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Incorporating soil texture in soilmoisture estimation from extendedlow‐1 beam mode RADARSAT‐1 SARdataHari Shanker Srivastava a , Parul Patel b & Ranganath R. Navalgundb

a Regional Remote Sensing Service Centre (RRSSC) , Indian SpaceResearch Organization (ISRO) , IIRS Campus , 4Kalidas Road,Dehradun – 248 001 (Uttaranchal), Indiab Space Applications Centre (ISRO) , Ahmedabad – 380 015, IndiaPublished online: 22 Feb 2007.

To cite this article: Hari Shanker Srivastava , Parul Patel & Ranganath R. Navalgund (2006)Incorporating soil texture in soil moisture estimation from extended low‐1 beam modeRADARSAT‐1 SAR data, International Journal of Remote Sensing, 27:12, 2587-2598, DOI:10.1080/01431160500497838

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Incorporating soil texture in soil moisture estimation from extendedlow-1 beam mode RADARSAT-1 SAR data

HARI SHANKER SRIVASTAVA*{, PARUL PATEL{ and

RANGANATH R. NAVALGUND{{Regional Remote Sensing Service Centre (RRSSC),

Indian Space Research Organization (ISRO),

IIRS Campus, 4Kalidas Road, Dehradun – 248 001 (Uttaranchal), India

{Space Applications Centre (ISRO), Ahmedabad – 380 015, India

(Received 3 January 2005; in final form 23 November 2005 )

Sensitivity of microwaves towards soil moisture is well understood; still,

development of a practical algorithm for soil moisture estimation using

microwaves is difficult. This is due to the fact that along with their strong

sensitivity to soil moisture, microwave signals are also sensitive to other target

properties such as soil texture, surface roughness, and crop cover. In this paper,

an attempt has been made to incorporate the effect of soil texture in large area

soil moisture mapping using extended low-1 beam mode RADARSAT-1 SAR

data in such a way that knowledge of soil texture is not a prerequisite.

1. Introduction

The key factor behind soil moisture estimation using microwaves is the large

difference between the dielectric constant of water (,80) and of dry soil (3 to 4) at

microwave frequencies. The radar backscattering coefficient (su) is strongly related

to soil moisture due to the high dielectric constant of a mixture of soil and water

(Wang 1980). Though the radar backscattering coefficient is strongly related to soil

moisture, at the same time it is also sensitive to other target properties such as

surface roughness, crop cover, and soil texture (Henderson and Lewis 1998, Ulaby

et al. 1978, 1979, Dobson and Ulaby 1981). Hence, it is necessary to incorporate the

effects of these parameters in the soil moisture retrieval model. There have been

studies to understand the effects of surface roughness and crop cover in the soil

moisture retrieval model (Wooding 1995, Borgeaud et al. 1995, Patel et al. 2002). Oh

et al. (1992) have developed an empirical model using a ratio of co-polarized and

cross-polarized SAR backscatter. The model attempts to invert the soil moisture and

surface roughness in terms of ks, with the validity region over ks,3. Dubois et al.

(1995) have also developed an empirical algorithm for bare soils to retrieve soil

moisture and surface roughness using suHH and suVV SAR, which functions over

areas with low Normalized Difference Vegetation Index (NDVI) values. Attempts

have also been made by a few researchers to incorporate the effect of soil texture in

the soil moisture retrieval model (Ulaby et al. 1986a). Blumberg et al. (2000) have

also observed the influence of soil texture while assessing soil water content using a

P band airborne scatterometer. They noted that amongst sandy and clay soil, a

*Corresponding author. Email: hari_space@yahoo.com

International Journal of Remote Sensing

Vol. 27, No. 12, 20 June 2006, 2587–2598

International Journal of Remote SensingISSN 0143-1161 print/ISSN 1366-5901 online # 2006 Taylor & Francis

http://www.tandf.co.uk/journalsDOI: 10.1080/01431160500497838

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higher correlation was detected for sandy soils. In the case of clay, it is the higher

content of clay that makes the water molecules to be tightly bound with soil

particles, which in turn restricts water molecules to align with the incident radar

signal.

