Large-Area Soil Moisture Estimation Using Multi ... - IEEE Xplore

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 47, NO. 8, AUGUST 2009

Large-Area Soil Moisture Estimation Using Multi-Incidence-Angle RADARSAT-1 SAR Data Hari Shanker Srivastava, Parul Patel, Yamini Sharma, and Ranganath R. Navalgund

Abstract—The sensitivity of synthetic aperture radar (SAR) backscatter to soil moisture has been adequately established. However, monitoring of soil moisture over large agricultural areas is still difficult because SAR backscatter is also sensitive to other target properties like surface roughness, crop cover, and soil texture (soil type), along with its strong sensitivity to soil moisture. Hence, to develop a methodology for large-area soil moisture estimation using SAR, it is necessary to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model. In this paper, a methodology for soil moisture estimation over a large area is developed using a pair of low- and high-incidence-angle RADARSAT-1 SAR data over parts of Agra, Mathura, and Bharatpur districts, India, during March 1999. The methodology requires acquisition of synthetic aperture radar data at low and high incidence angles, such that the soil moisture changes are negligible between the two acquisitions. In order to demonstrate the applicability of the developed methodology, the same was validated over a different area (parts of Saharanpur and Haridwar districts, India) during March 2005. Both test sites provided the variety of agricultural heterogeneity required for development and validation of the methodology for large-area soil moisture estimation. The proposed methodology offers an approach to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model from the space platform, without making any assumptions on the distributions of these parameters or without knowing the actual values of these parameters on ground. Index Terms—Crop cover, multi-incidence-angle SAR, RADARSAT-1 SAR, soil moisture, soil texture, surface roughness, synthetic aperture radar (SAR).

I. I NTRODUCTION

S

OIL moisture is a critical input parameter for various agricultural, hydrological, and meteorological applications. Conventional methods for measuring soil moisture are specific to a location and provide point estimates. Soil moisture varies both spatially and temporally, so point estimates cannot be extended to large areas with high accuracy. Thus, remote sensing methods are best suited for estimating spatial distribution of soil moisture over large agricultural areas. Furthermore, among Manuscript received June 27, 2007; revised October 25, 2007, May 5, 2008, and September 2, 2008. First published May 15, 2009; current version published July 23, 2009. H. S. Srivastava is with the Regional Remote Sensing Service Centre, Indian Space Research Organisation, Dehradun 248 001, India (e-mail: [email protected]). P. Patel and R. R. Navalgund are with the Space Applications Centre, Indian Space Research Organisation, Ahmedabad 380 015, India (e-mail: [email protected]; [email protected]). Y. Sharma is with the Department of Physics, Feroz Gandhi Post Graduate College, Rae Bareli 229 001, India (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2009.2018448

the various electromagnetic bands, the microwave bands have higher potential for remote sensing of soil moisture due to large difference between the dielectric constant of water (∼80) and that of dry soil (3–4) at microwave frequencies [1]. It is also sensitive to other target properties like surface roughness, crop cover, and soil texture [2]–[6]. Hence, it is necessary to incorporate the effects of these parameters while developing a soil moisture retrieval model. There have been studies to understand the effects of surface roughness and crop cover in the soil moisture retrieval model [7]–[10]. Verhoest et al. [11] adopted a fuzzy modeling approach to simulate soil moisture and the uncertainty in its retrieved value due to surface roughness for bare-soil surfaces. Blumberg et al. [12] empirically related synthetic aperture radar (SAR) backscatter to soil moisture and surface rms height using European Remote Sensing 1 (ERS-1) SAR backscatter. They also addressed the issue of eliminating the effect of surface roughness by operating a P-band scatterometer at 1◦ angle of incidence. Oh et al. [13] used ratios of co- and cross-polarized SAR data to develop an empirical model to invert soil moisture and surface roughness with the validity region over ks < 3. Dubois et al. [14] studied the angular behavior of multifrequency multipolarized SAR for developing an empirical algorithm for retrieval of soil moisture ◦ ◦ and surface roughness for bare soil using σHH and σVV SAR ◦ ◦ and soil moisture over vegetated areas using σHH and σHV , which is valid over regions with low normalized difference vegetation index (NDVI). Attempts have also been made by a few researchers to incorporate the effect of soil texture (soil type) in the soil moisture retrieval model [15]. Blumberg et al. [16] observed that higher correlation between soil moisture and SAR backscatter exists for sandy soil as compared to that for clay soil. They pointed out that, for clay, it is the higher content of clay that makes water molecules to be tightly bound with soil particles, which, in turn, restricts them to align with the incident radar signal. Blumberg et al. [17] demonstrated that it is possible to determine soil water saturation percent, which is independent of soil texture, and to monitor its changes along continuous time intervals using a P-band scatterometer. They observed that soil water saturation percent was in good agreement with gravimetric measurements of soil water content. Thus, a number of efforts have been made to address the issue of incorporating the effects of soil texture, surface roughness, and crop cover in soil moisture retrieval using SAR. However, there is a need to merge different approaches to arrive at a single methodology for large-area soil moisture estimation using SAR that can cater to a larger agricultural heterogeneity using operational spaceborne sensors. This paper describes the outcome of such a study using data from two predominantly wheat-growing agricultural areas, where various varieties of wheat are grown.

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SRIVASTAVA et al.: LARGE-AREA SOIL MOISTURE ESTIMATION USING RADARSAT-1 SAR DATA

II. C ONCEPTUALIZATION The work presented in this paper aims to incorporate the effects of surface roughness, crop cover, and soil texture on SAR sensitivity to soil moisture to reach to a methodology of soil moisture estimation in such a way that one need not make any assumptions about the distribution of these parameters. One should be able to incorporate the effects of these parameters from the space platform alone. Srivastava et al. [18]–[20] arrived at methodologies to incorporate the individual effect of these major parameters affecting SAR sensitivity to soil moisture. Each of these approaches is independently developed with a specific aim of incorporating the effect of a single given parameter while keeping the other parameters in a controlled condition. The aim of this paper is to ensure that the different approaches adopted for incorporating the effects of soil texture, surface roughness, and crop cover in soil moisture retrieval are combined and that a methodology for soil moisture retrieval is arrived at for soil moisture estimation over large areas. In order to ensure this, an experiment has been designed to develop models over one study area and to test the validity of models over a completely independent data set in terms of time and space.

A. Incorporating the Effect of Soil Texture Wet soil is a heterogeneous mixture of soil, water, and air pockets. In general, the water in it can be further divided into bound water and free water. The percentages of free water and bound water that are present in a soil medium largely determine the dielectric constant of a soil medium [15]. Moreover, the percentage of each depends on the surface area of soil particles present in the soil medium to a large extent. As the surface area of soil particles in a soil medium depends on the particle size and the relative proportions of various-sized particles in a given soil, the dielectric constant of wet soil varies with soil texture. Change in dielectric constant, in turn, results in a change in SAR backscatter. 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. This was achieved by representing soil moisture by a more realistic term that would relate to the possible amount of free water in a given soil–water mixture. For this purpose, soil moisture was represented in terms of water that is above the wilting point. Plants cannot uptake any water from the soil medium that is below the wilting point. This soil moisture measure is chosen owing to the fact that the amount of water at the wilting point (15-bar pressure) is very tightly held with soil particles. Thus, there is a strong synergy between bound water and water at 15-bar pressure as both represent the amount of water that is very tightly held with soil particles. Hence, it is reasonable to assume that the amount of soil moisture at the wilting point is proportional to the amount of bound water. Therefore, the amount of free water can easily be related to the difference between the observed soil moisture and the wilting point for that soil. Thus, we can assume that the amount of water that is free to interact with the incident microwaves, and gives significant contribution to SAR

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backscatter, is close to the amount of water above the wilting point per unit volume of soil given by [18] SM_WAP = (Observed soil moisture from sampling location −Soil Moisture at 15-bar pressure for the same location). (1) Srivastava et al. [18] conducted an experiment over smooth bare fields alone to demonstrate the effectiveness of the measure SM_WAP in incorporating the effect of soil texture in the soil moisture retrieval model. Selection of only smooth bare fields ensured that it is only the soil texture, other than soil moisture, that affects the SAR backscatter. SM_WAP was related to SAR backscatter σ ◦ by the following: SM_WAP = A + B ∗ (σ ◦ ).

(2)

Values of SM_WAP were derived using (1) with the help of the observed soil moisture from sampling location and of soil moisture at 15-bar pressure for the same location, and values of σ ◦ were extracted from the image corresponding to the respective sampling locations. It was reported that, by representing soil moisture in terms of SM_WAP, R2 increased considerably from 0.88 to 0.96 as compared to the case where soil moisture is represented as gravimetric soil moisture. At the same time, it was observed that the rms error for SM_WAP was the lowest at 0.62 as compared to that of 2.23 obtained for the model developed using gravimetric soil moisture over an independent data set. Although the study was carried out over bare smooth soil, to highlight the influence of soil texture alone, the results of the study suggested that SM_WAP is a measure that is a more realistic way of representing soil moisture as it describes the parameter affecting microwave interaction in the soil medium. It is expected that SM_WAP would be able to incorporate the effect of soil texture effectively, even in the case of bare soil with varying surface roughness conditions, as well as cropcovered fields. Hence, in this study, for monitoring of soil moisture over large agricultural areas with varying soil texture, SM_WAP was considered as soil moisture measure. SM_WAP can be converted to a conventional volumetric soil moisture using wilting point (i.e., soil moisture at 15-bar pressure) from easily available soil maps. B. Incorporating the Effect of Surface Roughness Surface roughness significantly affects SAR backscatter response from an agricultural land. It has been observed that the dynamic range of σ ◦ due to variation in surface roughness conditions is often comparable or even larger than that due to soil moisture variability [7]. Hence, for fallow fields, monitoring and mapping of soil moisture with higher accuracy call for incorporating the effect of surface roughness in a soil moisture retrieval model. There are two different approaches to this. The first approach is a theoretical one based on physical models [21]. These models simulate the radar backscatter from bare rough surfaces using deviation in surface height (rms height), autocorrelation function, associated correlation length, and dielectric constant as the input parameters [22]. The second approach is to incorporate a variable in the soil moisture retrieval model, which represents the surface roughness condition

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prevailing in the field and is obtainable from the satellite platform. Although the physical modeling approach has shown excellent agreement between the modeled and the observed values of radar backscatter coefficient, it is difficult to extend such techniques for mapping of soil moisture over a large agricultural area owing to their complexity. Alvarez-Mozos et al. [23] showed that there was a great dispersion between IEM simulations and observations at the field scale, leading to inaccurate estimation owing to spatial variability in surface roughness parameters. Similarly, a requirement of accurate surface roughness parameter (in the order of 20% of their whole variability range) was pointed out by Mattia et al. [24] while demonstrating an approach developed to use a priori information on soil moisture and surface roughness to constrain the integral equation model, as well as the geometric optics model, to improve soil moisture retrieval using ENVISAT-1 ASAR data. Moreover, the surface roughness heterogeneity between various fields falling in a large agricultural area makes it impractical to model the surface roughness distribution, which is a prerequisite for using a theoretical model. This calls for a simple and practical means to incorporate the effect of surface roughness information in the soil moisture retrieval model from the satellite platform. In this paper, an attempt is made to incorporate the effect of surface roughness in the soil moisture retrieval model by exploiting the angular behavior of multi-incidenceangle SAR. For a rough surface, the SAR backscatter signal strengths at low and high incidence angles are compatible with each other, whereas for a smooth surface, the SAR backscatter signal strength at a higher incidence angle is much less than that at low angle of incidence [25]. Thus, the difference between ◦ ◦ and σhigh is high for smooth fields and low for rough σlow fields. Srivastava et al. [19] attempted to incorporate the effect of surface roughness in the soil moisture retrieval model by exploiting the angular response of SAR backscatter to soil moisture. They conducted an experiment using data from agricultural fallow fields falling in the same soil texture class with varying surface roughness conditions, ensuring that it is only the surface roughness, other than soil moisture, that affects the SAR backscatter and not the crop cover or soil texture. They ◦ ◦ − σhigh ) could be used as an indicator of reported that (σlow the surface roughness conditions prevailing in the agricultural fields at the time of satellite pass. By adopting a similar approach in this paper, the effect of surface roughness on the soil ◦ ◦ − σhigh ) as moisture estimation is incorporated by using (σlow a surface roughness indicator, as given by ◦ ◦ ◦ SM_WAP = A + B ∗ (σlow . (3) ) + C ∗ σlow − σhigh It should be noted that the soil moisture measure is taken as SM_WAP [as defined in (1)], instead of the volumetric soil moisture that was used by Srivastava et al. [19] in line with the discussion given in the previous section.

Fig. 1.

View of wheat fields at the time of data acquisition.

the soil underneath. Therefore, even under crop-covered conditions, the low-incidence-angle SAR backscatter is affected by the underlying soil. Bush and Ulaby [26] conducted a study to understand the penetration of microwave signal in different cultural crops using a data set containing several hundred observations on corn, wheat, soybean, milo, and alfalfa crops at different growing stages over a period of three years. Based upon this study conducted over the 4–18-GHz range and an incidence angle ranging from 0◦ to 50◦ , they concluded that, for soil-related studies over cultural crops, frequency should be below 6 GHz, and angle of incidence should not exceed 20◦ . Similarly, Brown et al. [27] observed that, for a wheat canopy in the ear emergence stage, the difference in SAR backscatter between dry and wet field conditions was in the order of 5 dB at low angle of incidence. However, it was observed that, at higher angle of incidence (around 35◦ –40◦ ), the effect of moisture status of underlying soil reduces significantly. Several researchers have indicated that higher incidence angle increases the path length of SAR signal through the vegetation volume, resulting in higher interaction with crop canopy [28]–[30]. Srivastava et al. [20] observed that, for crop-covered fields, the ◦ , can be taken as return signal at higher incidence angle, σhigh a crop canopy descriptor as it represents the overall effect of crop cover (combined effect of crop type, crop structure, crop volume, canopy moisture, etc.) in the soil moisture retrieval model. An empirical model for soil moisture retrieval was developed [20] for wheat-crop-covered soil by including an ◦ in the soil moisture retrieval model additional term of σhigh as a crop canopy descriptor while controlling other parameters such as soil texture and surface roughness. In this paper, the soil moisture measure used is SM_WAP, ensuring that the restriction on soil texture class in which the soil sample belongs ◦ is used in the soil moisture retrieval is relaxed. Hence, σhigh model to incorporate the effect due to the large heterogeneity within wheat crop introduced due to various variety-related differences, as well as the differences in date of sowing (Fig. 1). Thus, for the wheat-crop-covered area, the soil moisture retrieval model is given by ◦ ◦ . (4) ) + C ∗ σhigh SM_WAP = A + B ∗ (σlow III. D ATA S ET AND S TUDY A REA

C. Incorporating the Effect Due to Crop Cover It is well known that C-band SAR at low angle of incidence has shorter path length within vegetation volume, and hence, a low-incidence-angle SAR signal is able to penetrate up to

As discussed in Section II, the methodology proposed for large-area soil moisture estimation is based upon acquisition of a pair of low- and high-incidence-angle SAR data sets. Since there are currently no operational SAR configurations that can


cater to simultaneous acquisition of the two angle SAR data, the time lag between the two angle SAR data acquisitions could result into a major limitation of the methodology in case of a precipitation event taking place between the two angle data acquisitions. It is expected that, in the future, with the launch of multiple spaceborne SAR sensors, one can expect the time gap between multi-incidence-angle SAR data acquisitions to reduce significantly, leading to less chances of precipitation event to occur between the two angle data acquisitions, thereby reducing the errors occurring due to precipitation event between the two acquisitions. Ideally, one should use simultaneously acquired two incidence angle data; however, due to technological limitations, the authors have used multi-incidence-angle data from RADARSAT-1 SAR, with a time gap of three days, with no precipitation event having taken place between the two acquisitions. Separation between the two angles of acquisitions also needs to be examined critically. As described in Section II, the methodology proposed in this paper depends upon the angular response of SAR to surface roughness and crop cover. As the angle separation increases, the dependence of surface roughness on the difference of SAR backscatter acquired at low and high incidence angles also increases. For example, in an earlier study conducted by the authors using 16◦ (EL-1) and 23◦ (S1) RADARSAT-1 SAR data to evaluate the impact of reducing the separation of incidence angle between the two acquisitions on soil moisture error, a 7◦ separation of incidence angle (23◦ − 16◦ = 7◦ ) resulted in a soil moisture error of 4.38 root-mean-square error (rmse) (rmse between the observed and the estimated soil moisture) as against the rmse of 2.65 found in this paper with an angle separation of 20◦ . Hence, in order to reduce errors in soil moisture retrieval due to surface roughness, the angle of high-incidence-angle SAR data should not be close to that of low-incidence-angle SAR data. Similarly, for the case of crop cover, the path length that the SAR signal travels within vegetation of, for example, height h, is [h ∗ sec(θ)]. Thus, larger separation between the angles of SAR acquisitions would result in increased path length within crop for high-incidence-angle SAR. Hence, the effect due to vegetation can be better incorporated in the soil moisture retrieval model, as proposed in this paper. Two pairs of low- and high-incidence-angle RADARSAT-1 SAR scenes were acquired over two different test sites. RADARSAT-1 is a C-band (5.3-GHz) SAR sensor with HH-polarization and varying incidence angle. Details of data acquisition are given in Table I. The low-incidence-angle SAR was acquired using extended low-1 beam mode (EL-1) of RADARSAT-1 SAR with 16◦ central incidence angle, and the high-incidence-angle SAR was acquired using Standard beam mode-4 (S-4) of RADARSAT-1 SAR with 36◦ central incidence angle. The nominal spatial resolutions of EL-1 and S-4 RADARSAT-1 SAR pixels were 35 and 25 m, respectively. For the model development site, the dates of acquisition for the lower and higher incidence angle data were March 13 and 16, 1999, whereas the SAR acquisition dates for the model validation site were March 12 and 15, 2005, respectively. Hence, the data acquisition between the two sites was temporally separated by six years (1999 for the model development site and 2005 for the

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TABLE I DETAILS OF THE DATA USED FOR MODEL DEVELOPMENT AND M ODEL V ALIDATION

model validation site). Optical data from Indian Remote Sensing Satellite (IRS) Linear Imaging Self-Scanning-III (L-III) were also used to delineate crop-covered fields from bare fields. The spectral bands used to generate FCC using IRS L-III are green (0.52–0.59 μm), red (0.62–0.68 μm) and near infrared (0.77–0.86 μm), with a spatial resolution of 23.5 m. The overpass times of IRS L-III and RADARSAT-1 SAR are around 10:30 A . M . and 06:00 P. M ., respectively. In order to achieve the goal of arriving at the methodology, two test sites were selected, namely, one for developing a comprehensive soil moisture retrieval model, and the other for validating the model. The first test site covers parts of Agra, Mathura, and Bharatpur districts, India, and the second test site covers parts of Saharanpur and Haridwar districts, India. The geographical coordinates of the four corners of the model development site are 77◦ 13 07 E and 27◦ 31 50 N, 78◦ 05 07 E and 27◦ 40 46 N, 78◦ 13 10 E and 27◦ 03 11 N, and 77◦ 22 25 E and 26◦ 53 46 N, whereas the geographical coordinates of the four corners of the model validation site are 77◦ 25 55 E and 30◦ 01 33 N, 77◦ 52 56 E and 30◦ 07 05 N, 77◦ 58 50 E and 29◦ 39 08 N, and 77◦ 32 25 E and 29◦ 32 16 N. Both study areas are characterized by mostly flat level terrain and include irrigated, as well as unirrigated, agricultural land and therefore provide a full range of soil moisture. The first test site consists of fine loamy, coarse loamy, fine silty, sandy, and fine-textured soils, while the second test site comprises fine loamy, coarse loamy, fine silty, and sandy soils. IV. M ETHODOLOGY A. Reconnaissance Survey and Ground-Truth Collection A detailed field data collection campaign was carried out in synchrony (within ±2 h) with RADARSAT-1 passes. Sampling locations that were adjacent to the ground control points (GCPs) and larger than 100 × 100 m2 were selected. GPSbased mobile mapping unit and 1 : 50 000 scale Survey of India (SOI) topographic maps were used for fieldwork and identification of sampling locations in the image. In March, for both sites, wheat was the major crop type at the grain-filling stage. During the field observations, a lot of heterogeneities within wheat crop existed due to differences in varieties, as well as differences in sowing date ranging between 0 and 20 days.

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position in the range direction. The header information was used for the calculation of α, the local incidence angle at each pixel [31]. After conversion of DN to σ ◦ , speckle suppression was carried out using the enhanced Lee filtering algorithm [32]. C. Image Processing

Fig. 2. Roughness plate used for estimation of rms height and correlation length of soil surface.

A total of 43 soil samples were collected for developing the model from the first site, comprising 17 from bare fields and 26 from wheat-crop-covered fields. For the validation site, a total of 32 soil samples were collected, of which 10 were from bare fields and 22 were from wheat-crop-covered fields. Thus, a total of 75 soil samples (43 from the model development site and 32 from the model validation site) were available for the study. During ground truth, soil samples at 0–5-cm soil depth were collected with the help of a tube auger and a core sampler for the measurement of gravimetric soil moisture and bulk density. Other field information like soil cover, crop-related information, and associated field conditions were also recorded. The volumetric soil moisture values varied between 8% and 42% for the first test site, and approximately similar range of soil moisture values was observed for the second test site (9%–45%). A pressure plate instrument was used to calculate soil moisture at 15-bar pressure for arriving at the wilting point of all the 75 sampling locations. Subsequently, SM_WAP was derived for all the 75 sampling locations using (1) with the help of the observed volumetric soil moisture from sampling location and of soil moisture at 15-bar pressure for the same location. Digital photographs were taken for all sampling fields. Surface roughness was measured for bare fields from the digital photographs of the soil profile projected on a roughness plate (Fig. 2). The orientation of the roughness plate was aligned in line with RADARSAT-1 viewing so as to acquire the surface profile similar to that viewed by the SAR sensor. The rms height and correlation length were estimated for all the 27 bare-soil fields. For the first site, the lowest and highest values of rms height were observed to be 0.5 and 2.8 cm, respectively, with corresponding associated correlation lengths of 9.0 and 3.0 cm. For the validation site, surface roughness conditions were more or less similar to that of the first site with the lowest and highest values of rms height of 0.4 and 3 cm, respectively, with corresponding associated correlation lengths of 11 and 2.6 cm.

The RADARSAT-1 S-4 image of March 16, 1999, over parts of Agra, Mathura, and Bharatpur districts and the image of March 15, 2005, over parts of Saharanpur and Haridwar districts were georeferenced separately using the GCPs from 1 : 50 000 scale SOI topographic maps. The registration accuracy for map to SAR image was in the order of 1.5 pixels. The remaining SAR (EL-1) and optical (IRS L-III) images acquired over parts of Agra, Mathura, and Bharatpur districts and parts of Saharanpur and Haridwar districts were then registered to the respective georeferenced (S-4) images using the nearest neighborhood resampling method [33]. After georeferencing of all the data sets, rail/road/canal networks and groundtruth locations were digitized and transferred on the image. Maximum-likelihood classification [34] was carried out on the georeferenced optical (IRS L-III) images acquired over the two test sites to delineate cropped and bare fields for both study areas. All the ground-truth sampling locations were identified on the images, and their backscattering coefficient values were extracted from SAR images. Once the backscattering coefficient values were obtained, soil moisture retrieval models were developed, as described in detail in the following section. V. R ESULTS AND D ISCUSSION In order to arrive at a soil moisture estimation methodology, models were developed over the first test site and were validated over the second test site. For both of the test sites, crop type and its growth stage are kept the same to demonstrate the extendibility of the soil moisture retrieval model for a given crop at a particular growth stage to a spatially and temporally different location for the same crop in similar growth stage. However, the model for bare soil is more general as it is generated for a wider range of soil surface roughness conditions. The following sections describe the results in detail. A. Model Development

The DN image values were converted to radiometrically calibrated SAR backscatter (σ ◦ ) values using the following: σ o dB = 10 × log10 (DN 2 + offset)/gain + 10 × log10 (sin(α)) (5)

As discussed in Section II, all the 75 ground measured soil moisture values were converted into SM_WAP. A subset of 43 SM_WAP values from the first test site (17 from bare soil and 26 from crop-covered soil) was used for model development. For bare-soil conditions, the soil moisture retrieval model was developed by carrying out multiple linear regression analysis with SM_WAP as dependent variable and correspond◦ ◦ ◦ and (σlow − σhigh ), extracted from ing SAR backscatter, σlow the multi-incidence-angle SAR image pair as independent variables, using (3). The estimated coefficients are given as follows: ◦ ◦ ◦ . (6) +1.51 ∗ σlow −σhigh SM_WAP = 36.93+2.75 ∗ σlow

where DN is the digital number of SAR image, which is in amplitude, and α is the local incidence angle at that pixel

The coefficient of determination (R2 ) was found to be 0.93 for the aforementioned model with 2.84 standard error of

B. Radiometric Calibration (DN -to-σ ◦ Conversion)


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TABLE II RESULTS OF REGRESSION ANALYSIS AND VALIDATION FOR B ARE AND C ROP -C OVERED F IELDS

estimate (SEE). The F statistic for the same was observed to be 92.88. The details of the developed model are given in Table II. Since these coefficients were generated over a wide range of soil texture classes (fine loamy, coarse loamy, fine silty, sandy, and fine-textured soils) and surface roughness conditions (rms height ranging from 0.5 to 2.8 cm), the model coefficients can be used “as is” at other agricultural areas having soil texture and surface roughness ranges that are similar to the ranges over which the model is developed. For the crop-covered case, the soil moisture retrieval model was developed by carrying out multiple linear regression analysis with SM_WAP as dependent variable and correspond◦ ◦ and σhigh , extracted from the multiing SAR backscatter, σlow incidence-angle SAR image pair as independent variables, using (4). The estimated coefficients are given as follows: ◦ ◦ . (7) ) − 1.36 ∗ σhigh SM_WAP = 36.09 + 3.59 ∗ (σlow The coefficient of determination (R2 ) was found to be 0.95 for the aforementioned model with SEE of 1.89. The F statistic for the same was observed to be 204.89. The details of the developed model are given in Table II. While the aforesaid model is able to take care of withincrop variations of wheat crop due to various varieties and differences in sowing dates, it cannot be extended to other crop types “as is” since SAR is also sensitive to the structure of the crop under observation. The model coefficients being empirical would represent only the variability that exists in the data set from which the coefficients were derived. Hence, at this stage of the study, the model coefficients are valid only for wheat crop. In this sense, the study does not claim to offer “models” but rather “methodology,” which can be adapted to derive distinct empirical coefficients for different structured crops at different growth stages for soil moisture retrieval. The attempt in this study is to test the validity of the coefficients derived for wheat crop over an independent location elsewhere, where wheat crop is at similar growth stage. Hence, the basic structure of the crop remains the same. In the future, it is planned to conduct experiments over a wider range of similar structured crop types and test the model for its validity by adopting an approach that is similar as the one reported in this paper. B. Model Validation Validation is an essential component of a statistical approachbased analysis. For this purpose, it is essential to have a

Fig. 3. Scatter plot of the observed soil moisture from farmers’ fields versus the estimated soil moisture from the model for the validation data set.

validation data set consisting of independent observations of the parameter to be estimated, and these observed values of the parameters should not be used to arrive at an estimate of the parameter. Hence, an independent validation site was selected, which is different both in space and in time. Moreover, the sample size for validation was taken as 10 for the baresoil condition and 22 for the crop-covered condition, which is significantly larger than the minimum number of data points required for model validation determined using the precision power approach, as suggested in [35], keeping the criteria that the sample correlation coefficient is not to decrease by more than 0.05, no matter what the expected value of correlation coefficient is. For carrying out the validation exercise, the models developed for retrieval of soil moisture in terms of SM_WAP, over the first test site (parts of Agra, Mathura, and Bharatpur), were validated over the second test site (parts of Saharanpur and Haridwar districts). The results obtained are shown in Table II. When the model for the bare-soil condition of the first test site (6) was applied to the ten observations of the validation test site having the bare-soil condition (i.e., parts of Saharanpur and Haridwar districts), the rmse between the observed and estimated values of soil moisture was found to be 2.65. When the model for wheat-crop-covered soil of the first test site was applied to the 22 observations of the validation test site having wheat crop cover, the rmse between the observed and estimated values of soil moisture was found to be 1.81. Fig. 3 shows the scatter plot of the estimated soil moisture (in SM_WAP) using model versus soil moisture (in SM_WAP) observed from farmers’ fields for the validation site under bare-soil and wheat-crop-covered-soil conditions. As can be observed, the SEE (2.65) for the model of bare-soil conditions is higher than that (1.81) of the model for crop-covered-soil conditions. The behavior of rmse of the models clearly reflects the variability covered in the data set on which the models are developed. The bare-soil-condition model presented in this paper covered a wider range of surface roughness conditions, whereas the crop-covered model presented in this paper was restricted to

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wheat crop in the grain-filling stage. Thus, the model for cropcovered soil accounts for a limited variability due to a single crop, and it does not cater to mixed crop conditions, which leads to better characterization of the effect of vegetation (wheat crop). This results to lower values of rmse for the model for wheat-crop-covered soil as compared to bare-soil conditions, which accounts for a larger surface roughness variability. The low values of rmse between the observed and estimated values of soil moisture for the bare-soil, as well as the wheat-cropcovered-soil, case indicates that the methodology developed in this paper can be used for estimation of soil moisture over large agricultural areas. VI. C ONCLUSION This paper presented the outcome of a study taken up to develop a methodology for large-area soil moisture estimation using multi-incidence-angle SAR data. While, for the case of bare soils, the study offers an empirical model that can be applied over a wider range of surface roughness conditions, for the case of crop-covered soil, empirical coefficients are developed for wheat crop at the grain-filling stage. Hence, although the study offers a methodology for large-area soil moisture estimation, the only vegetated terrain considered in this paper is that of wheat crop at the grain-filling stage, so the study is not offering “operational models” that can cater to all crops at different growth stages. At the same time, the mixed-vegetation issue is not yet addressed. Hence, the developed empirical models cannot be used for areas of mixed vegetation with global satellite missions. However in the future, the authors do intend to study the impact of mixed vegetation on the soil moisture retrieval accuracy using the methodology proposed in this paper. Thus, the study offers a methodology and not models for large-area soil moisture estimation. Encouraging results obtained in the validation exercise, with the validation site being disjointed in space and time, support the soundness of the developed methodology. At the same time, the soil moisture measure SM_WAP, which represents soil moisture in terms of water above the wilting point (the water that the plant can uptake from the soil medium), used in this paper as an effort to incorporate the effect due to soil texture is more meaningful information for many agricultural researchers. The significant outcome of this paper is that it offers an approach to incorporate the effects of surface roughness, crop cover, and soil texture in the soil moisture retrieval model from the space platform, without making any assumptions on the distributions of these parameters or without knowing the actual values of these parameters on ground. ACKNOWLEDGMENT The authors would like to thank Dr. V. Jayraman [Director of the National Remote Sensing Center, Hyderabad, India, and Former Director of the Regional Remote Sensing Service Centre/National Natural Resources Management System (RRSSC/NNRMS)] for his keen interest. H. S. Srivastava and P. Patel would like to thank Dr. Y. V. N. Krishnamurthy (Director of RRSSC/NNRMS), Dr. K. P. Sharma (Head of RRSSC Dehradun), Dr. J. S. Parihar (Deputy Director of the

Remote Sensing Applications Area), Dr. M. Chakraborty (Group Director of the Geo-Informatics and Techniques Development Group), and Dr. S. Mohan (Head of the Advance Techniques Development Division) for their encouragement and support. R EFERENCES [1] J. R. Wang, “The dielectric properties of soil–water mixtures at microwave frequencies,” Radio Sci., vol. 15, no. 5, pp. 977–985, 1980. [2] M. C. Dobson and F. T. Ulaby, “Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part III—Soil tension,” IEEE Trans. Geosci. Remote Sens., vol. GRS-19, no. 1, pp. 51–61, Jan. 1981. [3] F. M. Henderson and J. L. Lewis, Principles and Applications of Imaging Radar—Manual of Remote Sensing, vol. II. Hoboken, NJ: Wiley, 1998, pp. 407–425. [4] F. T. Ulaby, P. P. Batlivala, and M. C. Dobson, “Microwave backscatter dependence on surface roughness, soil moisture and soil texture, Part—I: Bare soil,” IEEE Trans. Geosci. Electron., vol. GE-16, no. 4, pp. 286–295, Oct. 1978. [5] F. T. Ulaby, G. A. Bradley, and M. C. Dobson, “Microwave backscatter dependence on surface roughness, soil moisture and soil texture, Part—II: Vegetation covered soil,” IEEE Trans. Geosci. Electron., vol. GE-17, no. 2, pp. 33–40, Apr. 1979. [6] W. Wagner and C. Pathe, “Has SAR failed in soil moisture retrieval?” in Proc. Envisat ERS Symp., Salzburg, Austria, Sep. 6–10, 2004, pp. 1745– 1751. ESA SP-572. [7] M. Borgeaud, E. Attema, G. Salgado, A. Bellini, and J. Noll, “Analysis of bare soil surface roughness parameter with ERS-1 SAR data,” in Symp. Extraction Bio Geophys. Parameter From SAR Data Land Appl., Toulouse, France, 1995, pp. 307–316. [8] P. Patel, S. Panigrahy, and M. Chakraborty, “Performance of RADARSAT-1 extended low beam mode SAR data for soil moisture retrieval,” Asian J. Geoinf., vol. 2, no. 3, pp. 85–91, 2002. [9] M. G. Wooding, Satellite Radar in Agriculture: Experience With ERS-1. Noordwijk, The Netherlands: ESA Publication, 1995. 1185. [10] N. Baghdadi, N. Holah, and M. Zribi, “Soil moisture estimation using multi-incidence and multi-polarization ASAR data,” Int. J. Remote Sens., vol. 27, no. 10, pp. 1907–1920, May 2006. [11] N. E. C. Verhoest, B. De Baets, and H. Vernieuwe, “A Takagi–Sugeno fuzzy rule-based model for soil moisture retrieval from SAR under soil roughness uncertainty,” IEEE Trans. Geosci. Remote Sens., vol. 45, no. 5, pp. 1351–1360, May 2007. [12] D. G. Blumberg, V. Freilikher, I. V. Lyalko, L. D. Vulfson, A. L. Kotlyar, V. N. Shevchenko, and A. D. Ryabokonenko, “Subsurface microwave remote sensing of soil–water content: Field studies in the Negev Desert and optical modeling,” Int. J. Remote Sens., vol. 23, no. 19, pp. 4039– 4054, 2002. [13] Y. Oh, K. Sarabandi, and F. T. Ulaby, “An empirical model and an inversion technique for radar scattering from bare soil surfaces,” IEEE Trans. Geosci. Remote Sens., vol. 30, no. 2, pp. 370–381, Mar. 1992. [14] P. C. Dubois, J. Van Zyl, and E. T. Engman, “Measuring soil moisture with imaging radar,” IEEE Trans. Geosci. Remote Sens., vol. 33, no. 4, pp. 915–926, Jul. 1995. [15] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive, vol. II. Norwood, MA: Artech House, 1990a, pp. 860–863. [16] D. G. Blumberg, V. Freilikher, I. V. Lyalko, L. D. Vulfson, A. L. Kotlyar, V. N. Shevchenko, and A. D. Ryabokonenko, “Soil moisture (watercontent) assessment by an airborne scatterometer: The Chernobyl disaster area and the Negev desert,” Remote Sens. Environ., vol. 33, pp. 915–926, 2000. [17] D. G. Blumberg, G. Ronen, J. Ben-Asher, V. Freilikher, L. D. Vulfson, and A. L. