
(1987).
Apr May
Jun
Station
Jan
Feb Mar
Heifa
5.3
5.4
6.8
8.5
9.9 12.7
Alnamas
4.2
5.1
4.7
5.7
6.7
Abha
4.7
5.3
4.9
5.7
Abha (Agri.)
5.4
4.4
4.4
Biljuarshy
5.3
5.8
Gizan
5.4
KhamisMushat
Jul Aug
Sep
Oct Nov
Dec
14.3
13.5
12.3
9.4
7.9
6.3
9.1
9.4
7.3
8.9
6.6
5.9
5.7
6.8
9.3
9.1
7.3
8.4
6.8
6.1
4.9
5.4
6.5
8.9
8.9
7.7
7.9
6.2
5.7
5.2
5.8
7.1
7.8 10.2
9.8
8.5
8.3
6.4
5.8
6.3
5.4
6.2
7.3
7.6
8.2
8.8
8.8
8.0
7.8
7.7
6.0
5.1
6.2
5.6
6.6
8.2 11.2 10.9
8.4
10.3
8.0
6.9
5.8
Serai Abida
5.4
5.4
4.9
5.9
7.2 10.6 10.6
8.7
10.4
8.7
8.1
6.9
SirLasan
4.9
5.6
5.3
6.3
7.0
9.8
9.4
8.0
8.3
7.1
5.9
4.8
Monthly average
5.0
5.4
5.4
6.5
7.5 10.1
10.1
8.7
9.2
7.4
6.7
5.8
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32
Hargreaves Method. In 1974, Hargreaves developed a method for estimating PET
using mean monthly temperature. The equation is
PET=M F(l.8Ta+32 )CH,
(13)
where PET is in mm/month, MF is a monthly latitude-dependent factor, which is given
in a table, Ta is the mean monthly temperature (°C), and CH is a correction factor for
relative humidity (RH). The value of CH can be obtained from the formula
CH= 0.166(100-R H ) 1/2
CH - 1.0
if RH is greater than 64% and
if RH is less than or equal to 64%.
This method may apply over a wide range of climatic conditions. Hargreaves
(1974) evaluated his method by regression analysis using lysimeter data from several
locations around the world. Estimated evapotranspiration rates (mm/day), using
Hargreaves' method, are summarized in Table 4.S for stations in southwestern Saudi
Arabia.
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33
Table 4.5. Estimated evapotranspiration rates (mm/day) using Hargreaves'
method (1987).
Station
Jan
Feb Mar
Apr May
Jim
Jul Aug
Sep
Oct Nov
Heifa
3.0
3.8
4.9
6.1
6.8
5.6
7.6
7.4
6.1
4.7
3.5
3.0
Alnamas
2.4
3.0
3.4
4.5
5.4
5.9
6.0
5.5
4.9
3.7
2.8
2.4
Abba
2.8
3.5
3.7
4.9
5.7
6.4
6.4
5.8
5.3
4.2
3.2
2.8
Abba (Agri.)
2.8
2.8
3.4
4.5
5.6
6.6
6.7
6.1
5.4
4.2
3.3
2.8
Biljuarshy
2.6
3.3
3.9
5.0
5.8
5.0
6.7
6.1
5.4
4.0
3.2
2.6
Gizan
3.6
4.0
5.3
6.7
7.4
7.6
7.8
7.5
6.5
5.7
4.8
3.7
Khamis Mushal
2.8
3.6
4.2
5.2
5.9
6.5
6.5
5.9
5.5
4.3
3.3
2.9
Serai Abida
2.7
3.3
3.8
4.8
5.6
6.2
6.2
5.8
5.1
4.1
3.2
2.8
SirLasan
2.7
3.4
4.0
5.0
5.6
6.1
6.2
5.9
5.1
4.0
3.1
2.7
Monthly average
2.8
3.4
4.1
5.2
6.0
6.2
6.7
6.2
5.5
4.3
3.4
2.9
Dec
Estimation o f Evapotranspiration
In this study, the methods of Thomthwaite, Linacre, and Hargreaves were used
to estimate the mean monthly PET. The estimated values of PET using the above
methods were compared with measured Class A pan evaporation rate. Tables 4.2-4.5
illustrate that evaporation estimated using Thomthwaite's method is less than
evaporation estimated by other methods, as Hashemi and Habibian (1979) found for
arid areas, with a minimum value of 1.1 mm/day in January and a maximum value of
5.9 mm/day in July. Thomthwaite's and Hargeaves' methods estimate that the
maximum value for PET occurs in July, whereas the Class A Pan and Linacre's
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34
methods indicate that there are two maximum values for PET\ which occur in June and
September. August has a lower value than July and September, which is demonstrated
by the Class A Pan and Linacre’s methods.
In this study, it is found that Linacre's method produced the same patterns as the
Class A Pan. Therefore, Linacre's method will be used to estimate evapotranspiration.
The daily PET value for each method can be obtained from Figure 4.2.
Soil Moisture Model
The temporal and spatial patterns of soil moisture, using precipitation and
estimated AET data, are needed as "ground truth." Soil moisture value for day i over
the entire study period at a given grid cell was computed, in millimeters, using the
following model:
SM, = SM m + P -(A E T+ R ),
(14)
where SM^ is the previous day's soil moisture, P is the precipitation amount, AET is the
actual evapotranspiration, and R is the runoff outflow. In the region and the period of
study, the runoff outflow R is small and it can be neglected. Therefore, Eq. (14), with a
lower limit for SMt equal to zero, can be written as
SMt = SM,. i + P -A E T .
(15)
The value of AET differs from the value for Pis 7 under most environments as
AET depends on the amount of water available and on such plant characteristics as
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35
10
L in a c r e
ou
c
o
0u
o
o>a
us
T h o m th w a ite
MAR
FEB
MAY
APR
JUL
JUN
SEP
AUG
NOV
OCT
DEC
Month
Figure 4.2. Comparison of estimated daily values of evaporation rates
(mm/day) using Thomthwaite, Linacre, and Hargreaves methods with measured Class
A pan evaporation rate in southwestern Saudi Arabia for 1987.
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36
root depth. AE T is less than PET, whereas PET is approximately equal to the
evaporation from a large free-water surface, such as a lake. The relation between AET
and PET has been the subject of much discussion in the literature (Griffiths 1982).
Most researchers agree that the value of AET is about the same as the value of PET
when adequate moisture is available; they also agree ihatAET is close to zero when
moisture is low. However, they differ in their opinion about the ratio of AET to PET
between these two points.
In this research, two relations between AET and PET are adopted. The first is
that
=
(16)
(i.e., A E T equals PET when SMUI is greater than zero, and AET equals zero when SMU
is zero). The second is that
AET _ ln U w + 1)
PET
ln(101) ’
t
}
where Aw is the available-water. Here, the field capacity is assumed to be 100 mm
(McFarland 1992).
The calculated soil moisture using Eq. (16) will be called Method 1 (SMI), and
using Eq. (17) will be called Method 2 (SM2). Figure 4.3 shows scatter plots of SMI
versus SM2 for all soil moisture data (number of observations [iV] = 3034). High
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37
Figure 4.3. Scatter plots of soil moisture (mm) SMI versus SM2.
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38
correlation occurred between the two methods (R = 0.9), and SMI values were less than
or equal to the values of SM2.
The value for soil moisture at about 6:00 a.m. and 6:00 p.m. local solar time is
needed to compare it with the SSM/I data. In summer, rain occurs in the afternoon
most of the time. The equation used to calculate soil moisture at satellite descending
overpass time (Node D, about 6:00 p.m. local solar time) is
SMi = (SM^ -A E T)+ P ,
(18)
with the minimum value inside the parenthesis equal to zero. The equation used to
calculate soil moisture at the satellite ascending overpass time (Node A, about 6:00 a.m.
local solar time), on the other hand, is
SMi =SMi-i.
(19)
The initial value for SM-, for each grid cell is selected to be zero after five dry days.
Ground Temperature
Because of the topographical difference in each grid cell, elevation corrections
for temperatures are needed. The regression analysis of monthly mean temperature and
station's elevation for available stations (twelve stations) for each month (July September, 1987) shows that R2 (the R 2values are the square of the linear correlation
coefficient) are large. For maximum temperatures, the coastal station (Gizan) has been
excluded. When Gizan data are used, R2is lower in value (0.78 for July, 0.82 for
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39
August, and 0.88 for September) than without the Gizan data (0.94 for July, and 0.97
for August and September). This is caused by the sea breeze during the day resulting in
a lower maximum temperature. For minimum temperature values, all the stations were
used in the regression analysis and R2are 0.72,0.89, and 0.73 for July, August, and
September, respectively.
The plots of maximum and minimum temperatures versus elevation for July,
August, and September, 1987, respectively, are illustrated in Figures 4.4-4.6. The
elevation average for each grid cell (see Table 4.1) has been approximated using the
maps for the study area prepared by the United States Geological Survey for the
Kingdom of Saudi Arabia from computer-enhanced LANDS AT MSS Band 7 imagery
and Figure 2.1. If the elevation is known and Figures 4.4-4.6 are used, then the
monthly mean of the maximum and minimum temperatures for each grid cell can be
estimated. Daily maximum and minimum temperatures for each grid cell were
estimated from the daily fluctuations of temperature from the mean for each station
located close to the cell. For example, suppose one wants to estimate the maximum
temperature for 10 July, 1987 for Grid Cell 13. From Figure 4.4 the average maximum
temperature for July for Grid Cell 13, which has an average elevation of 2200 m, is
30.7° C. The average maximum temperature for July for Alnamas Station, which is
located close to Grid Cell 13, is 27.5°C. For 10 July, 1987, the maximum temperature
for Alnamas Station is 26.3 °C, which is less than the monthly average by 1.2°C.
Therefore, the maximum temperature for 10 July, 1987 for Grid Cell 13 will be taken
as 1.2°C less than the monthly average (i.e., 29.5'C).
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40
50
S l o p e - -0 .008
I n t e r c e p t -48 .30
N - l l
40
30
20
S l o p e - -0 .005
I n t e r c e p t -28.43
N -12
10
0
100 0
Elevation
2000
3000
(m)
Figure 4.4. Temperature versus elevation for July in southwestern Saudi Arabia
(1987). Black square represents maximum temperature and black triangle represents
minimum temperature.
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41
50
S l o p e " -0 .009
R * ’>0.97
\
I n t e i c e p t - 4 9 .22
N-ll
40
30
20
S l o p e » - 0 .006
I n t e x c e p t "31.33
10
0
1000
Elevation
2000
3000
(m)
Figure 4.5. Temperature versus elevation for August in southwestern Saudi
Arabia (1987). Black square represents maximum temperature and black triangle
represents minimum temperature.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
42
40
S l o p e * -0.008
I n t e r c e p t - 4 6 .40
30
20
S l o p e - -0.005
I n t e r c e p t -26.86
10
0
1000
Elevation
2000
3000
(m)
Figure 4.6. Temperature versus elevation for September in southwestern Saudi
Arabia (1987). Black square represents maximum temperature and black triangle
represents minimum temperature.
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43
The estimated minimum temperature for each grid cell is assumed to represent
the ground temperature at satellite ascending overpass time (about 6:00 a.m. local
time), whereas the estimated mean temperature for each grid cell ( 7^ + Tmin)/2 is
assumed to represent the ground temperature at satellite descending overpass time
(about 6:00 p.m. local time).
Satellite Data
The data used in this investigation contained passive microwave data from the
Special Sensor Microwave/Imager (SSM/I).
Sensor Description
The SSM/I is a passive multichannel microwave radiometer deployed on the
Defense Meteorological Satellite Program (DMSP) Block 5D-2 F 8 satellite, which was
launched into a near-polar orbit in June 1987. The satellite is at an altitude of about
833 km with an orbit period of 102 min. The orbit produces 14 revolutions a day.
The SSM/I scans the Earth in a conical pattern with a swath width of 1400 km
and an earth-incidence angle of 53.1° (see Fig. 4.7). It measures upwelling radiation at
four frequencies (19.35,22.235,37.0 and 85.5 GHz) for both vertical and horizontal
polarization, except for 22.235 GHz, which has only vertical polarization. The SSM/I
covers the globe twice a day, so it is possible to get two passes over Saudi Arabia per
day at about 6 a.m. local solar time (Node A) and 6 p.m. local solar time (Node D).
Further details of the SSM/I is given in the Special Sensor Microwave/Imager User
Guide (Hollinger et al. 1987).
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Figure 4.7. SSM/I scan geometry (adapted from Hollinger et al. 1987).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
45
SSM /I Brightness Temperatures
The SSM/I Orbit Preview System (SMIOPS) package, which is a program
developed for personal computers, was used to determine the location of satellite
overpasses for the areas and dates of interest in this study. SMIOPS needs input
information at the equator, such as the beginning ascending revolution number, Julian
day, and time. The output data are the revolution number, and the start and end time of
the satellite overpass for the area of interest. One should note that the start or end time
found is for the satellite location, not for the area scanned. The SSM/I scans about
1000 km behind the satellite. A correction factor for start and end time is needed.
When the revolution number and start time are known, the NRL SSM/I archived raw
data tape listing, prepared by Bendix Field Engineering Corporation, will show the tape
listing number and file position. Appendix C lists the revolution number, tape number,
file position, and start and end times in seconds for the southwestern region of Saudi
Arabia for the period from 26 June to 30 September, 1987.
The SDR (Sensor Data Record) data files were extracted from the SSM/I
archive tapes using SMIEXT, a FORTRAN program. Then, a data file containing the
latitude and longitude and the seven channels of microwave brightness temperatures
was created by using the SDRCLEAN.FOR program and SDR data files for each orbit.
The SDRCLEAN.FOR was modified so that it could be applied to overpasses over
Saudi Arabia and was renamed as SDRSAUDI.FOR. Finally, the means of brightness
temperatures at each SSM/I frequency were calculated for each grid cell consistent with
"ground truth" in the form of a 0.25° * 0.25° cell.
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46
Problems
During the preparation of the data for this research, several problems were faced
in collecting SSM/I data or ground data.
SSM /I Data
For several interesting cases, the SSM/I data are not available. This is a result
of the swath width (1400 km) of the SSM/I, which does not cover all of the area; or it is
caused by the unavailability of tape for an interesting orbit; or no data for Saudi Arabia
are present in the tape. Another problem in getting data from the tape is that the SDR
file position number is sometimes incorrect. Each tape has temperature data record
(TDR), sensor data record (SDR), and environmental data record (EDR) files. To read
the SDR file, it is necessary to jump over the TDR file by knowing the total number of
files on the tape (4, 5, or 6) as well as the SDR file position from the beginning of the
tape. The number of files to skip (n) can be defined by the following formula: n =
3(m-l), where m is the sum of the file position and the maximum file position in the
tape. When no data are found, another file position number less than the maximum is
selected. Table 4.6 summarizes the available SSM/I data for this study.
Ground Data
Unfortunately, hourly data for rainfall and temperatures were not available, so
24-hour average data were used. Most of the data are printed on paper, and some of the
data are unreadable or missing. Stations that are missing some data are neglected,
which limits the data available for the study, as continuous ground data are needed.
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47
Table 4.6 Available overpass SSM/I data for southwestern Saudi Arabia.
Overpass calendar date (1987)
Node A
193
Node D
192
199
206
215
224
216
225
231
224
232
226
233
234
232
206
225
247
233
234
248
235
249
250
242
256
250
259
251
257
267
272
273
249
258
265
274
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48
CHAPTER V
ANALYSIS AND DISCUSSION
All Data
Linear regression method was used to determine the degree of correlation of soil
moisture caused by rainfall and the seven channels of SSM/I brightness temperatures.
The initial analysis consisted of two data sets of all the 33 grid cells. The first data set
(467 observations) is for the satellite ascending overpass time (Node A, about 6:00 a.m.
local solar time), and the second data set (550 observations) is for the satellite
descending overpass time (Node D, about 6:00 p.m. local solar time). Each data set
contains the grid cell number; Julian date; brightness temperatures (H 9, i/19, V22,
VS1, H il, V85, and H85, where V is vertical polarization, H is horizontal polarization,
and 19, 22,37, and 85 are frequencies 19.350,22.235, 37.000, and 85.5000 GHz,
respectively), ground temperature (7); normalized brightness temperatures (or
emissivities, which are defined at each frequency for each grid cell as Tb IT where Tb is
the brightness temperature at given SSM/I frequency); polarization difference {Vf- H f
where / is the frequency); polarization ratio (PRf, which is defined as [Vf- Hf\ I [Vf+
and two values for calculated soil moisture (SMI and SM2). Statistical regression
analyses for all available data for Nodes A and D were performed. The results of the
analyses are summarized in Tables 5.1 through 5.5.
Table 5.1 shows the maximum (Max), minimum (Min), average (Avg), and
standard deviation (St D) for the SSM/I brightness temperatures, the ground
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49
Table 5.1. Statistical analysis for brightness temperatures (K), surface
temperatures (K), normalized brightness temperatures, polarization
differences (K), and polarization ratios for Nodes A and D for all
available SSM/I data for the period 1 July to 30 September, 1987
over southwestern Saudi Arabia.
Variable
Node D (iV- 550)
Node A (V - 467)
Max
Min
Avg
StD
Max
Min
292.5
279.9
263.8
222.1
282.9
269.7
4.4
H19
V22
292.9
272.5
283.6
7.5
4.4
298.1
286.7
295.2
V37
290.5
279.6
264.4
224.4
279.9
269.8
5.3
7.0
293.3
288.7
305.9
266.7
256.1
6.2
283.3
282.7
278.0
293.8
V \9 -m 9
41.7
6.1
m -m i
40.7
11.3
V19/T
StD
273.5
261.3
Avg
287.4
275.5
270.5
286.1
5.1
3.7
294.8
285.4
247.5
241.9
282.4
273.4
5.6
6.3
294.7
291.0
310.0
192.7
280.4
7.3
4.9
187.1
290.9
275.8
301.0
10.9
11.3
13.2
6.0
29.8
5.2
11.9
4.8
3.3
1.1
10.2
5.5
27.2
2.1
4.5
4.7
2.1
15.2
0.2
9.0
4.7
0.995
0.864
0.963
0.015
0.985
0.906
0.955
0.014
H19/T
0.963
0.728
0.918
0.031
0.956
0.915
V22JT
0.999
0.915
0.965
0.013
0.981
0.866
0.906
0.950
0.016
0.014
V37/T
0.986
0.867
0.953
0.015
0.972
0.815
0.938
0.019
m i/T
0.962
0.645
0.908
0.932
0.019
1.000
0.918
0.962
0.949
0.978
0.803
n s iT
0.735
0.921
0.027
0.016
0.036
m s /T
0.986
0.883
0.946
0.021
0.966
0.627
0.916
0.036
PR19
0.011
0.006
0.024
0.012
PKS1
0.086
0.083
0.019
0.009
0.004
0.021
0.016
0.009
0.008
PRS5
0.021
0.002
0.009
0.010
0.004
0.053
0.049
0.027
0.000
0.008
0.005
F19
m i
V85
HS5
T
V85-M5
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3.8
4.3
2.6
50
temperature, the normalized brightness temperatures, polarization differences, and the
polarization ratios for Nodes A and D for all available SSM/I data for the period from 1
July 30 to September, 1987. For each frequency, the vertically polarized brightness
temperature is greater in value than the horizontally polarized brightness temperature.
This agrees with microwave physics in that the vertical polarization is greater than the
horizontal polarization, and the difference increases with an increasing angle of
incidence until Brewster's angle (about 70*) is reached. The SSM/I viewing angle of
53° provides a marked polarization difference. The minimum values of F85 and 7785
for Node D are less than the minimum values for Node A. This is because channels 85
are more strongly attenuated by precipitating clouds, which exist in the afternoon, than
the other SSM/I channels. For a particular frequency, the standard deviation of the
vertical channel is less than that of the horizontal channel. When the horizontal channel
shows a larger temperature range than the vertical channel, a better pattern
identification will result. The polarization difference is inversely related to the
frequency. For each frequency, the normalized brightness temperatures and the
polarization ratio vary according to the presence of moisture as well as differences in
topography and surface cover. The average polarization ratio is inversely related to the
frequency.
Tables 5.2 and 5.3 list the correlation coefficients of the SSM/I brightness
temperatures, normalized brightness temperatures, and ground truth data for Nodes A
and D, respectively. The highest correlation coefficients occur between the horizontally
and vertically polarized brightness temperatures, also between the normalized
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 5.2. Correlation matrix of SSM/I brightness temperatures, normalized brightness temperatures, and ground truth data
for Node A for all grid cells ( N - 467). The p(0.01) value is 0.119.
V19
H19
V22
V37
H37
V85
H85
V19/T H19/T V22/T V37/T H37/T V8S/T H85/T T
V19
1.000
H19
0.606
1.000
V22
0.904
0.338
1.000
V37
0.926
0.391
0.964
H37
0.749
0.930 0.569 0.632
V85
0.782
0.223
H85
0.799
0.448
0.416 0.897 0.903 0.667 0.964 1.000
0.705 0.195 0.265 0.635 0.093 0.203
H19/T
0.251
0.875 -0.042 0.024 0.721 -0.128 0.075 0.844
V22/T
0.427
0.539 0.346 0.361
V37/T
0.591
H37/T
0.585
0.374
V85/T
0.525
H85/T
0.576
0.407 0.566 0.603 0.565 0.665 0.708 0.659 0.408 0.837 0.875 0.582 1.000
0.586 0.576 0.617 0.735 0.665 0.777 0.657 0.531 0.788 0.841 0.703 0.953
T
0.500 -0.120 0.659 0.612 0.083
SMI
0.050
SM2
0.098
V19/T
SMI
SM2
1.000
1.000
0.932 0.926 0.497
1.000
1.000
1.000
0.556 0.284 0.356 0.910 0.677
1.000
0.468 0.559 0.669 0.438 0.505 0.892 0.638 0.936
0.878 0.132 0.213
1.000
0.829 0.080 0.280 0.863 0.957 0.759 0.765
1.000
1.000
0.647 0.556 -0.550 -0.585 -0.478 -0.313 -0.487 -0.139 -0.091 1.000
0.023 0.064 0.067 0.048 0.101 0.116 -0.057 -0.031 -0.051 -0.025 -0.015 0.032 0.063 0.101 1.000
0.032 0.113 0.113 0.064 0.150 0.165 -0.057 -0.045 -0.050 -0.017 -0.025 0.052 0.087 0.146 0.873
1.000
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 5.3. Correlation matrix of SSM/I brightness temperatures, normalized brightness temperatures, and ground truth data
for Node D for all grid cells (N= 550). The p(0.01) value is 0.109.
V19
HI9
V22
V37
H37
V85
H85
V19/T H19/T V22/T V37/T H37/T V85/T H85/T T
V19
1.000
H19
0.460
V22
0.949 0.424
V37
0.862 0.408 0.909
H37
0.588 0.864 0.618 0.725
1.000
V85
0.633
0.689
H85
0.593 0.477
V19/T
0.471 -0.036 0.466 0.439 0.090 0.412 0.289
1.000
H19/T
0.124 0.689 0.121
0.147 0.551
0.434
V22/T
0.397 -0.073
0.452 0.104 0.473
V37/T
0.537 0.075 0.613 0.741
H37/T
0.343 0.640 0.405 0.558 0.795 0.644 0.708 0.492 0.831
V85/T
0.468 0.161
SMI
1.000
1.000
1.000
0.330 0.730 0.891
1.000
0.686 0.856 0.800 0.974
0.481
0.213 0.301
0.396 0.765
0.579 0.758 0.521
1.000
0.933
1.000
0.352 0.960 0.417
0.668 0.851
0.876 0.621
1.000
0.380 0.885
1.000
0.527 0.685
1.000
0.332 0.692 0.894 0.707
1.000
SMI
0.452 0.328 0.562 0.755 0.663 0.942 0.938 0.506 0.428 0.580 0.819 0.794 0.972 1.000
0.448 0.462 0.406 0.353 0.454 0.170 0.257 -0.578 -0.324 -0.605 -0.366 -0.180 -0.195 -0.093 1.000
-0.248 -0.023 -0.242 -0.220 -0.072 -0.205 -0.163 -0.352 -0.128 -0.333 -0.311 -0.167 -0.252 -0.215 0.127
SM2
-0.244 0.021 -0.232 0.202 -0.025 -0.176 -0.124 -0.370 -0.100 -0.345 -0.309 -0.129 -0.231 -0.182 0.149 0.937
H85/T
T
SM2
1.000
1.000
53
horizontal and vertical brightness temperatures of Channel 85 for both Nodes A and D.
This is because higher frequencies are less sensitive to soil moisture. The FI 9 (F19/T)
was highly correlated with F37 (F37/7). Moreover, the V22 (V22/T) was highly
correlated with F19 (FI9/7) and F37 (F37/7); and F37 was highly correlated to F85
and 7/85, with only a small difference in the correlation coefficient values between
Nodes A and D. High correlation coefficients occur between soil moistures calculated
using Methods 1 (SMI) and 2 (SM2). As noted earlier, SMI values were less than or
equal to the values of SM2 for Nodes A and D. For Node A, the F22 was highly
correlated with F85 and 7/85, however, it was less correlated for Node D. This is
because, as noted earlier, V85 and H85 are more strongly attenuated by precipitating
clouds, which exist in the afternoon, than V22.
Low correlation coefficients (R2 <= 0.14) occur between the SSM/I brightness
temperatures or the normalized brightness temperatures and soil moistures SMI or SM2
(see also Fig. 5.1). This is a result of differences in emissivity among the grid cells.
The average emissivity or normalized brightness temperature (FI9/7) for Channel FI9,
Node D for each grid cell for dry-land, is illustrated in Figure 5.2 (see Appendix D for
the other channels). It shows that emissivity (F I9/7) varies considerably from grid cell
to grid cell with a maximum of 0.9721, a minimum of 0.9396, a mean of 0.9561, and
standard deviation of 0.0092. It is found that grid cells sited in the peak or east side of
the mountains have greater emissivity than grid cells located in the west side. The
exception is for grid cells that are partially covered by vegetation, such as Grid Cells 26
and 23. More correlation coefficients between the normalized brightness temperatures
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
54
50
45
40
G
G
35
H
o
.3
4->
30
®
a
25
o
H
*3>
“
o
a
20
iH 15
0
01
10
V 19/T
Figure 5.1. Soil moisture using method 1 (SMI) versus the normalized
brightness temperature (V19/T) for all grid cells, Node D. Scatter plot shows the poor
relationship between the soil moisture and the normalized brightness temperature when
all grid cells data were used (N = 550).
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
55
0.980
Max - 0.9721
0.975
Min - 0.9396
Mean « 0.9561
0.970
St D - 0.0092
0.965
V19/T
0.960
0.955
0.950
0.945
0.940
0.935
0.930
l i l
1
3
2
I
5
4
7
6
I
'
I
I
| |
I
9 11 13 15 17 19 21 23 25 27 29 31 33
8 10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure 5.2. Average normalized brightness temperature ( V19/T) for each grid
cell (dry-soil) for Node D. N values vary between 17 and 6 .
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
56
and several transformations of the SSM/I brightness temperatures are presented in
Appendix E (Tables E-l and E-2 for Nodes A and D, respectively).
The normalized brightness temperatures data at each grid cell were analyzed,
and the R2values are listed in Tables 5.4 and 5.5 at Nodes A and D, respectively (the R 2
values are the square of the linear correlation coefficient). For any two normalized
brightness temperatures, differences in the R 2values are present between the grid cells.
The normalized brightness temperatures at F85 and H85 were highly correlated at each
grid cell except at Grid Cells 3 and 4 (east side of the mountains) for Node A, and at
Grid Cells 15,19, and 23 (east side of the mountains) for Node D. One can see in
Table 5.3 that normalized brightness temperatures at F85 and HS5 were highly
correlated (R2= 0.95) when all the data were used. However, when each grid cell was
analyzed individually (Table 5.5), the R2values varied from 0.23 in Grid Cell 19 to
1.00 in Grid Cell 7 for Node D. Grid Cell 31, which is the closest cell to the sea, had
high correlation between any two normalized brightness temperatures for Node A;
however, it had poor correlation between normalized brightness temperatures H19 and
V22, P37, F85, or 7785 for Node D. Grid Cells 2, 6 , 8,20,25,28,31, and 32 had high
correlation between any two normalized brightness temperatures for Node A, whereas
Grid Cells 16,25,27,28, and 30 had high correlation between any two normalized
brightness temperatures for Node D. No apparent spatial pattern can be found as
information about soil type, roughness, and vegetation cover is not available.
Therefore, each grid cell should be studied individually to eliminate features that differ
among the cells, such as topography, vegetation cover, soil type, climate, etc.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 5.4. R2values for each grid cell of normalized brightness temperatures for Node A.
Grid Cell#
1
2
3
4
5
6
7
8
9
11
12
13
14
15
V19/T H19/T
0.89
0.94
0.27
0.34
0.11
0.88
0.33
0.76
0.79
0.74 0.55
0.78
0.78
0.79
V19/T V22/T
0.44
0.74
0.52
0.80
0.82
0.92
0.85
0.73
0.85
0.79
0.78
0.74
0.74
V19/T V37/T
0.84
0.93
0.78
0.86
0.94
V19/T H37/T
0.75
0.94
0.43
0.91
0.41
0.80
0.87
0.88
0.80
0.55
0.87
0.42
0.88
0.28
0.74
0.90
0.79
0.74
0.88
0.57
0.69
0.80
0.71 0.52
0.74 0.31
V19/T V85/T
0.68
0.81
0.60
0.58
V19/T H85/T
0.62
0.87
0.64
0.40
0.50
0.88
0.39
0.50
0.77
0.65
HI9/T V22/T
0.42 0.75
0.30
H19/T V37JT
0.85
0.94
0.28 0.15
0.37 0.21
0.81
0.33
0.68
0.69
0.64 0.24
0.51
0.91
0.13
0.89
0.87
0.79
0.04
H19/T H37/T
0.81
0.93
0.75
0.88
0.69
0.96
0.92
0.93
0.92
0.85
H19/T V85/T
0.69
0.86
0.23
0.12
0.22
0.64
0.17
0.64
H19/T H85/T
0.70
0.89
0.52
0.61
0.30
0.68
0.27
V22/T V37/T
0.67
0.79
0.66
0.76 0.78
0.91
V22/T H37/T
0.59
0.77
0.31
0.33
0.41
V22/T V85/T
0.80
0.83
0.59
0.69
V22/T H85/T
0.62
0.72
0.50
0.43
V37/T H37/T
0.63
10
16
17
18
0.62
0.74 0.87
0.65
0.73
0.63
0.83
0.65
0.58
0.87
0.88
0.78
0.97
0.79
0.73
0.75
0.55
0.55
0.55
0.64
0.68
0.82
0.76
0.44
0.79
0.44
0.77
0.60
0.21
0.21
0.32
0.51
0.61
0.66
0.48
0.56 0.56 0.60
0.61
0.73
0.80
0.81
0.53 0.75
0.74 0.89
0.51
0.73
0.88
0.73
0.73
0.94
0.94
0.85
0.93
0.77
0.70
0.54 0.04
0.36
0.36
0.34
0.76
0.47
0.94
0.56
0.81
0.60
0.06
0.19
0.19
0.27
0.91
0.39
0.88
0.71
0.70
0.85
0.74
0.81
0.70
0.81
0.82
0.82
0.73
0.63
0.75
0.77
0.66
0.80
0.34
0.67
0.75
0.62 0.25
0.79
0.79
0.43
0.56
0.57
0.64
0.84
0.91
0.74
0.71
0.57
0.66 0.59
0.57
0.57
0.75
0.73
0.75
0.67
0.82
0.60
0.84
0.54
0.53
0.62
0.67
0.40
0.34
0.34
0.52
0.61
0.44
0.80
0.71
0.91
0.16
0.90
0.66
0.07
0.90
0.76
0.72
0.71
0.64
0.79
0.72
0.71 0.95
0.86 0.94
0.78
0.84
0.76
0.72 0.89
0.80
0.92
0.95
V37/T V85/T
0.88
0.89
0.73
0.51 0.56
0.66 0.65
0.78
0.93
0.82
V37/T H85/T
0.80
0.88
0.75
0.48
0.57
0.82
0.53
0.64
H37/T V85/T
0.75
0.88
0.25
0.23
0.51
0.67
0.21
H37/T H85/T
0.75
0.93
0.71
0.71
0.68
0.75
V85/T H85JT
0.90
0.93
0.57
0.42
0.69 •0.94
0.18
0.75
0.80
0.74
0.67
0.90
0.71
0.79
0.59
0.41
0.46
0.46
0.49
0.70
0.80
0.48
0.06
0.61
0.61
0.23
0.67
0.45
0.95
0.78
0.39
0.59
0.80
0.55
0.08
0.40
0.40
0.16
0.90
0.40
0.91
0.88
0.82
0.89
0.92
0.89
0.92
0.91
0.91
0.82
0.85
0.83
0.95
0.93
0.86
58
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Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Table 5.5. R2values for each grid cell of normalized brightness temperatures for Node D.
Grid Cell #
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
V19/T H19/T
0.79
0.29
0.05
0.01
0.89
0.80
0.90
0.88
0.67
0.31
0.82
0.97
0.80
0.95
0.25
0.91
0.26
0.17
V19/T V22/T
0.79
0.67
0.88
0.75
0.85
0.78
0.94
0.96
0.78
0.81
0.87
0.88
0.73
0.92
0.68
0.88
0.92
0.88
V19/T V37/T
0.86
0.69
0.91
0.69
0.72
0.79
0.71
0.73
0.80
0.89
0.70
0.68
0.78
0.65
0.66 0.74
0.64
0.85
V19/T H37/T
0.75
0.18
0.03
0.76
0.81
0.72
0.73
0.72
0.69
0.76
0.63
0.36
0.33
0.67
0.48
0.48
0.55
0.60
0.68
0.37
0.50
0.53
0.43
0.24 0.80
0.14 0.64
0.38
0.53
0.68
0.66
0.57
V19/T V85/T
0.00
0.38
0.45
0.65
V19/T H85/T
0.57
0.19
0.53
0.22
0.49
0.48
0.56
0.59
0.63
0.66
0.36
0.50
0.50
0.43
0.36
0.64
0.36
0.55
H19/T V22/T
0.59
0.72
0.23
0.03
0.04
0.69
0.67
0.91
0.89
0.62
0.39
0.76
0.81
0.57
0.84
0.08 0.76
0.50
0.09
0.04
0.56
0.73
0.60
0.68
0.60
0.41
0.56
0.61
0.58
0.58
0.13
0.63
0.19
0.18
0.10
H19/T V37/T
H19/T H37/T
0.94
0.92
0.73
0.83
0.60
0.81
0.63
0.72
0.73
0.80
0.65
0.61
0.70
0.58
0.91
0.78
0.66
0.68
H19/T V85/T
0.37
0.21
0.06
0.34
0.31
0.45
0.47
0.45
0.17
0.32
0.41
0.28
0.34
0.03
0.48
0.12
0.15
H19/T H85/T
0.50
0.44 0.00
0.07
0.02
0.34
0.34 0.45
0.47
0.48
0.28
0.33
0.42 0.32
0.34
0.30
0.48
0.14
0.25
V22/T V37/T
0.81
0.63
0.94
0.90
0.76
0.86 0.71
0.80
0.87
0.89
0.89 0.72
0.79
0.72
0.85
0.66
0.68
V22/T H37/T
0.64
0.18
0.06
0.00
0.74
0.81
0.72
0.80
0.91
0.86 0.62
0.90
0.90 0.66
0.77
0.04 0.82
0.33
0.19
V22/T V85/T
0.53
0.52
0.83
0.74
0.55
0.60
0.60
0.67
0.73
0.68
0.66
0.75
0.57
0.60
0.49
0.82
0.49
0.47
V22/T H85/T
0.74
0.53
0.60
0.53
0.83
0.38
0.35
0.23
0.51
0.36 0.71
0.64 0.60
0.67
0.74
0.68
0.66
0.78
0.50
0.02
0.51
0.00
0.55
V37/T H37/T
0.99
0.93
0.89
0.75
0.98
0.98
0.09
0.63
0.55
0.69
0.75
0.88
0.84
0.89
0.89
0.86
0.83
0.81
1.00 0.91
0.94 0.83
0.94
V37/T V85/T
1.00
0.74 0.95
0.98
0.91
0.35
0.92
0.94
0.90
V37/T H85/T
0.70
0.66 0.72
0.58
0.88
0.77 0.95
0.90
0.85
0.85
0.80
0.94 0.80
0.91
0.40 0.91
0.89
0.84
H37/T V85/T
0.37
0.26
0.07
0.00
0.86
0.58 0.94
0.85
0.74
0.52
0.78
0.93
0.66
0.88
0.11
0.77
0.61
0.50
H37/T H85/T
0.48
0.57
0.22
0.13
0.87
0.62
0.94
0.86
0.79
0.67
0.78
0.92 0.68
0.89
0.17
0.77
0.67
0.67
V85/T H85/T
0.96
0.79
0.93
0.76
1.00
0.98
1.00
1.00
0.98
0.96
1.00
1.00 0.97
1.00
0.46
0.99
0.98
0.94
60
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61
Correction for Precipitating Clouds
The presence of precipitating clouds will influence brightness temperatures, as
described in Chapter n. Therefore, data with precipitating clouds should be excluded
from the analysis. Unfortunately, hourly rainfall information is not available for this
research, so daily rainfall had to be used. Ideally, a method is needed to identify the
existence of precipitating clouds at the time of the satellite overpass. The existence of
precipitating clouds will mask radiation emitted from the surface, and the scattering
effect of rain and cloud droplets increases with increasing frequency.
For SSM/I frequencies, the F85 and HS5 channels are more strongly attenuated
by precipitating clouds and the FI 9 and HI 9 channels are least affected by the presence
of precipitating clouds. TheH85 channel measurements will be scattered by
hydrometeors in the atmosphere in a similar manner as the F85 channel. The existence
of precipitating clouds over the study area likely can be determined through an
examination of the F85 and H19 channels. The lower brightness temperature of the
F85 channel may indicate the presence of precipitating clouds. This is because V85 is
strongly attenuated by precipitating clouds. Figure 5.3 shows the data for H19 - F85
versus ground temperatures for Node D. It shows the lowest brightness temperature of
F85 (HI 9 - F85 > 0), most probably over the area of rain at the time of die SSM/I
overpass. The reported rainfall for a 24-hour total is written above the low F85
brightness temperatures. Therefore, the existence of precipitating clouds will be
examined only by using SSM/I brightness temperatures (HI 9 and F85 channels). If
HI 9 - F85 > 0, then the data will be excluded from the analysis. For Node A, it is
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
62
80
7.2
x
2 9 .6
x
60
1.4
x
0 .1 .2
0.2
x
40
3 .1
9 .5
x
tt
X X
1.1
x
1 0 .8
X
6 .5
x
x
0 .4
x
lf>
00
6.3
0.2
20
>
o.*
X
5 .2
I
<T\
iH
2;3.o
o._
x X
x ^ .2
0.0X
EC
x
0
4.2
x
3; 3
X «-,%.3
2.0
K8 !W ,
O tt» A tB X 1 . 6
*A
X.*'
x_
<* M
Xx X X v X
-
20-
290
I
292
1
294
I
296
I
298
I---------- 1---------1-----------1--------- 1------300
302
304
306
308
310
Ground Temperature (K)
Figure 5.3. Scatter plots of ground temperatures versus H 19 - F85 for Node D.
The reported rainfall for a 24-hour total is written above H19 - V85 > 0.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
63
found that all the data had H19 - F85 < 0, so all the data will be used in the analysis;
however, for Node D several data points had H19 - F85 > 0. This agrees with the
synoptic situation over southwestern Saudi Arabia at this time of year, namely, that
clouds form and rainfall occurs in the afternoon over the region.
Case Studies
In mountain regions, the effects of surface inhomogeneities such as variation of
soil type, vegetation cover with height, scale of roughness, and terrain slope have a
strong influence on surface-emitting radiation. These influences are difficult to
incorporate properly into models for areas greater than the spatial resolution of the
satellite. To eliminate the influences among the grid cells, each grid cell should be
studied individually. To investigate the response of the normalized brightness
temperatures of SSM/I to soil moisture, two case studies were conducted. The first case
study is for Node A, and Grid Cell 16 was chosen; the second case study is for Node D,
and Grid Cell 12 was chosen. These two grid cells were selected for their high range of
soil moisture (20 mm), several SSM/I observations available immediately after rainfall,
and several ground rainfall stations (three rainfall stations in each grid cell) that could
provide better "ground truth" data.
For each grid cell, a time series was developed for the entire period of SSM/I
data availability, that is, from 1 July to 10 September, 1987 (days 182-273 on the Julian
calendar). Figures 5.4 and 5.5 show the responses of the normalized brightness
temperatures to the soil moisture caused by rainfall in Grid Cells 16 and 12 for Nodes A
and D, respectively. For all frequencies, the normalized brightness temperatures were
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
64
0.99
18
12-
-0.96
S
10-
-0.95
^-0.94
-0.93
Brightness
-0.97
Normalized
14-
Temperatures
16-
-0.92
0.91
182
193
204
+
X
215
226
237
Julian Day, 1987
V19/T
*
B19/T
H37/T
A
V85/T
248
□
259
270
V37/T
Figure 5.4. Time series plots of rainfall and the normalized brightness
temperatures for Grid Cell 16, Node A, between 1 July and 30 September 1987. The
line plot is the rainfall.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
65
20
18-
-0.85
-
0.8
Brightness
-0.9
Normalized
14-
Temperatures
-0.95
16-
-0.75
0.7
182
193
204
+
X
226
237
215
Julian Day, 1987
V19/T
H37/T
*
H19/T
▲
V85/T
248
□
259
270
V37/T
Figure 5.5. Time series plots of rainfall and the normalized brightness
temperatures for Grid Cell 12, Node D, between 1 July and 30 September 1987. The
line plot is the rainfall.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
66
0.990
0.980-
0.970-
0.960si
a9)
H
5
0.950-
0.940-
0.930-
0.920
V19/T
H19/T V22/T V37/T H37/T V85/T H85/T
Normalized Brightness Temperatures
I
I Dry
N et
Figure 5.6. Average of normalized brightness temperatures with respect to
ground temperature for dry soils (SMI = 0 mm) and wet soils (SMI >5.0 mm) for Grid
Cell 16, Node A.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
67
0.960
0.950-
0.940-
0.930-
0.920-
>
0.910-
0.900-
0.890-
0.880
V19/T H19/T V22/T V37/T H37/T V85/T H85/T
Normalized Brightness Temperatures
I
I Dry
Wet
Figure 5.7. Average of normalized brightness temperatures with respect to
ground temperature for dry soils (SMI = 0 mm) and wet soils (SMI > 5.0 mm) for Grid
Cell 12, Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
68
reduced in value after rainfall; they then increased when it became dry. Figures 5.6 and
5.7 show the average of normalized brightness temperatures, or emissivities, for dry
(when SMI = 0.0 mm) and wet soils (when SMI > 5.0 mm) for both grid cells,
respectively. For all frequencies, the average normalized brightness temperatures for
wet soils are lower than the average normalized brightness temperatures for dry soils.
This agrees with the fact that, as soil moisture increases, the dielectric constant
increases and emissivity decreases.
Basic statistical analyses for brightness temperatures, ground temperatures,
normalized brightness temperatures, polarization differences, and polarization ratios for
the two grid cells for dry (SMI = 0.0 mm) and wet soils (SMI > 5.0 mm) are
summarized
in Tables 5.6 and 5.7. The averages of the brightness temperatures,
normalized brightness temperatures, and ground temperature are lower for wet soil than
that for dry soil for both Node A and D, except for the average ground temperature for
Node A. The standard deviations for the wet period are greater than the standard
deviations for the dry period for Node D. This is because of the existence of
precipitating clouds at the time of the satellite overpass. The average polarization
difference values for Channels 19 and 37 for the wet period are greater than the values
for the dry period for Node A; however, they are lower than the values for the dry
period for Node D.
The averages of the brightness temperatures and the ground temperature are
lower for Grid Cell 16 than the values for Grid Cell 12 for dry soils. This is because
Grid Cell 16 has an average elevation above the sea level (1400 meters) that is greater
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
69
Table 5.6. Statistical analysis for brightness temperatures (K), ground
temperature (K), normalized brightness temperatures, polarization
differences (K), and polarization ratios for Grid Cell 16, Node A.
Variables
Max
Dry
Min
F19
285.5
HI9
277.3
282.0
271.3
V22
V37
286.5
282.9
281.6
278.8
m i
278.2
F85
m s
286.5
283.7
270.3
282.4
T
296.3
277.5
288.2
V19-m9
11.1
m -m i
v%5-ms
Wet
Avg
StD
Max
Min
Avg
StD
1.2
283.4
281.8
282.4
2.1
273.9
271.6
273.0
0.7
1.0
1.6
1.6
284.5
281.0
282.9
279.2
283.9
279.8
0.7
0.8
274.4
2.5
273.1
271.5
272.3
0.7
284.7
281.0
1.5
2.2
285.1
282.2
282.7
278.5
284.0
280.5
1.0
1.5
293.2
3.2
296.8
294.9
0.8
6.9
8.9
1.3
10.2
8.0
295.8
9.4
8.5
4.7
6.7
1.2
8.7
6.2
7.5
1.0
5.5
2.8
3.7
0.8
4.2
2.9
3.5
0.5
VX9/T
0.979
0.960
0.968
0.007
0.956
0.954
0.955
0.001
m 9 /T
0.947
0.933
0.938
0.005
0.926
0.921
0.923
0.002
V22/T
0.979
0.969
0.007
0.962
0.958
0.002
0.001
283.8
274.9
284.2
281.0
1.0
VSHT
0.968
0.958
0.954
0.958
0.005
0.947
0.945
0.960
0.946
m i/T
0.941
0.930
0.936
0.003
0.924
0.918
0.921
0.003
VS5/T
0.965
0.953
0.971
0.953
0.956
0.942
0.960
0.949
0.003
0.958
0.006
0.004
0.964
m s /T
0.981
0.965
PR19
0.020
0.012
0.016
0.002
0.018
0.014
0.017
0.002
PRS1
0.015
0.008
0.012
0.002
0.016
0.011
0.014
0.002
0.010
0.005
0.007
0.001
0.007
0.005
0.006
0.001
PR&5
0.005
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
70
Table 5.7. Statistical analysis for brightness temperatures (K), ground
temperature (K), normalized brightness temperatures, polarization
differences (K), and polarizations ratios for Grid Cell 12, Node D.
Dry
Variables
Wet
Max
Min
Avg
StD
Max
Min
Avg
StD
R9
293.5
288.3
2.0
291.3
280.5
281.3
288.6
283.1
2.6
284.0
289.9
288.2
273.9
290.0
287.3
1.6
1.1
m
285.3
291.4
289.9
284.7
277.4
4.3
H \9
291.0
283.2
278.3
262.2
283.7
277.2
3.7
4.7
9.2
m i
283.8
277.8
281.3
2.4
282.6
257.9
271.8
9.0
m
m s
291.8
288.0
283.1
279.8
288.5
285.0
3.5
3.1
290.6
288.5
245.3
242.3
274.1
271.3
16.5
16.4
T
306.0
305.7
304.4
7.0
8.7
6.0
305.7
7.3
1.0
8.2
0.1
0.5
307.4
R9-7/19
305.9
7.9
m -m i
6.7
4.4
5.3
2.8
6.0
0.5
7.0
4.3
5.4
3.6
0.6
4.5
2.0
2.8
0.9
0.8
V19IT
0.961
0.943
0.955
0.007
0.954
0.920
0.931
0.012
m 9 /T
0.934
0.920
0.929
0.006
0.930
0.896
0.907
0.011
V22JT
0.958
0.951
0.944
0.951
0.942
0.005
0.949
0.928
0.013
0.010
0.943
0.913
0.860
0.907
0.889
0.028
0.027
F22
n s-m s
V31/T
0.926
1.0
m i/T
0.929
0.908
0.923
0.008
0.925
0.846
VZSIT
0.954
0.926
0.946
m s /T
0.944
0.805
0.795
0.897
0.888
0.052
0.052
0.014
0.935
0.014
0.951
0.944
PR19
0.915
0.012
0.012
0.012
0.001
0.015
0.011
0.013
0.002
PR'S!
0.012
0.009
0.005
0.013
0.008
0.008
0.003
0.002
0.008
0.001
0.001
0.010
PRS5
0.011
0.006
0.005
0.002
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71
than the average elevation of Grid Cell 12 (800 meters), and the measurements occur in
the early morning (Node A) for Grid Cell 16 and in the afternoon for Grid Cell 12
(Node D). However, for wet soils the average brightness temperatures for Grid Cell 12
are less than the average brightness temperatures for Grid Cell 16 for Channels 22,37,
and 85. The presence of precipitating clouds (usually rainfall occurs in the afternoon)
causes this influence on the brightness temperatures. For each frequency, the vertically
polarized brightness temperatures are greater than the horizontally polarized brightness
temperatures for both dry and wet soils, which agrees with the difference in behavior
between the vertical and horizontal polarizations.
The quantitative temporal analysis involved correlating the computed soil
moisture values (SMI and SM2) with the seven SSM/I brightness temperatures and with
various transformations of the SSM/I brightness temperatures using all the available
data and the filtered data when no precipitating clouds were present (i.e., when i/19 VS5 < 0). The various brightness temperature transformations are the normalized
brightness temperatures with respect to the ground temperatures, the ratios or
differences between any two of the seven channels, and the polarization ratios.
The correlation coefficients for the regressions of the soil moisture (SMI and
SM2) and each of the seven SSM/I channels and their transformations for Grid Cell 16
in Node A and Grid Cell 12 in Node D are presented in Tables 5.8 and 5.9,
respectively. For Node A, Table 5.8 shows that SSM/I normalized brightness
temperatures with respect to ground temperature are highly correlated with SMI or SM2
amounts and the SSM/I channel transformations without ground temperature are poorly
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72
Table 5.8. Correlation coefficients between the soil moistures (SMI and SM2)
and several transformations of the SSM/I brightness temperatures for
Grid Cell 16, Node A. The p(0.05) value is 0.554.
Transformations
All data (7/= 13)
SMI
SM2
R9
-0.38
-0.34
7719
-0.43
-0.40
V22
m
-0.14
-0.26
-0.21
H i7
-0.42
-0.40
F85
m s
-0.10
0.00
-0.04
0.06
V19/T
-0.64*
-0.62*
H19/T
-0.80*
V22JT
-0.58*
-0.78*
-0.57*
m iT
-0.68*
-0.64*
H i7/T
-0.90*
-0.87*
F85IT
-0.53
-0.50
m s /T
-0.51
-0.45
H I 9/R 9
F37/R9
-0.31
0.24
-0.31
0.28
H iH V \9
-0.36
-0.36
-0.12
F85/R9
0.42
0.44
m s tv \9
0.39
0.43
V19/V22
-0.36
-0.34
H19IV22
-0.45
-0.44
VZ7/V22
-0.23
-0.19
Hi7IV22
-0.56*
-0.55
V&5/V22
0.06
0.09
m 5 /V 2 2
H\9!V57
0.13
-0.42
0.18
-0.44
HS7/V37
-0.50
-0.52
7/19/7/3 7
0.11
0.11
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73
Table 5.8. (Continued).
Transformations
All data (N - 13)
SMI
SM2
H19IVS5
-0.45
-0.46
V37/VZ5
-0.37
-0.37
//37/F85
-0.53
m sm s
-0.51
0.18
H l9 /m s
-0.50
-0.53
0.23
V37/m S
-0.41
H37/H&5
-0.59*
-0.44
-0.62*
V19-H19
0.30
0.30
V19-V22
-0.34
-0.29
V\9-Hi7
-0.36
-0.26
0.35
m -m
-0.42
-0.44
V\9-M 5
-0.39
-0.43
H19-V22
-0.45
-0.44
H19-V37
-0.41
-0.43
H19-IB7
0.11
0.11
H 1 9 -n 5
-0.45
-0.46
H \9 -m 5
-0.53
V22-VY1
-0.50
0.23
V22-H57
0.57*
0.56*
V19-F37
0.36
0.18
V22-VZ5
-0.06
-0.09
V22-H85
V37-H37
-0.13
0.50
-0.18
0.52
m -n s
-0.37
-0.36
V37-M5
-0.41
-0.44
H57-V%5
-0.51
-0.53
H37-H&5
-0.58*
-0.62*
m -m s
-0.19
-0.23
PR19
0.31
0.31
PR37
0.50
0.52
PRS5
-0.18
-0.23
* Indicates value is statistically significant at p(0.05).
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74
Table 5.9. Correlation coefficients between the soil moistures (SMI and SM2)
and several transformations of the SSM/I brightness temperatures for
Grid Cell 12, Node D. For N = 17, the p(0.05) value is 0.483 and for
N = 9, the p(0.05) value is 0.660.
Transformations
All data (N= 17)
Data whenifl9-F85 < 0 (N*= 9)
SMI
SM2
SM I
SM2
V19
-0.44
-0.48
-0.75*
-0.69*
H19
-0.52*
-0.54*
-0.80*
V22
-0.23
-0.30
-0.75*
-0.67*
vsi
-0.07
-0.09
-0.73*
-0.65
m i
-0.08
-0.10
-0.59
-0.53
F85
0.07
0.07
-0.41
m s
0.08
0.08
-0.45
-0.42
V19/T
-0.52*
-0.52*
-0.85*
-0.79*
m 9 /T
-0.59*
V22JT
-0.31
-0.57*
-0.34
-0.89*
-0.86*
-0.83*
-0.80*
V31/T
-0.11
-0.76*
-0.70*
m i/T
-0.11
-0.12
-0.13
-0.75*
-0.68*
F85/T
0.05
0.06
-0.61
-0.57
0.06
-0.59
-0.36
0.07
-0.58
-0.51
m 9 /V l9
-0.14
-0.07
-0.46
-0.52
F37/R9
0.14
0.13
0.56
0.52
m n v \9
0.14
0.12
0.82*
0.81*
n s iv \9
0.18
0.19
0.63
0.59
m s iv \9
0.19
0.21
0.63
0.63
V\9IV22
-0.43
-0.35
-0.65
-0.59
m 9IV 22
-0.40
m 5 /T
-0.30
-0.76*
-0.73*
V31/V22
0.05'
0.07
-0.32
-0.29
m i!V 2 2
0.05
0.05
-0.21
-0.16
VS5/V22
0.17
0.06
0.06
M 5/V22
0.15
0.16
0.18
m 9 im
-0.14
-0.13
0.05
-0.62
0.09
-0.61
m im i
-0.04
-0.18
0.33
0.37
H \9 /m i
-0.14
-0.12
-0.79*
-0.81*
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75
Table 5.9. (Continued).
Transformations
All data ( N - 17)
Data when 7/19- F85 < 0 (N - 9)
SMI
SM2
SMI
SM2
7/19/F85
F37/F85
-0.18
-0.21
-0.19
-0.23
-0.66*
-0.61
-0.63
-0.56
m ans
-0.20
0.14
-0.24
-0.39
-0.32
0.26
0.00
0.08
-0.20
-0.68*
-0.69*
F37/7/85
-0.19
-0.22
-0.25
-0.55
-0.58
m i/m s
-0.21
-0.25
-0.39
-0.39
m -m 9
0.07
-0.44
0.00
0.33
-0.65
0.40
m sm s
//1 9/7/85
-0.14
-0.35
-0.14
-0.15
-0.19
-0.13
-0.20
-0.86*
-0.64
-0.20
-0.21
-0.65
-0.59
-0.64
H19-V22
-0.38
-0.28
-0.75*
-0.72*
7/19-F37
7/19-7/37
-0.14
-0.13
-0.59
-0.59
-0.15
-0.12
-0.80*
-0.81*
7/19-F85
7/19*7/85
-0.19
-0.19
-0.19
-0.64
-0.61
-0.20
-0.67*
-0.68*
V22-V37
-0.05
-0.07
0.31
V22-HS7
-0.06
V22-V15
-0.05
-0.16
V22-US5
-0.16
-0.17
-0.19
0.18
-0.06
0.28
0.13
-0.06
-0.06
-0.10
V37-H37
0.01
-0.22
0.13
-0.24
-0.40
-0.44
-0.60
-0.26
-0.56
H37-V&5
-0.23
-0.21
-0.55
-0.59
-0.23
-0.34
-0.28
7/37-7/85
-0.22
-0.26
-0.36
-0.36
F85-7/85
-0.12
-0.23
-0.02
-0.10
PR\9
0.14
0.07
0.46
0.52
PR27
0.04
0.18
-0.33
-0.37
PR&5
-0.14
-0.26
0.00
-0.08
V19-V22
R9-F37
R9-7/37
R9-F85
R9-7/85
V37-VS5
F37-7/85
-0.57
-0.59
-0.53
-0.84*
* Indicates value is statistically significant at p(0.05).
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76
25
(mm)
20
15
( N m1 3 )
moisture
R a«0.8O2
Soil
10
5
Y— n ^ D - O jO
0
0.910
0.920
0.930
0.940
0.950
H 37/T
Figure 5.8. Relation between normalized brightness temperature (H37/T) and
soil moisture (SMI) for Grid Cell 16, Node A.
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77
correlated 'with SMI or SM2. This indicates that the SSM/I brightness temperature
variables cannot be used to estimate soil moisture over semi-arid mountain regions
without an estimate of the ground temperature. The best correlation between SSM/I
normalized brightness temperatures and soil moisture was obtained between f/37/rand
soil moisture SMI. For SMI >5.0 mm, the normalized brightness temperatures
decrease with increasing soil moisture. This is illustrated graphically in Figure 5.8 for
Grid Cell 16, Node A.
For Node D, Table 5.9 shows that all SSM/I transformations are poorly
correlated with SMI or SM2 when all the data are used. When precipitating cloud
effects are taken into account, the SSM/I normalized brightness temperatures and some
SSM/I transformations become highly correlated with SMI or SM2. The best
correlation between SSM/I normalized brightness temperatures and soil moisture was
obtained between H19/Tand soil moisture SMI. For SMI > 5.0 mm, the normalized
brightness temperatures decrease with increasing soil moisture. This is illustrated
graphically in Figure 5.9 for Grid Cell 12, Node D. Moreover, high correlation
coefficients occur between soil moisture SMI and H31IV19 or V19-H31. This indicates
that either one of these transformations of SSM/I brightness temperatures can be used to
estimate soil moisture over a semi-arid mountain region without an estimate of ground
temperature. The linear regression results for the above analysis are summarized in
Table 5.10.
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78
20
15
o
3
4J
H
ffi
10
•rl
0
E
-H
0
w
5
K 3-0 .350
A ll
data
0
0.890
0.900
0.910
0.920
0.930
0.940
H19/T
Figure 5.9. Relation between normalized brightness temperature (HI9/7) and
soil moisture (SMI) for Grid Cell 12, Node D. Black square represents presence of
precipitating clouds.
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79
Table 5.10. Regression analysis. The dependent variable is soil moisture (SMI).
For Node A, all data were used. For Node D, the data with H19-V85
< 0 were used.
16
16
A
Independent
variable
H19IT
A
m iIT
-674.25
-805.48
633.36
754.19
12
12
12
12
12
D
D
D
D
m 9 IT
V22JT
-475.85
-669.23
m n v \9
H \9 tm i
1952.03
-1291.72
443.88
637.68
-1884.98
1302.77
0.63
9
9
9
D
H9-H37
-6.76
67.15
0.75
9
Grid cell
Node
Slope
Intercept
R2
N
0.64
13
0.80
13
0.79
0.74
9
0.68
Test Case
The results of this research indicate the general conditions that are necessary for
the SSM/I to infer soil moisture. It is useful to examine the results obtained above;
therefore, the results of Grid Cell 12 will be tested. Unfortunately, the number of
SSM/I data points is limited and all data for Grid Cell 12 were used; therefore, the data
for Grid Cell 8, which is adjacent to Grid Cell 12, were chosen for this test. Grid Cell
8, Node D, had 16 data points that will be tested as independent data for the results of
Grid Cell 12.
First, the existence of precipitating clouds at the time of the SSM/I overpass will
be determined through an examination of H19 - F85. It is found that 8 data points had
HI 9 - V85 > 0, which are excluded from the analysis. Second, the best single SSM/I
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80
channel for a surface soil moisture investigation at satellite descending overpass time
will be used. The normalized brightness temperatures in Channel i/19 with respect to
ground temperature (i/19/7) were found to be the best channel (Table 5.10). The
formula is
SMmm = 443.88(1 - 1 . 0 7 ^ ) ,
where SMHlgrT is the soil moisture estimated using normalized brightness temperatures
ofH19. The results are shown in Table 5.11. The soil moisture values obtained from
the above formula may have negative values. Because soil moisture cannot be
negative, these values can be set equal to zero. The soil moisture values obtained from
using normalized brightness temperatures (i/19/7) have similar patterns to the values
(SMI) and (SM2) obtained using ground observations. It is suggested that the
normalized brightness temperatures (7/19/7) can be used to classify such soil moisture
into a quantitatively defined categories.
Table 5.11. Comparison of calculated soil moisture (mm) SMI and SM2 using
ground observations with SMmgfr using normalized brightness
temperatures (i/19/7).
0.1
0.0
0.1
0.0
10.5
0.4
0.0
15.5
SM2
0.1
0.0
0.1
0.0
13.8
0.3
0.0
15.7
1.6
-0.7
1.0
5.3
3.9
0.4
10.9
SMmwr
ot
SMI
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81
CHAPTER VI
CONCLUSIONS AND RECOMMENDATIONS
Summary of Results
In this study, the methods of Thomthwaite, Linacre, and Hargreaves were used
to estimate the mean monthly PET. The values of PET estimated by Thomthwaite's
method were found to be less than the values estimated by other methods, as Hashemi
and Habibian (1979) found for arid areas. The maxim um value of PET estimated by
Thomthwaite's and Hargeaves' methods was in July, whereas the Class A Pan and
Linacre's methods indicated that there are two maxima of PET in June and September.
August had a lower value than July and September, which was demonstrated by the
Class A Pan and Linacre's methods. In this study, it was found that Linacre's method
had the same patterns as the Class A Pan. Therefore, Linacre's method was used to
estimate the evapotranspiration. Two models of soil moisture {SMI and SM2) were
used to estimate the soil moisture patterns, which were used as "ground-truth," by
means of daily precipitation amounts and empirical evapotranspiration formulae
(Linacre method). High correlation occurred between the two methods (R = 0.9) and
SMI values were less than or equal to the values of SM2. The high correlation between
the two methods of soil moisture suggested that there are no significant differences
between the two relationships between AET and PET which are adopted in this study.
This is because of the high rate of evaporation and low amount of rainfall in the study
area.
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82
The quantitative temporal analysis involved correlating such SSM/I variables as
brightness temperatures, normalized brightness temperatures with respect to ground
temperature or another frequency of brightness temperatures, polarization differences,
and polarization ratios with the computed soil moisture values. Because of the effect of
variations in topography, surface cover, scale of roughness, and terrain slope, low
correlation occurred between the SSM/I variables and soil moistures when all the grid
cells were studied together. Therefore, each grid cell was studied individually to
eliminate features that differ among the grid cells. Two case studies were performed,
first at Node A and second at Node D.
From the study cases, it was found that the normalized brightness temperatures,
or emissivities, with respect to ground temperatures responded to the change in soil
moisture caused by rainfall. For all frequencies, the normalized brightness
temperatures were reduced in value after rainfall. It was found that the average of
normalized brightness temperatures for wet soil {SMI > 5.0 mm) is lower than the
average of normalized brightness temperatures for dry soil {SMI = 0.0 mm) For SMI
>5.0 mm, the normalized brightness temperatures decrease with increasing soil
moisture. For SMI < 5.0, no apparent pattern was found. Two relationships were
found between the near-surface soil moisture and the normalized brightness
temperatures. The first relation is that SM is proportional to [1.00 - 1.07 {H37/T)] at
Node A, and the second relation is that SM is proportional to [1.00 - 1.07 (H19IT)] at
Node D.
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83
Conclusions
The major findings from this research are as given.
Linacre's method was the best method to estimate the mean monthly PET for
southwestern Saudi Arabia.
Any of the two models of soil moisture (SMI and SM2) can be used as the
"ground-truth."
Elevation corrections for ground temperature are needed to normalize the SSM/I
data to estimate soil moisture in near-surface layers.
Poor correlation coefficients occurred between the SSM/I brightness
temperatures and soil moistures when all the grid cell were studied together. Therefore,
each grid cell was studied individually to eliminate aspects that markedly differ among
the grid cells in this mountainous region.
Rainfall information at the time of the satellite overpass can be identified. The
existence of precipitating clouds was determined through an examination of the V85
and .#19 channels. When (#19 - V85) is greater than zero, then the presence of
precipitating clouds at the time of the SSM/I overpass is indicated.
The normalized brightness temperature in Channel #19 with respect to ground
temperature (#19/7) was the best single SSM/I channel to use for a surface soil
moisture investigation at satellite descending overpass time. The normalized brightness
temperature in Channel #37 with respect to ground temperature (7737/7) was the best
single SSM/I channel to use for a surface soil moisture investigation at satellite
ascending overpass time.
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84
The results obtained in this study showed that SSM/I brightness temperatures
can be used to classify near-surface soil moisture over semi-arid mountain regions.
Recommendations
Further testing of the use of passive microwave brightness temperatures as a
means of estimating soil moisture is needed. The results of this study have indicated a
number of additional studies that need to be conducted to understand better the effects
of surface inhomogeneities within the SSM/I footprints. The soil moisture models used
here as "ground-truth" need to be verified by field measurements. For the southwestern
region of Saudi Arabia, radar data and hourly rain gage data need to be collected and
stored for future research.
An effective program of using satellite data to monitor the change in soil
moisture should be initiated in Saudi Arabia. Coordination with the Meteorology and
Environmental Protection Administration (MEPA) in Jeddah and possibly other
governmental agencies should be considered. The effective use of the refined research
methodology will rely on correct data from various sources. Also, consideration should
be given to collaborative studies with various agencies and universities in Saudi Arabia
and other countries.
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85
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88
, and P. H. Harder, 1982: Development of an early warning system of crop
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pp.
, 1992: Personal communication.
Mo, T., T. J. Schmugge, and B. J. Choudhury, 1980: Calculation of the spectral nature
of the microwave emission from soil. AgRlSTARS Report No.
SM-GO-04018, NASA TM-82002, Goddard Space Flight Center, Greenbelt,
Maryland, 66 pp.
Newton, R. W., S. L. Lee, J. W. Rouse, and J. F. Paris, 1974: On the feasibility of
remote monitoring of soil moisture with microwave sensors. Proc. 9th
Symposium on Remote Sensing, E.R.I.M., Ann Arbor, Michigan, 725 -738.
, 1977: Microwave remote sensing and its application to soil moisture detection.
Ph.D. Dissertation, Texas A&M University, College Station, Texas.
, and J. W. Rouse, 1980: Microwave radiometer measurements of soil moisture
content. IEEE Trans., Antennas and Propagation, AP-28, No. 5, 680-686.
, Q. R. Black, S. Makanvand, A. J. Blanchard, and B. R Jean, 1982: Soil moisture
information and thermal microwave emission. IEEE Trans, Geosci. Remote
Sensing, GE-20, No. 3,275-281.
Njoku, E. G., and J. Kong, 1977: Theory for passive microwave remote sensing of
near-surface soil moisture. J. Geophys. Res., 82, No. 20,3108-3118.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
89
, and P. E. O'Neill, 1982: Multifrequency microwave radiometer measurements of
soil moisture. IEEE Trans., Geosci. Remote Sensing, GE-20, No. 3,468-475.
Paris, J. F., 1971: Transfer of thermal microwave in the atmosphere. Rep. under NASA
contract. NGR-44-001-098, Dept, of Meteorology, Texas A&M University, 2
vols., 468 pp.
Pelton, W. L., K. M. King, and C. B. Tanner, 1960: An evaluation of the Thomthwaite
and mean temperature methods for determining potential evapotranspiration.
Agronomy J., 52, No. 7,387-395.
Poe, G. A., and A. T. Edgerton, 1971: Determination of soil moisture content with
airborne microwave radiometry. Summary report 4006R-2, COM-72-10430,
NOAA contract No 1-35378, Hillerest Heights, Maryland, 43 pp.
Schmugge, T. J., G. Gloersen, T. Wilheit, and F. Geiger, 1974: Remote sensing of soil
moisture with microwave radiometers. J. Geophys. Res., 79, 317-323.
, 1978: Remote sensing of surface soil moisture. J. Appl. Meteor., 17, 1549-1557.
, T. J. Jackson, and H. L. McKim, 1979: Survey of methods for soil moisture
determination. NASA-TM-80658, Goddard Space Flight Center, Greenbelt,
Marland, 74 pp.
, 1980: Effect of texture on microwave emission from soils. IEEE Trans., Geosci.
Remote Sensing, GE-18, No. 4,353-361.
, 1983: Remote sensing of soil moisture: recent advances. IEEE Trans., Geosci.
Remote Sensing, GE-21, No. 3, 336-344.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
90
, 1985: Hydrological Forecasting. Edited by M. G. Anderson and T. P. Burt. John
Wiley & Sons, NY.
Tsang, L., and R. W. Newton, 1982: Microwave emission from soils with rough
surfaces. J. Geophys. Res., 87, No. 11,9017-9024.
Ulaby, F., M. Razani, and M. C. Dobson, 1983: Effects of vegetation cover on the
microwave radiometric sensitivity to soil moisture. IEEE Trans., Geosci.
Remote Sensing, GE-21, No. 1,51-61.
Wang, J. R., J. C. Shiue, and J. E. McMurtrey, El, 1980: Microwave remote sensing of
soil moisture content over bare and vegetated fields. Geophy. Res. Lett., 7,
801-804.
Wilheit, T., 1978: Radiative transfer in a plane stratified dielectric. IEEE Trans.,
Geosci. Elec., GE-16,138-143.
Wilke, G. D., 1984: Multispectral passive microwave correlations with an antecedent
precipitation index using the Nimbus 7 SMMR. M.S. Thesis, Texas A&M
University, College Station, Texas.
, and M. J. McFarland, 1986: Correlations between Nimbus-7 scanning
multichannel microwave radiometer data and an antecedent precipitation
index. J. Climatol. Appl. Meteorol., 25, 227-238.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
91
APPENDIX A
MEAN MONTHLY PRECIPITATION (MM)
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Station Long. Lat.
Elev.
D M D M (m)
code
Jan
Feb
Mar
Apr
May
Jun
Jul
Aug
Sep
Oct
Nov
A004
Dec Annual
total
Period of
record
3.0
51.1
21.6
27.2
18.5
3.3
4.8
11.7
:;.3
5.3
2.3
2.8
154.9
1982-86
A005
43 06 18 10 2400.0
42 29 18 12 2200.0
33.0
39.9
74.4
67.8
60.7
13.5
38.9
41.9
<i.4
12.4
7.9
13.0
409.8
1970-86
A006
42 36 18 15 2100.0
14.2
35.1
66.3
48.3
36.1
13.2
28.7
31.2
A.8
8.9
6.9
5.3
299.0
1974-86
A007
80.8
49.0
75.7
92.2
73.4
4.1
23.6
19.8
1.8
16.0
27.2
53.8
517.4
1970-86
A103
42 09 19 06 2600.0
42 47 18 06 2100.0
18.7
28.0
45.1
55.6
58.7
15.6
27.0
57.2
11.9
6.4
335.2
1966-80
43 22 17 50 2350.0
17.0
25.4
47.0
47.7
35.3
0.0
20.9
17.2
7.1
('.0
3.9
A104
4.6
10.8
4.3
230.2
1966-80
A105
16.5
14.3
25.0
37.4
32.8
7.7
8.8
3.2
162.5
1966-80
24.1
45.0
66.5
67.8
54.8
11.7
23.0
9.1
460.3
1966-80
A107
42 34 18 36 2150.0
11.8
8.6
32.7
32.5
44.0
5.4
18.8
88.0
17.4
0.0
25.4
3.8
15.7
2.0
25.2
11.0
A106
43 11 18 14 2060.0
42 29 18 16 2200.0
1.2
2.8
8.4
17.0
200.6
1966-80
A108
8.9
12.3
20.2
29.0
28.1
11.7
14.6
2.0
3.8
13.0
13.0
161.3
1966-80
2.3
11.2
56.2
33.6
22.0
4.7
1.4
19.8
3.0
0.7
1.5
8.4
20.4
180.5
1966-80
A112
42 25 18 31 2300.0
42 54 18 41 1880.0
42 34 18 22 1980.0
9.0
13.1
28.0
28.7
36.5
9.5
35.6
32.3
1.5
1.4
14.4
7.1
217.1
1966-80
A113
42 41 18 38 1700.0
13.0
21.3
32.5
75.3
35.6
13.8
21.0
7.6
f.O
2.0
27.0
6.9
261.0
1966-80
A120
42 10 18 53 2100.0
42 45 18 02 2300.0
42 52 18 19 1900.0
53.0
70.0
80.5
42.8
2.4
16.7
16.0
3.6
6.6
21.7
25.4
379.4
1966-80
38.0
40.7
44.1
54.0
74.5
76.4
14.3
33.5
54.0
12.2
20.1
28.0
12.6
461.7
1966-80
7.7
6.5
29.0
33.4
36.4
6.2
22.5
16.8
3.4
0.9
4.7
11.3
178.8
1966-80
16.4
15.1
28.1
55.9
76.8
10.4
29.1
20.1
4.5
5.6
18.7
17.1
297.8
1966-80
A127
42 20 18 25 2400.0
42 15 18 47 2250.0
38.3
48.3
67.2 104.0
69.5
3.4
28.5
31.0
1.3
7.6
35.7
24.0
458.8
1966-80
B004
42 36 20 01 1020.0
9.1
5.6
23.4
38.6
18.3
1.3
1.0
3.8
0.0
6.4
1.3
5.6
114.4
1970-86
B005
42 32 19 52 1090.0
41 33 19 52 2400.0
7.1
8.6
19.8
26.7
19.8
2.8
5.6
2.0
0.0
11.2
4.8
1.3
109.7
1974-86
B007
78.5
40.4
50.0
69.1
44.2
10.7
21.3
23.4
11.4
23.6
35.1
69.1
476.8
1970-86
B101
41 35 19 54 2330.0
76.7
49.4
36.0
77.3
29.0
8.3
17.1
23.8
1.9
10.7
41.3
45.0
416.5
1966-80
B110
42 53 18 48 1650.0
8.2
3.5
17.3
26.0
15.2
1.2
6.4
5.3
0.1
0.4
7.4
4.8
95.8
1966-80
A110
A121
A123
A124
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Dec Annual
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SA142
9
SA137
Station
code
Long. Lat.
Elev.
D M D M (m)
Jan
Feb
May
fO CO
On ©
Apr
o
Mar
Jun
Aug
o
Jul
Sep
On o C; On
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Nov
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Period of
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94
2
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rf 5
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
95
APPENDIX B
RAINFALL STATIONS
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
96
Station name
Sent Abida
Abha (Agr.)
SirLasan
Station code Longitude
D M S
A004
AOOS
A006
18 10 00
18 12 00
18 15 00
2200
2100
42 09 00
42 47 00
43 22 00
43 11 00
19 06 00
2600
18 06 00
17 50 00
18 14 00
2100
2,350
18 16 00
2200
18 36 00
18 31 00
2150
18 41 00
1880
18 22 00
18 38 00
18 37 00
1980
A007
Al-Jawf
A105
Al-Kam
A106
Al-Mowayn
Al-Tajer
A107
A108
Al-Yaara
A110
Beni-Malik
Beni-Thawr
A112
42 54 00
42 34 00
A113
42 41 00
Sabah
Tenomah
A117
42 16 00
42 10 00
A120
Temniyah
A121
Tindaha
A123
A124
Elevation
(m)
43 06 00
42 29 00
42 36 00
Alnamas
Al-Amir
Al-Haraja
A103
A104
Latitude
DM S
42 29 00
42 34 00
42 25 00
42 45 00
42 52 00
2400
2060
2300
1700
2200
18 53 00
18 02 00
2100
2300
1900
A127
42 20 00
42 15 00
18 19 00
18 25 00
18 47 00
W. Bin Hashbel
A128
42 42 00
18 28 00
Teyban
Hani
A130
A201
42 19 00
42 31 00
18 20 00
18 25 00
*
Al-Sawdah
A203
42 22 00
18 15 00
*
Abala
Tenomah
A206
A211
42 15 00
42 10 00
18 41 00
18 55 00
•
Mala
Shaalian
WadiZuIair
A213
A220
18 10 00
19 10 00
19 13 00
*
A219
42 50 00
42 12 00
42 03 00
Saroo
A221
42 05 00
19 18 00
*
Zaliara
Bishah
A222
B004
42 07 00
42 36 00
19 03 00
20 01 00
2400
Zahra
Belesmer
2400
2250
1780
2440
*
*
*
1020
Heifa
B005
42 32 00
19 52 00
1090
Biljuarshy
B007
41 33 00
19 52 00
2400
Al-Ajaeda
B101
B110
41 35 00
19 54 00
2330
42 53 00
18 48 00
1650
Khayber
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Station name
Station code Longitude
D M S
Latitude
D M S
Elevation
(m)
*
•
Abu-Jenniyah
Thulth Bani Amr
B20S
B216
42 44 00
41 59 00
Adama
B217
41 56 00
19 28 00
19 45 00
Samakh
Al-Alayah
B219
B221
42 48 00
41 54 00
19 20 00
19 32 00
Al-Tarj
42 20 00
42 20 00
42 04 47
19 45 00
Al-Awja
Upper W. Surum
B223
B224
B231
19 15 00
19 59 25
W.Surum &Tabalah
B232
42 07 15
19 56 45
1351
Near J. Idhar
J. Al-Quwayidah
B233
B234
42 24 20
42 14 00
19 55 45
19 53 00
1254
Near J. Samrah
B235
42 06 40
19 46 27
W.Tabalah &Mishfidh
B236
B237
19 46 07
19 36 50
Al-Sikheen
B238
Al-Mudailif
Malaki
J001
SA001
41 59 30
41 58 51
42 02 43
41 03 00
42 57 00
Sabya
SA002
Kwash
Kiyat
19 01 00
1715
1480
1850
1351
*
1467
1235
1712
1733
19 23 34
1938
2214
19 52 00
17 03 00
53
190
42 37 00
17 10 00
40
SA003
SA004
41 53 00
41 24 00
19 00 00
18 44 00
350
30
Abu Arish
SA101
42 50 00
16 58 00
69
Darb
SA102
42 14 00
17 42 00
65
Ardah
SA104
17 03 00
Parik
SA105
43 05 00
41 58 00
18 56 00
223
390
Beysh
SA106
42 32 00
17 22 00
70
Damad
Gam Al-Bahr
SA107
SA108
17 07 00
18 20 00
70
420
J. Fayfe
SA110
42 47 00
41 55 00
43 08 00
17 16 00
860
J. Sala
SA111
SA113
17 03 00
18 32 00
18 00 00
900
450
SA115
43 07 00
42 02 00
41 40 00
Suq Alahad Masarh
Thurayban
SA118
SA120
42 58 00
41 50 00
16 43 00
40
19 26 00
580
Suq Althuluth
SA121
41 48 00
19 16 00
390
Mauarda
SA122
19 07 00
450
Al-Gooz
SA125
41 50 00
42 27 00
17 08 00
5
Mubayil
Qahmah
20
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
98
Station, name
Harub
Station code Longitude
D M S
SA126
Latitude
D M S
Elevation
(m)
WadiDamad
SA129
42 58 00
42 54 00
Jadiyah
Kubah
SA133
42 59 00
16 48 00
45
N .Q ufl
SA135
SA136
43 1400
43 08 00
16 48 00
16 47 00
240
90
Samtah
SA137
42 57 00
16 36 00
40
Tarqush
SA138
SA139
42 01 00
42 02 00
18 38 00
Ghat
19 03 00
570
450
SuqAyban
SA140
43 03 00
17 19 00
305
Wadi Hali
M'khashel
SA142
SA143
41 35 00
43 08 00
18 46 00
16 54 00
90
340
Habil
SA144
42 15 00
18 10 00
408
Rayth
Gizan (Agr.)
Main Itwad
SA145
SA148
SA203
42 48 00
42 32 00
42 20 00
17 37 00
16 55 00
17 46 00
600
10
150
WadiBeysh
Ardah
SA204
SA205
42 36 00
43 05 00
17 34 00
17 02 00
200
223
Qufl
SA209
43 04 10
16 43 10
132
Quran Quraysh
SA221
19 15 25
Wadi Hafyan
SA222
41 48 30
41 49 00
19 38 07
431
751
Al-Bakhara
Mishrif
SA223
SA224
41 41 00
19 31 00
460
41 52 00
19 25 21
651
Jurbash
SA226
41 48 05
19 23 02
533
Al-Irq
SA227
41 42 53
19 21 44
638
Nugmat Shumran
SA228
41 49 17
19 29 15
655
Masid
41 56 44
19 17 02
711
Al-Zahra
SA229
SA230
42 02 40
19 13 02
2633
NearQaradah
SA231
41 35 17
19 12 44
213
Al-Zaharah
SA232
41 53 24
470
Harf Mibrah
SA233
41 47 19
19 09 47
19 07 21
Al-Uqdah
Al-Kha*anah
SA234
41 36 51
19 06 58
190
SA235
SA236
42 01 46
41 29 21
19 05 29
665
Al-Quashah
19 03 22
123
Shaib Alzafir
SA237
41 39 34
19 01 13
185
Jarad
SA238
41 26 19
18 59 49
89
Wadi Sahul
SA239
41 45 00
18 58 03
238
17 27 00
17 10 00
540
150
305
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
99
Station name
Station code Longitude
_____________________________________D M S
Al-Qahman
SA240
41 18 27
SA241
Shaib Sayalah
41 36 05
Latitude
Elevation
D M S_________(m)
18 56 50
36
Ruayli
SA242
43 10 40
18 54 00
16 47 30
198
204
Hajnabah
Raja
SA243
SA244
43 00 15
43 10 15
16 46 00
16 43 30
103
204
SeOfSuqAl-Ahad
SA245
SA246
16 42 25
74
SA247
42 57 30
43 04 00
42 58 50
16 38 20
16 37 00
100
76
Sa-Adiah
Khalfo
SA248
42 52 50
16 33 30
54
SA249
Dijama
SA250
43 05 00
42 47 30
16 33 00
16 32 10
106
22
Muharraqab
SA251
42 58 00
16 31 40
70
Abba
Khamis Mushat
41112
41114
42 39 39
42 48 23
18 13 59
18 17 58
2039
2055
Gizan
41140
42 35 05
16 53 49
7
Al-Hagla
Mujar
* Elevation is not available.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
100
APPENDIX C
LIST OF REVOLUTION NUMBER, FILE POSITION, TAPE NUMBER,
AND START AND END TIMES FOR THE SOUTHWESTERN REGION OF SAUDI
ARABIA
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
101
Date
Jun26,87
Jun27,87
Jun30,87
Jul01,87
Jul02,87
Jul03,87
Jul05,87
Jul06,87
Jul08,87
Jul09,87
JullO,87
Julll,87
Jull2,87
Jull3,87
Jul 17,87
Jul 18,87
Jull9,87
Jul20,87
Jul21,87
Jul25,87
Jul26,87
Jul27,87
Node and
Julian Day
A177
Rev.#
85
D177
D178
A181
D181
A182
D182
A183
D183
92
106
142
149
157
163
170
177
A184
D184
A185
D185
D186
A189
184
191
198
205
219
255
A190
D190
A191
D191
A192
D192
A193
D193
D194
A198
D198
A199
D199
A200
D200
A201
D201
D202
A206
D206
A207
D207
A208
269
276
283
290
297
304
311
318
332
382
389
396
403
410
417
424
431
445
495
502
509
516
523
File position
Tape#
Start time
End time
(sec)_______ (sec)
*
9070
9500
*
54170
54600
53420
3200
53850
*
12190
12620
*
57290
57720
*
11450
11880
*
56590
57020
*
10740
11170
*
55840
56270
*
9990
10420
*
55090
55520
*
9240
9670
*
54350
54780
*
53600
54030
•
12840
12410
*
11660
12090
2201
56770
57200
2201
10910
11340
2206
56020
56450
2206
10160
10590
55270
55700
2208
2211
9420
9880
2212
54560
54990
54230
2216
53800
3233
11820
12300
56940
57420
3220
3219
11120
11550
3224
56230
56660
*
10370
10790
3226
55480
55910
*
9620
10050
*
54730
55160
•
53980
54410
3242
12040
12470
3244
57140
57570
*
11280
11710
3249
56390
56820
*
10580
11010
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
102
Date
Jul28,87
Jul29,87
Jul30,87
Aug03,87
Aug04,87
Aug05,87
Aug06,87
Aug07,87
A ugll,87
Augl2,87
Augl3,87
Augl4,87
Augl9,87
Aug20,87
Aug21,87
Aug22,87
Aug23,87
Aug27,87
Aug28,87
Aug29,87
Node and
Julian Day
D208
A209
D209
A210
D210
D211
A215
D215
A216
D216
A217
D217
A218
D218
D219
A223
D223
A224
D224
A225
D225
A226
D226
A231
D231
A232
D232
A233
D233
A234
D234
D235
A239
D239
A240
D240
A241
D241
Rev. #
File position
530
537
544
551
558
572
622
629
636
643
650
657
664
671
685
735
742
749
756
763
770
777
784
•
*
•
•
848
855
862
869
876
883
890
897
911
961
968
975
982
989
996
*
•
1
1
5
1
1
1
1
1
1
2
1
2
5
2
3
1
1
2
1
2
1
2
1
1
*
*
•
*
*
*
Tape#
*
*
*
*
*
*
3278
3287
3281
3279
3281
3283
3285
3290
3305
3309
3313
3311
3315
3317
3321
3340
3341
3336
3357
3328
3346
3348
3358
3360
3362
3351
*
*
*
*
*
*
Start time
(sec)
55690
9840
54940
9080
54190
53440
11250
56340
10510
55770
9760
55020
9010
54280
53570
11430
56690
10690
55800
9970
55050
9220
54300
End time
(sec)
56120
10270
55370
9510
54620
53870
11490
56770
10920
56180
10170
55430
9410
54690
53980
11840
57100
11100
56220
10210
55470
9630
54710
11640
56720
10890
55980
10140
55230
9390
54510
53770
12160
57270
11410
56520
10660
55770
12050
57150
11300
56400
10540
55650
9800
54920
54190
12420
57530
11670
56780
10920
56030
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
103
Date
Aug30,87
Aug31,87
Sep04.87
Sep05,87
Sep06,87
Sep07,87
Sep08,87
Sep08,87
Sepl3,87
Sepl3,87
Sepl4,87
Sepl5,87
Sep 16,87
Sep21,87
Sep22,87
Sep23,87
Sep24,87
Sep25,87
Sep29,87
Sep30,87
Node and
Julian Day
A242
D242
A243
D243
A247
A248
D248
A249
D249
A250
D250
A251
A251
D251
A256
A256
A257
D257
A258
D258
A259
D259
A264
D264
A265
D265
A266
D266
A267
D267
D268
A272
A273
D273
Rev.#
File position
1003
1010
1017
1024
1074
1088
1095
1102
1109
1116
1123
1130
1130
1137
1201
1201
1215
1222
1229
1236
1243
1250
1314
1321
1328
1335
1342
1349
1356
1363
1377
1427
1441
1448
*
*
1
2
1
1
1
1
1
1
6
1
3
4
2
4
1
8
1
4
5
2
3
1
1
2
1
2
4
1
1
1
5
2
2
3402
3404
3370
3392
3406
3408
3410
3412
2417
3416
3418
3419
3423
3430
3444
3447
3447
3449
3452
3452
3455
3471
3473
3474
Tape#
3476
3478
3481
3482
3484
3488
3504
3504
3507
Start time
(sec)
9910
End time
(sec)
10170
55030
9160
54280
12250
11510
56610
10760
55900
10040
55140
9290
9290
54400
11700
11700
10950
56060
10210
55310
9460
54590
11860
55290
9420
54540
12680
11940
57020
11190
56330
10470
55570
9720
9720
54830
12130
12130
11370
56490
10640
55740
9890
55020
12290
57450
11590
56700
10840
55950
10100
55200
54450
12500
11760
56870
57020
10990
56270
10320
55520
9580
54770
54110
11980
11230
56440
* Data are not available.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
104
APPENDIX D
AVERAGE NORMALIZED BRIGHTNESS TEMPERATURES FOR EACH
GRID CELL (DRY-SOIL) FOR NODE D
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
105
1.00
0.98-
H 19/T
0.96-
0.94-
0.92
0.90-
0 .8 8
1
3
2
5
4
7
6
8
9 11 13 15 17 19 21 23 25 27 29 31 33
10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-l. Average normalized brightness temperature (H19/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
106
l.oo
0.98-
0.96-
781848^888
0.92
0.90-
0.88
1
3
2
5
4
7
6
8
9 11 13 15 17 19 21 23 25 27 29 31 33
10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-2. Average normalized brightness temperature ( V22/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
107
1.00
0.98
0.96
99999999^
0.92-
0.90-
0.88
i
i
1
i
i
3
2
i
i
5
4
v
7
6
i
i
i
i
i
i
I
i
i
t
I
l
I
I
1
I
I
I
1 I
I
I
1
I
I
9 11 13 15 17 19 21 23 25 27 29 31 33
8 10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-3. Average normalized brightness temperature (V37/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
108
l.oo
0.98
H37/T
0.96
0.94
0.92
0.90
0.88
■ r r r r i-r n -r r r r r r r rr r r r i i r r r r r r . r
1
3
2
5
4
7
6
8
9 11 13 15 17 19 21 23 25 27 29 31 33
10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-4. Average normalized brightness temperature (Hil/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
109
1.00
0.98
0.96-
Eh
X
in
00
>
0.94-
0.92
0.90-
o .88
■ r r r r r r r r r r r r r r r r r r r r r r r r r i * T Bir r r r r
1
3
2
5
4
7
6
8
9 11 13 15 17 19 21 23 25 27 29 31 33
10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-5. Average normalized brightness temperature (V85/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
110
1.00
0.98
H 8 5 /T
0.96
0.94-
0.92-
0.90
u
■ r r r r r r r r r iT iT r i i i i i i i i i i
0.88
1
3
2
5
4
7
6
i n i r
9 11 13 15 17 19 21 23 25 27 29 31 33
8 10 12 14 16 18 20 22 24 26 28 30 32
Grid cell
Figure D-6. Average normalized brightness temperature (H85/T) for each grid
cell (dry-soil) for Node D.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
Ill
APPENDIX E
CORRELATION COEFFICIENTS BETWEEN NORMALIZED
BRIGHTNESS TEMPERATURES AND SEVERAL TRANSFORMATIONS OF THE
SSM/I BRIGHTNESS TEMPERATURES
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
112
Table E-l. Correlation coefficients between normalized brightness temperatures
and several transformations of the SSM/I brightness temperatures for
Node A (N= 467).
Variable
VY9/T
H19/T
V22/T
H19/V19
O.SS
0.39
V3VV19
A 37/R9
-0.26
0.55
0.93
-0.46
0.84
0.03
0.45
F85/F19
-0.36
-0.49
-0.03
m s /m
-0.10
-0.12
R9/22V
H19/22V
V37/V22
m u m
0.58
0.62
V37/T
0.35
0.21
m 7 /T
V2.5/T
0.85
-0.23
0.15
M 5/T
0.34
0.44
0.38
0.48
0.90
0.38
0.58
0.46
0.62
0.41
0.16
0.03
0.25
-0.29
0.69
0.66
0.19
0.27
-0.09
0.00
0.93
0.20
0.36
0.26
0.55
0.84
0.35
0.91
0.10
0.47
0.29
0.28
0.49
0.48
0.09
0.35
0.63
-0.10
0.91
0.43
0.35
0.59
0.48
-0.22
0.10
0.26
-0.02
0.63
0.61
0.17
0.19
0.29
0.43
0.38
0.70
0.82
m u m
0.55
0.57
0.89
0.90
0.23
0.34
0.33
0.57
-0.07
0.77
0.87
0.31
0.00
0.17
-0.32
0.18
0.37
H \9 im i
0.31
0.38
0.07
7/19/F85
0.56
0.34
0.86
0.28
0.22
0.72
-0.10
0.06
0.10
0.25
-0.39
0.05
-0.36
0.18
m /m
m s /m
H19IV37
V37/VS5
-0.26
m i/v% 5
0.59
0.37
0.88
0.34
0.32
0.81
0.00
//85/F85
0.42
0.61
0.40
0.47
0.72
0.52
0.76
m 9 /m s
V37IHZ5
0.48
0.76
-0.13
0.19
-0.21
0.10
-0.23
0.58
-0.28
-0.25
-0.15
-0.72
0.78
0.23
0.19
0.66
-0.59
-0.19
m 7 /M 5
-0.03
0.52
V19-H19/T
-0.54
-0.91
-0.35
-0.31
-0.83
-0.13
-0.07
-0.32
V37-m7/T
-0.54
-0.89
-0.35
-0.31
-0.85
-0.14
-0.35
VB5-m5IT
-0.40
-0.59
-0.61
-0.38
-0.73
-0.39
-0.71
-0.85
-0.87
-0.49
-0.93
-0.91
-0.61
-0.45
-0.35
-0.35
-0.15
-0.17
-0.34
-0.37
-0.47
-0.72
-0.52
-0.76
PR19
PR37
-0.58
PRS5
-0.42
-0.38
-0.40
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
113
Table E-2. Correlation coefficients between normalized brightness temperatures
and several transformations of the SSM/I brightness temperatures for
Node D (N= 550).
Variable
HI 9/R 9
F37/R9
H37/V19
VZ5/V19
H&5/V19
R9/22V
H19/22K
V37/V22
m um .
n s /m
m s /m
m9/V37
m 7 iv m
/H9/H37
/J19/F85
V37/VZ5
m 7 /m
m s /m
H \9im s
V57/HB5
m 7 im s
VL9-H19/T
V37-M7/T
v is -m s /r
PR19
PR57
PRiS
VV9IT
-0.40
0.23
-0.21
0.31
0.i7
0.04
-0.38
0.28
-0.21
0.33
0.18
-0.45
-0.41
-0.24
-0.44
-0.33
-0.48
-0.44
-0.35
-0.16
-0.37
0.43
0.43
0.48
0.40
0.41
0.44
H19IT
0.65
0.12
0.59
0.21
0.30
0.02
0.64
0.14
0.61
0.22
0.32
0.45
0.60
0.00
0.07
-0.22
0.11
0.43
-0.02
-0.33
-0.01
-0.63
-0.58
-0.41
-0.65
-0.60
-0.43
m IT
-0.39
0.35
-0.15
0.41
0.27
-0.24
-0.43
0.29
-0.21
0.40
0.25
-0.50
-0.41
-0.33
-0.52
-0.41
-0.55
-0.42
-0.44
-0.24
-0.45
0.41
0.43
0.47
0.39
0.41
0.42
V37/T
-0.33
0.71
0.12
0.71
0.59
-0.21
-0.38
0.70
0.08
0.71
0.59
-0.66
-0.35
-0.67
-0.77
-0.67
-0.74
-0.24
-0.73
-0.53
-0.70
0.35
0.37
0.31
0.33
0.35
0.24
m 7 /T
0.43
0.61
0.74
0.64
0.70
-0.18
0.37
0.60
0.72
0.64
0.71
0.00
0.44
-0.56
-0.40
-0.61
-0.30
0.43
-0.50
-0.68
-0.44
-0.41
-0.41
-0.38
-0.43
-0.44
-0.43
ViS/T
-0.19
0.82
0.32
0.94
0.86
-0.32
0.26
0.78
0.26
0.94
0.85
-0.60
-0.19
-0.78
-0.91
-0.93
-0.88
-0.02
-0.92
-0.83
-0.91
0.20
0.21
0.11
0.19
0.19
0.03
HS5/T
0.00
0.84
0.51
0.95
0.94
-0.32
-0.07
0.80
0.45
0.96
0.93
-0.45
0.01
-0.79
-0.85
-0.94
-0.79
0.21
-0.90
-0.92
-0.89
0.01
0.01
-0.13
-0.01
-0.01
-0.21
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
114
VITA
Abdul-Wahab Suliman Mashat was bom on April 17,1956, in Makkah, Saudi
Arabia. He received his high school education at King Abdul-Aziz High School,
Makkah, Saudi Arabia, graduating in 1974.
Mr. Mashat then attended the University of Petroleum and Minerals, Dhahran,
Saudi Arabia. He received his Bachelor of Science Degree in Mathematics in July
1979. He then worked as a Graduate Assistant at King Abul-Aziz University, Jeddah,
Saudi Arabia. He came to the United States of America in January 1981 to continue his
studies and received a Master of Science Degree in Atmospheric Sciences in May 1984
from the University of Arizona. He went back to Saudi Arabia and worked at King
Abdul-Aziz University as a Teacher Assistant. He entered Texas A&M University in
January 1987 to pursue die degree of Doctor of Philosophy in Meteorology.
Abdul-Wahab Suliman Mashat married Eman Ismail Dahlawi. They have four
daughters: Arwa, Ala, Afnan and Azhar. Their permanent address is P.O. Box 9013,
Makkah, Saudi Arabia.
Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.
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