The work presented in this manuscript is part of a study whereby attempts are

being made to incorporate the effect of surface roughness, crop cover, and soil

texture on SAR sensitivity to soil moisture to reach an operational methodology of

soil moisture estimation in such a way that one need not make any assumptions

about the distribution of these parameters and one should be able to extract

information on these parameters from the space platform alone. Thus ensuring that

knowledge of surface roughness, crop cover, and soil texture is not a prerequisite for

large area soil moisture estimation using SAR. In the future, it is proposed to merge

all three approaches to obtain an operational methodology that would be able to

cater for soil moisture estimation under all kinds of agricultural heterogeneity from

the space platform. Firstly, an attempt was made to incorporate the effect of surface

roughness in the soil moisture retrieval model using multi-incidence angle

RADARSAT-1 SAR. For this purpose, the angular behaviour of SAR backscatter

to surface roughness and soil moisture was exploited to include the effect of surface

roughness in the soil moisture retrieval model. Data from agricultural fallow fields

falling in the same soil texture class with varying surface roughness conditions were

used for this study to ensure that it is only the surface roughness other than soil

moisture that affects the SAR backscatter, thus eliminating the effect due to crop

cover and soil texture (Srivastava et al. 2003). Similarly, the sensitivity of lower and

higher incidence angle SAR to crop cover and soil moisture was exploited to arrive

at an empirical model to incorporate the effect due to crop cover in soil moisture

estimation, while controlling other parameters such as soil texture and surface

roughness (Srivastava et al. 2002).

Correspondingly, only bare, smooth fields are considered in the present study in

order to control the effect of crop cover and surface roughness on SAR backscatter

so that it is only the soil texture other than soil moisture that affects the SAR

backscatter. In the present paper, a soil moisture measure has been proposed that

incorporates the effect of soil texture on SAR response to the moisture content of

the soil in such a way that knowledge of soil texture is not a prerequisite. At the

same time, the proposed soil moisture measure, which is defined as the water that is

available to plant per unit volume of soil, is more realistic as it appeals directly to the

agricultural scientist.

2. Conceptualization

Wet soil is a heterogeneous mixture of soil, water, and air pockets. In general, the

water in wet soil can be further divided into bound water and free water. The bound

water refers to water molecules located in the first few molecular layers surrounding

the soil particles that are held tightly by the soil particles due to the effect of metric

and osmotic forces. These forces significantly reduce the free energy of the absorbed

water molecules and therefore these water molecules are termed as bound water. In

contrast to this, water molecules that are located several molecular layers away from

soil particles are able to move within the soil medium with relative ease and are

therefore termed as free water. The percentage of free water and bound water

present in a soil medium largely determines the dielectric constant of a soil medium

(Ulaby et al. 1986b).

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The percentage of free water and bound water in a soil medium largely depends

upon the surface area of soil particles present. As the surface area of soil particles in

a soil medium depends upon the particle size and the relative proportions of the

various-sized particles in a given soil, the dielectric constant of wet soil varies with

soil texture. The change in dielectric constant in turn results in a change in

backscattering coefficient. Thus, it is required to incorporate the effect of soil

texture, which governs the surface area of soil particles and in turn the proportion of

free water and bound water in a given soil–water mixture, while developing models

to retrieve soil moisture information with the help of radar remote sensing. This calls

for defining a parameter that represents the critical component of soil moisture,

which is related to the physical interaction process between microwave signal and

the soil–water mixture.

In this paper, an attempt has been made to incorporate the effect of soil texture in

the soil moisture retrieval model by representing the soil moisture in a more realistic

term that would relate to the possible amount of free water in a given soil water

mixture. For this purpose, it was thought to represent the soil moisture in terms of

water that is available to plants in the soil medium. It is well known that plants

cannot extract the water below wilting point, i.e. the moisture at 15-bar pressure.

Due to this reason, out of the total observed soil moisture, the plant is not able to

extract the water that is below wilting point. Hence, the water that is actually

available to the plant is the difference between the observed soil moisture and the

wilting point. It is reasonable to assume that the amount of soil moisture at wilting

point is directly proportional to the amount of bound water as water molecules at

15-bar pressure are also very tightly held with soil particles. Therefore, the amount

of free water can be related easily to the difference between the observed soil

moisture and the wilting point for that soil. Thus, we can assume that the amount of

water in the soil that is free to interact with the incident microwaves and give

significant contribution to the SAR backscatter is close to the amount of water

available to plants per unit volume of soil, as given by equation (1):

SM WAP~ SMOBS�SM15-barð Þ|bulk density ð1Þ

where:

SM_WAP 5soil moisture in terms of water that is available to plant per unit

volume

SMOBS 5observed soil moisture from field

SM15-bar 5soil moisture at wilting point

(SMOBS2SM15-bar)5water that is available to the plant at the time of observation.

Thus, SM_WAP is not merely the volumetric soil moisture content but it is the

volumetric soil moisture content above wilting point. The minimum wilting point

for the soils residing in the study area happens to be 8% for sandy loam soil, whereas

the maximum is 15% for fine loamy soils. Let us consider the case of two fields, one

with sandy loam soil and the other having fine loamy soil, and both fields having the

same volumetric soil moisture of say, 20%. Thus, volumetric soil moisture of 20%

for the field with sandy loam soil would result in 12% (520%–8%) of water being

available to the plant (SM_WAP) and since bound water would also be relatively

less for sandy loam soil, there would be more free water particles to interact with the

incoming microwave signal, giving a higher dipole moment. This would, in turn,

result in higher backscatter from that field, whereas the field with fine loamy soil,

Effect of soil texture in soil moisture estimation 2589

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which also has 20% volumetric soil moisture, would have a 5% (520%–15%)

SM_WAP. Since the bound water in this case would be relatively more, there would

be a higher number of water molecules tightly held with the soil particles, meaning

reduced free water particles to interact with the incoming SAR signal. Thus, there

would be a lesser dipole moment, which would in turn result in low backscatter from

that field. Therefore, variation in soil texture leads to a situation where two fields

having similar volumetric soil moisture would end up having higher or lower

available water to plants, which is effectively a higher or lower amount of free water

in the soil, resulting in higher or lower SAR backscatter. Hence, it is proposed to use

the soil moisture measure, SM_WAP, given by equation (1) as a representative of

the actual amount of soil moisture that interacts with the SAR signal.

3. Data set and study area

In order to validate the above concept, four scenes of RADARSAT-1 SAR at lower

incidence angle (beam mode: extended low-1 (EL1), incidence angle: 10u–23u) were

acquired on 13 March 1999, 10 December 1999, 27 January 2000 and 15 March 2000

over parts of the Agra, Mathura, and Bharatpur districts of India. Nominal

resolution of RADARSAT-1 EL1 beam mode is 35 m. Large coverage of

RADARSAT-1 (EL1) image (170km6170 km) provided data over a variety of soil

textures from very fine to very coarse. The study area broadly covers fine loamy, fine

silty, coarse loamy, sandy loam, and sandy soils. Geographic coordinates of

the study area are 77u29941.8299 to 78u48901.2499 east and 26u29905.9299 to

27u54901.0499 north. The study area is a flat terrain dominated by agricultural land.

The RADARSAT-1 operates at C-band SAR with HH polarization. It operates

under a variety of viewing modes with varying spatial and radiometric resolutions

(RSI 2000). In addition to SAR data, GPS and 1 : 50,000 scale Survey of India (SOI)

topographic maps have also been used to locate the ground truth sites onto the

image.

4. Methods

4.1 Reconnaissance survey and ground truth data collection

Sampling locations were selected carefully after reconnaissance field surveys near

the ground control points. Enough care was taken regarding homogeneity of

sampling locations in terms of soil moisture, size, and surface roughness. A sampling

unit size of 100 m6100 m was selected (Patel and Mohan 1993). In order to ensure

that it is only soil texture that influences the microwave backscatter other than soil

moisture, only smooth bare fields were chosen as sampling locations. Thus, major

factors such as crop cover and surface roughness affecting the sensitivity of SAR

backscatter to soil moisture were eliminated. During data acquisition in December

1999, wheat had just been sown. Hence, all these fields were treated as fallow fields

as the crop had not emerged on the surface. During March 1999 and March 2000,

the fallow fields consisted of harvested mustard fields, whereas in January 2000,

only agricultural fallow fields have been included in the analysis. A total of 57 fields

were included in the analysis, out of which 13 samples were taken for the 13 March

1999 pass, 30 samples for the 10 December 1999 pass, 4 samples for the 27 January

2000 pass, and 10 samples for the 15 March 2000 pass. Out of these 57 observations,

7 were randomly selected for validation of the models and the remaining 50 were

used to develop the model.

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A ground truth data collection campaign was carried out in synchronization to the

RADARSAT-1 passes. The information gathered during ground truth data collection

consists of soil samples with the help of an auger and core sampler for the measurement

of gravimetric soil moisture, bulk density, field capacity, and wilting point of soil

samples. Fresh weights of soil samples were noted and they were oven dried for 24 h at

105uC temperature. The dried samples were weighed once again for dry weight. With the

help of fresh weight and dry weight for each of the 57 sampling fields, gravimetric soil

moisture (SM_G) has been calculated. Gravimetric soil moisture was then converted to

volumetric soil moisture (SM_V) by multiplying the gravimetric soil moisture of the

sample with their respective bulk densities. The bulk densities were calculated using

100 cc of undisturbed soil sample taken with the help of a core sampler.

Field capacity and wilting point of the soil samples have been estimated with the

help of a pressure plate instrument. This instrument was used to calculate the soil

moisture of all the 57 soil samples at 1/3-bar pressure for field capacity and at 15-bar

pressure for wilting point. Conversion of soil moisture values into percentage of field

capacity is as per the following equation (2):

SM FC~ 100 SMOBS�

SM1=3-barð2Þ�

where

SM_FC 5soil moisture in percentage of field capacity

SMOBS 5observed soil moisture from field

SM1/3-bar 5soil moisture at field capacity.

Soil moisture in terms of water available to plant per unit volume of soil, SM_WAP,

has also been calculated by multiplying the water currently available to the plant

with bulk density, i.e. [(SMOBS2SM15-bar) bulk density], as given by equation (1).

4.2 Data processing

4.2.1 DN to su conversion. For the RADARSAT-1 data supplied from CSA,

output scaling in terms of gain and offset are applied to the data to ensure optimum

utilization of the available dynamic range. DN image was converted to radar

backscatter (su) image using the following equation:

s0dB~10:0 log10 DN2zoffset� ��

gain� �

z10:0 log10 sin að Þð Þ ð3Þ

where DN is the digital number of the SAR image, which is in amplitude, and a is the

local incidence angle at that pixel position in the range direction. The header information

was used for the calculation of a, the local incidence angle at each pixel (RSI 2000).

4.2.2 Image processing. After conversion of DN to su, speckle suppression was

carried out using enhanced Lee-filtering algorithm (Lee 1986). The 10 December

1999 image of RADARSAT-1 EL1 beam mode was geo-referenced using the ground

control points (GCPs) from 1 : 50,000 scale Survey of India (SOI) topographic maps.

The registration accuracy for map to SAR image (10 December 1999) was of the

order of 1.4 pixels. The other three SAR images were then co-registered with this

geo-referenced image using the nearest neighbourhood method of resampling

(Duggin and Robinore 1990). The rail/road/canal networks and ground truth

locations were digitized and transferred onto the image. After registering all the four

SAR images, the 57 sampling locations were identified on the images and their

backscattering coefficient values were extracted from SAR images. Once the

Effect of soil texture in soil moisture estimation 2591

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backscattering coefficient values were obtained, variation in SAR backscatter due to

soil moisture represented in terms of various units was studied. For this purpose, soil

moisture retrieval models have been developed for the following combinations using

linear regression analysis:

N backscattering coefficient with gravimetric soil moisture (SM_G)

N backscattering coefficient with volumetric soil moisture (SM_V)

N backscattering coefficient with soil moisture represented in terms of percentage

of field capacity (SM_FC)

N backscattering coefficient with soil moisture represented in terms of water

available to plant per unit volume of soil (SM_WAP)

5. Results and discussion

5.1 Model development

Since the objective of the present study was to incorporate the effect of soil texture in

the soil moisture retrieval model, enough care has been taken to avoid the effects ofother noise parameters such as crop cover and surface roughness by including only

smooth, bare fields in the analysis, as explained in the conceptualization section. The

absence of other noise parameters would enhance the effect of soil texture on the

radar backscatter. Regression analysis has been used to arrive at the soil moisture

estimators. Confidence level test using F statistic was performed for assessing

the significance of the regression analysis. For comparative evaluation of the

performance of the proposed soil moisture measure, four separate models have been

developed using the soil moisture values represented in terms of gravimetric soilmoisture, volumetric soil moisture, soil moisture in percentage of field capacity, and

soil moisture in terms of water available to plant per unit volume of soil. These

models are given by equation (4) to equation (7) and the results of regression analysis

for these models are given in table 1.

The model developed for gravimetric soil moisture is:

Gravimetric soil moisture SM Gð Þ~AzB s0 dBð Þ ð4Þ

The derived coefficients are as:

Gravimetric soil moisture SM Gð Þ~29:84z1:91 s0dBð Þ

The coefficient of determination (R2) obtained in regression analysis for the

model given by equation (4) was 0.88, and the variation of the backscattering

coefficient with gravimetric soil moisture is shown in figure 1(a) (F5356.3875, level

of significance of F57.45E-24).

The model for volumetric soil moisture is given as:

Volumetric soil moisture SM Vð Þ~A0zB0 s0 dBð Þ ð5Þ

The derived coefficients are as:

Volumetric soil moisture SM Vð Þ~44:15z2:83 s0 dBð Þ

In this case, the coefficient of determination (R2) was obtained as 0.90, which was

slightly higher than the value of R2 obtained for equation (4) (F5446.0902, level of

significance of F56.01E-26).

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Table 1. Results of regression analysis and validation results for different models.

Sr. No. Model used A B R2

No. of data pointsused for model

development

Root mean square(rms) error for

modeldevelopment

No. of data pointsused for model

validation

Root mean square(rms) error for model

validation

1. SM_G5A + B (su) 29.84 1.91 0.88 50 2.43 07 2.232. SM_V5A + B (su) 44.15 2.83 0.90 50 2.07 07 2.103. SM_FC5A + B (su) 106.2 6.70 0.92 50 1.58 07 1.494. SM_WAP5A + B (su) 31.78 2.57 0.96 50 0.81 07 0.62Where:SM_G: Gravimetric soil moisture in %g/gSM_V: Volumetric soil moisture in %g/cm3

SM_FC: Soil moisture in % of field capacitySM_WAP: Soil moisture in terms of water available to plant per unit volume of soil in %g/cm3

su: Backscattering coefficient

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The variation of the backscattering coefficient with volumetric soil moisture

represented by equation (5) is given in figure 1(b). In order to study the variation of

the backscattering coefficient with soil moisture represented in terms of percentage

of field capacity, the following model has been developed:

Soil moisture in percentage of field capacity SM FCð Þ~A00zB00 s0 dBð Þ ð6Þ

The derived coefficients are as:

Soil moisture in percentage of field capacity SM FCð Þ~106:20z6:70 s0 dBð Þ

The variation of the backscattering coefficient with soil moisture represented in

terms of percentage of field capacity as described by equation (6) is given in

figure 1(c) (F5534.5526, level of significance of F51.14E-27).

Figure 1. Variation of backscattering coefficient (su) with: (a) gravimetric soil moisture; (b)volumetric soil moisture; (c) soil moisture represented in terms of percentage of field capacity;and (d ) soil moisture represented in terms of water available to plant per unit volume of soil.

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It was observed that for this model, R2 has increased moderately from 0.88 to 0.92

in comparison to that of gravimetric soil moisture. In order to explore the possibility

of further improvement in the model, in terms of improved sensitivity of SAR

backscatter towards the soil moisture over a large agricultural area and covering a

wide range of soil textures, a model representing soil moisture in terms of water

available to plant per unit volume of soil, as discussed in the conceptualization

section, was developed. This model is given by equation (7).

Soil moisture in terms of water available to plant SM WAPð Þ~A000zB000 s0 dBð Þ ð7Þ

The derived coefficients are as:

Soil moisture in terms of water available to plant SM WAPð Þ~31:78z2:57 s0 dBð Þ

Variation of the backscattering coefficient with soil moisture represented in terms of

water available to plant per unit volume of soil is shown in figure 1(d ) and the

coefficient of determination (R2) for the model given by equation (7) was found to

be 0.96 (F51043.101, level of significance of F53.23E-34).

Regression analysis was carried out to arrive at the soil moisture estimators. To

test the significance of the regression analysis, confidence level test using F statistic

was performed, which yielded the level of significance of F statistic as 3.23E-34,

1.14E-27, 6.01E-26, and 7.45E-24, respectively, for the soil moisture, represented in

terms of SM_WAP, SM_FC, SM_V, and SM_G, with the corresponding values of

the F statistic being 1043.101, 534.5526, 446.0902 and 356.3875. All four regression

estimators of soil moisture measures are statistically significant, with the SM_WAP

being most significant with the smallest value of level of significance (3.23E-34) and

the highest value of F statistic (1043.101). Thus, the results based on the regression

estimator supports the physical process-based concept of using the available water

as soil moisture measure, which is in reality taking place when SAR interacts with

the moist soil.

From equation (4) to equation (7) it is observed that there is an increase in

R2 when soil moisture was represented in volumetric percentage of field capacity

as well as in terms of water available to plant per unit volume of soil, as compared to

R2 obtained for gravimetric soil moisture (table 1). Similar results have been

observed by Ulaby et al. (1986a) by expressing the soil moisture in terms

of percentage of field capacity. It was also observed that the increase in R2

was marginal when soil moisture was represented in volumetric units (R2590),

whereas R2 was 0.92 when soil moisture was represented as percentage of

field capacity. In contrast to this, there was a considerable increase in the coefficient

of determination (R250.96), when the soil moisture was represented in terms

of water available to plant per unit volume of soil. The study indicated that for

large area mapping of soil moisture under a variety of soil textures, gravimetric

soil moisture and volumetric soil moisture are a comparatively less sensitive

way to represent soil moisture. In contrast to this, the model given by

equation (6) was found to be better than the first two models given by

equations (4) and (5). However, the considerable increase in the coefficient of

determination (R250.96), when soil moisture was represented in terms of water

available to plant per unit volume of soil, clearly indicates that it is this measure that

is closer to the actual free water to which SAR interacts. Interestingly it is also the

water that is usable by a plant and therefore it would directly appeal to agricultural

scientists.

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5.2 Model validation

Validation is an essential component of a statistical approach-based analysis. It is

necessary to have a validation set consisting of independent observations of the

parameter, and these observed values of the parameters should not be used to arrive

at an estimate of the parameter. Moreover, the size of the validation sample is

equally important. In our study, the minimum number of validation points required

for the meaningful validation exercise have been determined using the precision

power approach, as suggested by Brooks and Barcikowski (1996), keeping the

criteria that the sample correlation coefficient is not to decrease by more than 0.05,

no matter what the expected value of the correlation coefficient is. Using this

approach, with the R2 values of the original sample (with sample size550) being

0.96, 0.92, 0.90, and 0.88, respectively, for the soil moisture represented in terms

of SM_WAP, SM_FC, SM_V, and SM_G, the size of the validation set for

the case of one parameter is observed to be 2, 4, 4, and 5, respectively. This

validation sample size requirement analysis indicated that a minimum of five

validation points is required to form the validation sample. However, in the

present study, seven validation points were kept aside to form the validation

data set, which is more than the minimum data points required for model

validation.

In order to substantiate the results, validation has been carried out for seven

locations randomly selected out of the 57 sampling fields. Estimated values of soil

moisture were calculated using all four models developed during the study, as given

by equation (4) to equation (7). The rms (root mean square) error between the

observed soil moisture and estimated soil moisture using the models given by

equation (4) to equation (7) is given in table 1. The rms error for SM_WAP was

found to be the lowest at 0.62, as compared to that of 2.23 obtained for model (4).

The considerable reduction in rms error by representing soil moisture in terms of

water available to plant per unit volume of soil, instead of gravimetric soil moisture,

clearly indicated the soundness of the approach presented in the study. For SM_FC,

the rms error shows lower value at 1.49, which was better than the rms error of 2.10

and 2.23 obtained for SM_V and SM_G. Validation results clearly indicated that

representation of soil moisture in terms of percentage of water available to plant per

unit volume of soil appears to be the best approach to represent soil moisture while

mapping soil moisture with microwaves over a large agricultural area covering a

variety of soil textures.

6. Conclusions

The study indicated that the percentage of water available to plant per unit volume

of soil is a more realistic way to represent soil moisture as it describes the parameter

affecting microwave interaction in the soil medium. Thus, by adopting an

appropriate soil moisture measure, like the one proposed in this paper, it is possible

to incorporate the effect of soil texture in the soil moisture retrieval model. The

concept of water available to plant in a soil medium is based upon the assumption

that the water present in the soil medium at 15-bar pressure, which is unavailable to

plants, is directly proportional to the bound water. A considerable reduction in rms

error, from 2.23 to 0.62, was observed when the proposed measure was used in place

of gravimetric soil moisture. This suggests that it is possible to incorporate the effect

of soil texture in soil moisture estimation using microwave remote sensing by

2596 H. S. Srivastava et al.

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representing the soil moisture in terms of water available to plant in that soil

medium. Moreover, the information on soil moisture in terms of water available to

plants would also be a more meaningful parameter as an input in many research

areas, particularly that of agriculture.

Acknowledgements

First author is extremely thankful to Mr Hari Shanker Srivastava, Director,

RRSSC/NNRMS and EOS, ISRO Headquarters, Bangalore, India and Prof. V. K.

Jha, Head, RRSSC, Dehradun, India for encouragement and support. Mrs Parul

Patel extends her thanks to Shri J. S. Parihar, Group Director, ARG, Space

Applications Centre, Ahmedabad, India and Mission Director, RSAM, and Dr

Sushma Panigrahi, Head, AMD, Space Applications Centre, Ahmedabad, India for

encouragement and support.

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