a method for retrieving soil moisture in tibet region by utilizing microwave index from trmm/tmi...

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A method for retrieving soil moisturein Tibet region by utilizing microwaveindex from TRMM/TMI dataK. B. Mao a b c , H. J. Tang a , L. X. Zhang b e , M. C. Li d , Y. Guo b

& D. Z. Zhao da Key Laboratory of Resources Remote Sensing and DigitalAgriculture, MOA , Hulunber Grassland Ecosystem Observation andResearch Station , Institute of Agricultural Resources and RegionalPlanning , Chinese Academy of Agricultural Sciences , Beijing100081, Chinab State Key Laboratory of Remote Sensing Science , JointlySponsored by the Institute of Remote Sensing Applications ofChinese Academy of Sciences and Beijing Normal University ,Beijing, 100101, Chinac Graduate School of Chinese Academy of Sciences , Beijing,100049, Chinad International Institute for Earth System Science , NanjingUniversity , Nanjing 210093, Chinae The Center of Remote Sensing and GIS , Beijing NormalUniversity , Beijing, 100875, ChinaPublished online: 29 Apr 2008.

To cite this article: K. B. Mao , H. J. Tang , L. X. Zhang , M. C. Li , Y. Guo & D. Z. Zhao (2008) Amethod for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMIdata, International Journal of Remote Sensing, 29:10, 2903-2923, DOI: 10.1080/01431160701442104

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A method for retrieving soil moisture in Tibet region by utilizingmicrowave index from TRMM/TMI data

K. B. MAO{{§, H. J. TANG{, L. X. ZHANG{**, M. C. LI", Y. GUO{ and

D. Z. ZHAO"

{Key Laboratory of Resources Remote Sensing and Digital Agriculture, MOA,

Hulunber Grassland Ecosystem Observation and Research Station, Institute of

Agricultural Resources and Regional Planning, Chinese Academy of Agricultural

Sciences, Beijing 100081, China

{State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of

Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal

University, Beijing, 100101, China

§Graduate School of Chinese Academy of Sciences, Beijing, 100049, China

"International Institute for Earth System Science, Nanjing University, Nanjing 210093,

China

**The Center of Remote Sensing and GIS, Beijing Normal University, Beijing, 100875,

China

(Received 23 June 2006; in final form 23 April 2007 )

According to simulation analysis of the advanced integral equation model

(AIEM), there is a good linear relationship between emissivity and soil moisture

under conditions of given roughness. The normalized difference of emissivities at

19.35 GHz and 10.65 GHz with vertical polarization can partly eliminate the

influence of roughness and the squared correlation coefficient is about 0.985.

This paper uses the normalized brightness temperature for retrieving soil

moisture in Tibet from TRMM/TMI data. This method avoids parametrizing the

land surface temperature which is a key parameter for the computation of

emissivity. We make some sensitivity analysis for the atmosphere which is the

main influence factor for our method. The analysis results indicate that our

method is very good for clear days but is not very good when there is rainfall. We

evaluate our algorithm by using the ground truth data obtained from GAME-

Tibet and the retrieval error of soil moisture is about 0.04m3 m23 relative to

experimental data. The analysis indicates that the relationship obtained from the

theoretical model should be revised through the local ground measurement data

because the method is still influenced by roughness and vegetation. After making

a regression revision, the retrieval error of soil moisture is under 0.02m3 m23.

Finally, we retrieve the soil moisture in Tibet from TRMM/TMI data, and the

distribution trend of retrieval results is consistent with the real world.

1. Introduction

Many studies have shown that soil moisture is an important parameter in numerical

weather-prediction models that leads to a significant forecasting improvement in the

physics of land surface processes on regional and global scales, which combine the

*Corresponding author. Email: [email protected]

International Journal of Remote Sensing

Vol. 29, No. 10, 20 May 2008, 2903–2923

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2008 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160701442104

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results of all surface-atmosphere interactions and energy fluxes between atmosphere

and the ground (Njoku and Li 1999). The surface soil moisture is an important

bridge between land surface and atmosphere, which directly influences the exchange

of energy. But it is very difficult to obtain the soil moisture by ground measurement

in both time and space, especially at large spatial scales; microwave remote sensing

from satellite overcomes these problems (Owe et al. 2001). Thus microwave

radiometry has been proved to be one of the best methods to retrieve soil moisture

during the last 25 years.

Microwave observations are sensitive to soil moisture through the effects of the

moisture on the dielectric constant, and hence the emissivity, of the soil (Wang and

Schmugge 1980, Njoku et al. 2003). Wang (1985) and Njoku and Patel (1985)

investigated the potential of the 6.6 and 10.65 GHz channels of the Scanning

Multichannel Microwave Radiometer (SMMR) for soil moisture monitoring. These

studies were followed by others (Choudhury and Golus 1988, Owe et al. 1988, Jackson

et al. 1990, 1993, 1997, 2001, 2002, Owe et al. 1992, Kerr and Njoku 1993, Griend and

Owe 1994). Vegetation and surface roughness reduce the sensitivity of microwave

observations of soil moisture and many studies indicate that vegetation and roughness

effects become more pronounced at higher frequencies (Jackson et al. 1982, Wigneron

et al. 2004). Hence, low frequency (like L band) is more suitable for soil moisture

sensing. The C band is still sensitive to soil moisture although it is more influenced by

vegetation and surface roughness than the L band (Jackson and Schmugge 1991).

Paloscia et al. (2001) proposed an algorithm based on the sensitivity to moisture of

both the brightness temperature and the polarization index for retrieving soil moisture

from microwave data from SMMR and the Special Sensor Microwave/Imager (SSM/

I) satellites. Owe et al. (2001) proposed a methodology for retrieving surface soil

moisture using the polarization difference index from satellite microwave radiometer

data. Gu et al. (2002), Bindlish et al. (2003) and Gao et al. (2006) made several studies

for retrieving soil moisture from TRMM/TMI.

Although microwave radiometry at 1.4 GHz has been proved to be one of the

most promising passive remote sensing techniques for retrieving soil moisture

(Schwank et al. 2005), some problems do exist: 1) the spatial resolution is lower at

low frequency than at high frequency; 2) we want to know how soil moisture

changes with time but the archive of low-frequency (1.4 GHz) satellite data is quite

limited; 3) the low frequency (1.4 GHz) microwave data does not resolve the

vegetation problem although the influence of the atmosphere is quite small and

vegetation is semitransparent. As we all know, almost all pixels for passive

microwave satellite data at the present time are mixed pixels. Although many

passive remote sensing experiments and studies have been carried out at small scale

(Owe et al. 1988, Matzler 1990, Griend and LoSeen 1995, Wigneron et al. 2004,

Schwank et al. 2005), no one has managed to successfully resolve the vegetation

problem at large scale. Many studies used the v – t model to eliminate the influence

of vegetation (Jackson and O’Neill 1990, Jackson and Schmugge 1991, Kerr and

Njoku 1993, Njoku 1997, Njoku and Li 1999).

This method has some limitations, for example, the NDVI value of 1000 pixels is

equal to 0.4 in a passive microwave image, but the distribution and types of vegeta-

tion are different (from the uniformity to at random, from grass to forest). How much

will the error be if we use one method to deal with the influence of vegetation.

Many people have studied land surface heterogeneity and found that the

inherently large footprint of microwave satellite observations has serious

2904 K. B. Mao et al.

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consequences for geophysical parameters retrieval from inhomogeneous surfaces

(Griend et al. 2003, Burke et al. 2004). The new SMOS (soil moisture and ocean

salinity) soil moisture algorithm will use a vegetation differentiation within the

footprint (Kerr et al. 2001). Low frequency and high frequency data should be used

at the same time when we retrieve soil moisture from mixed pixels (especially

including vegetation). The low frequency can obtain the information about the soil

moisture and the high frequency can mainly obtain information about the

vegetation. However, there is a positive relationship between vegetation water

content and soil moisture which will determine the unique signal at different

frequencies for different distributions and types of vegetation for different pixels.

In this study, we have used the normalized brightness temperature which partly

eliminates roughness effects to retrieve soil moisture in Tibet from TRMM/TMI

data, and we have revised the retrieval result using in situ measurement data to

improve the retrieval accuracy.

2. Data and study region

The TRMM Microwave Imager (TMI) is aboard ‘‘The Tropical Rainfall Measuring

Mission (TRMM)’’ satellite including the first precipitation radar (PR) to be flown in

space, along with a 9-channel SSM/I-like passive microwave imager (TMI), an

AVHRR-like visible-infrared radiometer (VIRS), a lightning sensor and a cloud

sensor, which is a joint project between the United States (NASA’s Goddard Space

Flight Center) and Japan (the National Space Development Agency). The TMI is a

multi-channel passive radiometer whose signals in combination can measure rainfall

quite accurately over oceans and somewhat less accurately over the land. The TMI is

based on the design of the highly successful Special Sensor Microwave/Imager (SSM/

I) which has been flying continuously on Defense Meteorological Satellites since 1987.

The TMI measures the intensity of radiation at five separate frequencies: 10.65, 19.35,

22.235, 37 and 85.5 GHz. The spatial resolution of the 10.65GHz data is about 50 km

and the TRMM orbit yields a swath width of 158.5 km, and can provide data between

238u and + 38u latitude which covers Tibet completely. The antenna beam views the

Earth surface with 49u at nadir and the ground surface incidence angle is 52.8u at the

earth’s surface. More information can be found in the TRMM user handbook (2001),

Bindlish et al. (2003) and the Goddard Earth Sciences Data and Information Services

Center (2005).

Tibet is our study region which is known as the ‘‘roof of the world’’ (77–87uE and

25–34uN), whose high altitude and vast expanses of plateau make it the starting

point for the East Asia monsoon climate and a regulator of the global climate. It is

also the source of a large number of Chinese rivers. Construction and protection of

the ecological environment in this area are vital to the ecological environment of the

whole of China which is affected by both natural and human factors. The trend of

global climate towards dry and hot weather results in ecological devastation there.

On the other hand, human activities such as farming, popular tourism and various

construction work also contribute to the devastation. The GEWEX Asian Monsoon

Experiment (GAME) was carried out to understand the role of the Asian monsoon

in the global energy and water cycles and in the global climate system and many

methods were developed for long-range forecasting. Soil moisture is an important

influence factor, so soil moisture experiments (1997–1998) were one of the most

important parts of the GEWEX Asia Monsoon Experiment which can be used to

evaluate the soil moisture retrieval algorithm for TRMM/TMI data. GTDC

A method for retrieving soil moisture in Tibet region 2905

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(ground truth data collection) used two methods to obtain measurement data in

GAME-Tibet. One method is small scale and the range is about 60 m660 m, the

other is middle scale (the red box in figure 1) and the range is 1 km61 km. There are

about 11 observation systems. The purpose of SMTMS (Soil Moisture and

Temperature Measurement System) observation is to monitor parameters (like soil

moisture and temperature) for hydrological values which provide the soil moisture

from 0.04 m to 1.96 m soil depth. The soil moisture and soil temperature are

recorded every hour.

A lot of meaningful studies about the surface energy budget have been done

(Tanaka et al. 2001, Ma et al. 2002). The surface conditions (like roughness,

topograghy, soil type and vegetation) of Tibet region are very complex and the

details can be found in several references (http://monsoon.t.u-tokyo.ac.jp/tibet/,

Tanaka et al. 2001, Ma et al. 2002).

3. Description of AIEM model

In the development of a microwave model, the small perturbation method (SPM) is

valid for slightly rough surfaces, and the Kirchhoff approximation (KA) is suitable

for rougher surfaces (Ulaby et al. 1986, Fung 1994). In order to simulate the ‘‘real

world’’, Fung et al. (1992, 1994) proposed an integral equation model (IEM) which

is in agreement with the SPM for slightly rough surfaces and with the KA for

rougher surfaces. Although the IEM is better than previous models, it still involves

some assumptions. In order to overcome the shortcomings of the IEM, Chen et al.

(2000, 2003) extended and advanced the IEM. The advanced integral equation

model (AIEM) is one of the best models to simulate the emission characteristic of a

rough surface and has been demonstrated to have a wider application range for

surface roughness than the conventional models such as the small perturbation

model, the physical model and the geometrical optical model (Fung 1994). The

improvements were mainly done by overcoming some weak assumptions in the

original IEM model development. For example, the complementary field

Figure 1. GAME-Tibet (Game-Tibet POP/IOP Dataset CD, January 2001).


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coefficients were rederived to keep the absolute phase terms in the surface Green’s

function and its gradient, leading to more complete, and thus more accurate,

expressions for the multiple scattering (Chen et al. 2000) and single-scattering terms

(Chen et al. 2003) and a physical-based transition function that naturally connects

these two approximations was included in the new version of the AIEM model (Wu

et al. 2001, Chen et al. 2003). Many comparisons of the AIEM with a three-

dimensional (3-D) Monte Carlo model simulated data (Chen et al. 2003, Shi et al.

2005) showed a significant improvement in more accurate calculation of surface

emission signals over a wide range of surface dielectric, roughness, and sensor

frequencies.

4. Derivation of a simple retrieval approach using model simulations

The microwave radiometer measures the thermal emission of the ground and its

transfer from the ground through the atmosphere to the remote sensor. According

to the Rayleigh-Jeans approximation to the Planck function, the intensity of

brightness temperature of the radiation observed by the radiometer can be written as

Tbp t, mð Þ~ 1{vð Þ 1{e{t=m� �

TczepTse{t=mzt 1{tð Þ 1{ep

� �T;

a z 1{tð ÞT:a ð1Þ

where P stands for horizontal (H) or vertical (V) polarization, m5cosh, ep is the

emissivity, and t (the equivalent optical depth) and v (the single scattering albedo)

are two important parameters that characterize the absorbing and scattering

properties of vegetation, Ts is the land surface temperature, Tc is the average

temperature of the vegetation, Tbp (t, m) is the brightness temperature of

the radiation received by the sensor at an angle h, t is the transmittance of the

atmosphere, T:a is the upwelling average atmospheric temperature, T;

a is the

downwelling average atmospheric temperature. The centimetre wave band is hardly

influenced by the atmosphere. The transmittance(t) for microwaves is very high and

approximates to 1 even when the atmospheric water vapour content is about

5 gcm22 (Ulaby et al. 1986), so the influence of the atmosphere can be omitted and

equation (1) can be simplified to

Tbp t, mð Þ~ 1{vð Þ 1{e{t=m� �

TczepTse{t=m ð2Þ

Paloscia and Pampaloni (1984, 1988) defined the microwave polarization index as

MPDI t, mð Þ~ Tbv{Tbh

12

TbvzTbhð Þð3Þ

Submitting equation (2) into (3), we can get

MPDI t, mð Þ~ evTbsv{ehTbshð Þe{t=m

1{womegað Þ 1{e{t=m

� �Tcz

12

evTbsvzehTbshð Þe{t=m

ð4Þ

For bare soil, the t50, ep5Tp/Ts, and we can have

MPDI 0, mð Þ~ ev{ehð Þ12

evzehð Þð5Þ


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After assuming Tc5Ts, es~12

evzehð Þ and some other conditions (like albedo)

(Paloscia and Pampaloni 1988), equation (4) can be written as:

MPDI t, mð Þ&MPDI 0, mð Þe{t=m ð6Þ

The MPDI is mainly used to describe the characteristics of vegetation through the

difference between different polarization at the same frequency. Meesters et al.

(2005) made more analysis about the MPDI. We write MPDI as PDI (equation (7)),

and the only difference is that p/q is V or H at the same or different frequency. In

equation (5), the emissivity is mainly influenced by two factors (Ulaby et al. 1986,

Fung 1994). The first is soil moisture and the other is roughness which includes sig

(root mean square height) and cl (correlation length). The influence of soil moisture

and roughness will be analysed through simulation of the AIEM. The influence of

different soil types is small for emissivity (Fung 1994), so the key point is how to

eliminate the influence of roughness and vegetation for accurately retrieving soil

moisture.

PDI~ep{eq

� �

12

epzeq

� �~2 Tp{Tq

� �

TpzTq

~2DT

TpzTq

ð7Þ

The change of emissivity (ef) is determined by the dielectric constant which is

mainly influenced by the soil moisture and roughness (Wang and Schmugge 1980,

Njoku et al. 2003). Many studies (Paloscia and Pampaloni 1984, Pampaloni and

Paloscia 1985, Choudhury and Tucker 1987) have proved that the difference of

polarization (DT) is positive with the soil moisture. On the other hand, soil moisture

can be derived by using plants as indicators by using the microwave polarization

index (Jackson 1982, Cmaillo and Schmugge 1983, Calvet et al. 1994). Seen from

equation (6), the difference of MPDI between bare soil and vegetation is a

coefficient. In this study, we will utilize the PDI to retrieve soil moisture. The first

step is that we build a database through the simulation of theory model for most

conditions. After obtaining the relationship between soil moisture and PDI from the

database, we will make a revision for retrieval algorithm according to the

experiment data in situ field which will improve retrieval accuracy.

Many people (Schmugge et al. 1974, Eagleman and Lin 1976, Wang and

Choudhury 1981, Tsang et al. 1982, Wang et al. 1983) have found that a linear

relationship can be built between brightness temperature and soil moisture. These

data obtained from experiments are very limited and cannot describe all conditions.

So these relationships are mainly empirical and mainly suitable for local regions. We

use the AIEM model to simulate the relationship between soil moisture roughness

and emissivity at different frequencies, for the TRMM/TMI sensor: 10.65, 19.35,

22.235, 37, 85.5GHz), which can partly overcome the difficulty that the

measurement data in situ is not enough.

The commonly used Gaussian correlation function was utilized in the simulation

since it is a better approximation for high-frequency microwave measurements than

that of the exponential correlation function. In order to analyse conveniently, we

just give a part of the simulation results under different given conditions. Figure 2

shows the relationship between soil moisture and emissivity at different frequencies

under given roughness conditions (sig52; cl56) and h552.8u. Seen from figure 2,

there is an approximately linear relationship between soil moisture and emissivity

for vertical and horizontal polarization. The horizontal polarization is more


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sensitive than vertical polarization. So the horizontal polarization is better for

retrieving soil moisture than vertical polarization under this condition. However, the

lower frequency is more sensitive to soil moisture and so it is more suitable to

retrieve soil moisture.

In order to conveniently analyse the influence of roughness on emissivity, we set

the frequency equal to 10.65GHz. Figure 3 shows the simulation relationship

between rms height and emissivity under given correlation length (cl56), h552.8ufor different soil moisture content. Figure 4 shows the simulation relationship

between correlation length and emissivity for a given rms height (sig52) and

h552.8u and different soil moisture content. It can be seen from figure 3 and figure 4

Figure 2. The relationship between soil moisture and emissivity at different frequency.


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that the influence of sig or cl is approximately the same for emissivity at different

soil moisture content. In figure 3, the influence trend for emissivity is different

between vertical polarization and horizontal polarization. The emissivity decreases

for vertical polarization but increases for the horizontal polarization when sig

increases. The change of emissivity is slight when sig is approximately more than

1 cm for vertical polarization or the horizontal polarization. In figure 4, the

emissivity increases for vertical polarization but decreases for the horizontal

polarization when cl increases. The change of emissivity is slight when the cl is

approximately more than 10 cm for vertical polarization or the horizontal

polarization. The emissivity is more sensitive to the roughness (sig and cl) for the

Figure 3. The relationship between rms height and emissivity in different soil moisture.


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horizontal polarization than for the vertical polarization. For retrieving soilmoisture, the lower sensitivity to the roughness makes it more suitable for retrieving

soil moisture. Seen from this point, vertical polarization is better than horizontal

polarization for retrieving soil moisture. The simulation results are similar for

different frequencies (like 19.35GHz).

It can be seen from the simulation analysis above that the horizontal polarization

is more sensitive to soil moisture, and more sensitive to roughness than the vertical

polarization. Under given roughness conditions, we can build a relationship betweensoil moisture and emissivity. Of course, we can also build a relationship between

brightness temperature and soil moisture because there is a linear relationship

Figure 4. The relationship between correlation length and emissivity in different soilmoisture.


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between emissivity and brightness temperature when the land surface temperature is

known. Many empirical methods for retrieving soil moisture are built on these

conditions (Schmugge et al. 1986, Ahmed 1995, Paloscia et al. 2001). These retrieval

methods are mainly suitable for local regions; however, the roughness changes in

different seasons or when there is some rainfall. In order to make the method for

retrieving soil moisture more practical, we simulate the relationship between soil

moisture and emissivity under the assumption that the roughness is changed in a

range (sig:0.25–3.1, cl:5–30) by the AIEM model, and build a database which

includes most conditions on Earth. The 22.235GHz is much more influenced by the

water vapour content in the atmosphere and the low frequency is more suitable for

retrieving soil moisture, so we obtain the simulation data when h552.8u between soil

moisture and emissivity at 10.65 and 19.35 GHz.

Figure 5 shows the simulation results when h552.8u where we have made a

regression for simulation data and obtained the relationship, as in table 1. It can be

seen from figure 5 and table 1 that the squared correlation coefficient (R2) is very

high and the accuracy decreases with the increase of frequency. We find that the

vertical polarization is more suitable for retrieving soil moisture because the

roughness influence is greater for horizontal polarization than vertical polarization,

which can be proved by R2 and figure 5. On the other hand, the influence of

vegetation structure is greater for horizontal polarization than for vertical

polarization, so the microwave polarization index is used to describe the vegetation

(Pampaloni and Paloscia 1985, Choudhury and Tucker 1987).

In fact, the different soil conditions and different view angles have some influence

on the emissivity, but we find that the influence is small for the normalized

polarization index (PDI ) after the simulation analysis. Figure 6 is derived from

equation (7) and the simulation data and the regression results shown in table 2.

Table 1. The relationship between soil moisture and emissivity.

Frequency (GHz) Number of Set Equation R2

e 10.65V 1584 SM520.67937 + 0.54084 e10.65V21.21069 e 10.65V2

0.95

e 10.65H 1584 SM50.9639721.44992 e10.65H + 0.40156 e 10.65H2

0.864

e 19.35V 1584 SM520.26618 + 2.92229 e19.35V22.64512 e 19.35V2

0.939

e 19.35H 1584 SM50.941621.22773 e19.35H + 0.18324 e 19.35H2

0.867

(note: e is emissivity)

Table 2. The relationship between soil moisture and difference of different frequency.

Frequency (GHz) Number of Set Equation R2

PDI 10.65V–10.65H 1584 SM50.0149 + 0.5274 PDI1 + 0.1977 PDI 12

0.504

PDI 19.35V–10.65V 1584 SM50.0339 + 3.91 PDI2 + 130.7795 PDI 22

0.985

PDI 19.35H–10.65H 1584 SM520.017 + 3.2993 PDI3 + 34.1996 PDI 32

0.72

PDI 19.35V–19.35H 1584 SM520.0124 + 0.5793 PDI4 + 0.2023 PDI 42

0.529


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Figure 5. The relationship between soil moisture and emissivity. (a) sig:0.2523.1, cl:5.0231,theta552.8, fre510.65VGHz; (b) sig:0.2523.1, cl:5.0231, theta552.8, fre510.65VGHz; (c)sig:0.2523.1, cl:5.0231, theta552.8, fre519.35VGHz; (d) sig:0.2523.1, cl:5.0231,theta552.8, fre519.35VGHz.


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Figure 6. The relationship between soil moisture and PDI.


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It can be seen from figure 6(a), (b) and (d) that none of the PDIs for different

polarizations at 10.65GHz, for different polarizations at 19.35GHz or for horizontal

polarization at these two frequencies can eliminate the effect of surface roughness.However, figure 6(c) shows that PDI frequency 19.35GHz and 10.65GHz vertical

polarization can partly eliminate the influence of roughness and the squared

correlation coefficient (R-square) is about 0.985. The difference of same polarization

at different frequency can eliminate more influence of roughness than the different

polarization at same frequency, which can be known from figure 3 and 4 because the

influence of roughness for same polarization at different frequency is similar (see

figure 3 and figure 4). So equation (8) can be more accurately used to retrieve soil

moisture from bare soil.

SM~0:0339z3:91PDI19:35VGHz{10:65VGHzz130:7795PDI219:35VGHz{10:65VGHz ð8Þ

In equation (7), the emissivity can be obtained from the brightness temperature.

Thus, we can use the normalized difference of brightness temperature of TRMM/

TMI at frequencies of 19.35V GHz and 10.65V GHz, instead of the normalized

difference of the emissivity (ef) to retrieve soil moisture. For vegetation, the

influence is similar because the vertical polarization is not very sensitive to the

structure of vegetation. We can obtain the soil moisture through making a revisionwhich can be know from equations (5) and (6) because the relationship of MPDI

between bare soil and vegetation is an exponent function. However, there is little

information about soil moisture that can be obtained at frequencies above 10GHz

when the surface is covered by vegetation (O’Nell et al. 1984). Under this condition,

we assumed the vegetation as bare soil and the vegetation water content is positive

with soil moisture. The same polarization difference between 19.35GHz (V) and

10.65GHz (V) can also partly eliminate the influence of many factors such as

roughness, the structure of vegetation and topography and reflect the change of soilmoisture through vegetation, which can be known from O’Nell et al. (1984). Every

pixel is mixed for large scale passive microwave data in Tibet and the real condition

is very complicated, but the sensitivity of the PDI for bare soil and for vegetation is

similar. In order to improve retrieval accuracy, we need to use measurement data in

situ to revise the retrieval result. The best method is that we make a revision for

every pixel or small region in application.

5. Sensitivity analysis for retrieval method

Sensitivity analysis is necessary in many applications. From the retrieval

equation (8), the sensitivity is determined by PDI19.35VGHz–10.65VGHz which is given

by equation (7). In fact, the sensitivity is determined by the numerator (DT) becausethe denominator is very large. For the numerator, the error is made up of two parts.

One is the sensor sensitivity, and the other is the influence of the atmosphere. For

channels at frequency 19.35GHz and 10.65GHz, the sensitivity is usually less than

1 K and it varies with time (TRMM user handbook 2001, Goddard Earth Sciences

Data and Information Services Center 2005). We assume the sensitivity is 0.7 K

when T19.35 and T10.65 are between 273K and 320K, so that the difference of

brightness temperature at 19.35GHz and 10.65GHz should be between 0 and 1.4K.

For the sensor channel, the sensitivity of the retrieval method is between 0% and6.5%. Of course, the retrieval error is zero or under 3.25% (half of 6.5%) because the

noise should be at random (The sensor or data should be calibrated if not) at


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frequency 19.35GHz and 10.65GHz. This means that the retrieval error of soil

moisture is zero or under 3.25% even if there is no information about the ground

target.

For the influence of atmosphere, the transmittances of frequency 19.35GHz and

10.65 are very high and approximate to I even if the water vapour content is about

5 g cm22 (Ulaby et al. 1986). The value of the transmittance at 19.35GHz is lower

than at 10.65GHz though they are both very high. However, the difference of

brightness temperature at different frequencies can partly eliminate the influence of

the atmosphere. Of course, the 19.35GHz frequency will become sensitive when

there is rainfall, so the 19.35 GHz channel can also be utilized to monitor the rain

rate. Thus, our retrieval method will become sensitive when there is rain. In order to

illuminate this point, we compute the histogram of PDI19.35VGHz–10.65VGHz (see

figure 7) for TRMM/TMI (1998/09/01) data from Tibet and we retrieve the soil

moisture (figures 9 and 10) for this image. It can be seen from figure 7 and figure 6c,

that the number of pixels is largest when the value of PDI19.35VGHz–10.65VGHz is about

0.018 and soil moisture is about 0.15m3 m23. The value of PDI19.35VGHz–10.65VGHz

for some pixels is lower than zero because of cloud in Tibet, which indicates that the

retrieval method is sensitive when there is rainfall or when the cloud has a high water

vapour content. The distribution of histogram in figure 7 and the distribution of

PDI19.35VGHz–10.65VGHz in figure 6(c) indicate that our method can be utilized to

retrieve soil moisture. After the sensitivity analysis above, we conclude that

equation (8) is suitable to retrieve soil moisture.

6. Validation and application

Validation with ground truth data is important for any retrieval method (especially

one obtained from a theoretical model). It is very difficult to obtain the in situ

ground truth measurement of soil moisture matching the field of view

(51 km651 km) of TRMM/TMI data at the satellite pass for the validation of the

algorithm. Generally speaking, soil moisture varies from point to point on the

ground, and ground measurement is generally a point measurement. On the other

Figure 7. The PDI19.35VGHz–10.65VGHz histogram in a TRMM/TMI (1998/09/01) image inTibet.


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hand, precisely locating the pixel of the measured ground in TRMM/TMI data is

also a problem. There are so many difficulties in obtaining ground truth data that

validation with the use of ground truth data is quite difficult.

In this study, we select the Intensive Observation Period (IOP) of GAME/Tibet in

1998 to make some validation of the retrieval method. We use the soil moisture at

0.04m (the red box in figure 1) to validate the retrieval method because the highfrequency penetrates the soil surface very little. More information about the data

can be found from http://monsoon.t.utokyo.ac.jp/tibet/. A program was written to

read the brightness temperature data from TRMM/TMI 1B data (1998/08/01 and

1998/08/31) by setting the range (Lat: 31,35u, Lon: 91,94u, see figure 1) according

to the range of field experiment and many pixels were obtained. We retrieved the soil

moisture for these pixels by using the retrieval equation mentioned above. The

retrieval result is not very good during rainfall days because the 19.35GHz

frequency is sensitive to rain. So we just selected the data from clear days between1998/08/01 and 1998/08/31. Some experimental sites are not matched well with the

single pixel because of the satellite scanning characteristics, and we just select the

best approximation in range and the average value of the experimental sites to make

some analysis. Some data is obviously in error and has been deleted because there

was some influence of rainfall or cloud. The comparison between results obtained

from the TRMM/TMI data and the field data is shown in figure 8. It can be seen

from figure 8, the retrieval soil moisture is underestimated by about 0.04m3 m23 by

our method because the influence of atmosphere for 19.35GHz is more than for10.65GHz. On the other hand, the vegetation also has an important influence.

The relationship between measured ground soil moisture and retrieval soil

moisture in figure 8 can be represented by:

SMG~1:045SMRz0:024 R2~0:901 ð9Þ

SMR is the soil moisture retrieved using equation (8), SMG is the soil moisture

measured in the ground. We may use equation (9) which was obtained by regressing

Figure 8. The relationship between measured ground soil moisture and retrieval soilmoisture.


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the retrieval results and the measurement data to improve the retrieval accuracy.

The average retrieval error is about 0.02m3 m23 after revision. In order to provide

an example of the method in ation and to confirm our method in practice in Tibet,

we retrieve soil moisture from TRMM/TMI (1998/09/01) images in Tibet, and make

a distribution map of soil moisture with the same range of soil moisture content, see

figure 9. If the value of PDI19.35VGHz–10.65VGHz is lower than zero, we set it as zero.

We make the colour as red if the value of retrieval soil moisture is larger than 0.5. It

can be seen from figure 9 that the distribution condition of soil moisture is

reasonable. The black grid in figure 9 is the experiment site shown as a red grid in

figure 1.

We know that the retrieval method underestimates soil moisture content in

validation, so equation (9) is used to make a revision. The revision soil moisture is

shown in figure 10. We have performed a statistical analysis for figure 10. The value

of the soil moisture in most of the area is between 5–20% (see table 3). Of course, we

need to stress that our retrieval algorithm is mainly suitable for bare soil and under

conditions without rain. There may be some large retrieval error when the NDVI is

very large and there has been rainfall.

Figure 9. Retrieval of soil moisture by using equation (8).

Figure 10. Retrieval soil moisture revised by equation (9).


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7. Conclusion

On the basis of the radiative transfer equation, we briefly analyse the radiance

characteristics of microwave and the microwave polarization index. The emissivity

of soil is very sensitive to soil moisture, so microwave remote sensing is one of the

best methods for the retrieval of soil moisture.

According to the simulation analysis of AIEM, we can retrieve soil moisture

through difference of brightness temperature for given roughness, but this method is

empirical and mainly suitable for local regions. After analysing the relationship

between the emissivity and soil moisture by utilizing the simulation data of AIEM

under a wide range of conditions, we build a relationship between soil moisture and

the normalized difference at different frequency which can partly eliminate the

influence of roughness and atmosphere. The analysis indicates that the vertical

polarization index at 19.35GHz and 10.65 GHz can partly eliminate the influence of

roughness and the value of R2 is about 0.985, which indicates that our method is

very good. This method should be revised by in situ local measurement data because

it is still influenced by roughness. The same polarization difference between

19.35GHz (V) and 10.65GHz (V) can also partly eliminate the influence of many

factors (like the structure of vegetation and topography) and reflect the change of

soil moisture through the vegetation. Every pixel is mixed for large scale passive

microwave data in Tibet and so we use the measurement data in situ to improve

retrieval accuracy. The best method is that we make a revision for every pixel or

small region in application. On the other hand, although there is a positive

relationship between soil moisture and vegetation water content, the relationship is

very difficult to quantify accurately because the influence of the vegetation is too

complex. We evaluate our method by experimental data in GAME/Tibet, and the

retrieval error of soil moisture is about 0.04m3 m23 relative to experiment data.

After making a regression revision, the retrieval error of soil moisture is about

0.02m3 m23. This indicates that we need to make a revision after building a

relationship through simulation data because there is some difference between the

theoretic model and the real world. However, the theoretical model is not perfect

and cannot cover all the conditions. The sensitivity analysis indicated that our

method is not very sensitive to the influence factor (roughness, sensor noise), but the

retrieval results will not be very good when there has been rainfall.

Finally, further study is needed and the accuracy needs to be improved when the

value of the NDVI (vegetation) is very large and the weather (cloud and rainfall) is

not very good. The retrieval error is larger in a mixed pixel including water than in

other pixels. We suggest that the low frequency and high frequency should be

simultaneously used because almost every pixel of the large scale of TRMM/TMI is

mixed.

Acknowledgment

The authors would like to thank the following people for their various help with

this study: Jiancheng Shi, University of California, Santa Barbara, A. K. Fung,

Table 3. The distribution of pixels in different soil moisture range.

Rang of soil moisture 0–0.1 0.1–0.2 0.2–0.3 0.3–0.4 .0.4Number of pixels 8609 3349 773 305 363Percent 64.25% 25% 5.80% 2.27% 2.70%


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University of Texas, Kun-Shan Chen, Center for Space and Remote Sensing

Research, National Central University. They would also like to thank the

anonymous reviewers for their valuable comments, which greatly improved the

presentation of this paper.

Thanks are also due to NASA for providing TRMM/TMI data, the Department

of Civil Engineering University of Tokyo, providing the GAME-Tibet POP/IOP

Dataset CD. This work was sponsored by the National Natural Science Foundation

of China (Grant No: 90302008&40571101); basic research work of Central Scientific

Research Institution for Public Welfare; Project 863 of China: (Grant no.

2006AA10Z241&2007AA10Z230) and Sponsored by the Open Fund of Key

Laboratory of Resources Remote Sensing and Digital Agriculture, MOA.

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a method for retrieving soil moisture in tibet region by utilizing microwave index from trmm/tmi...

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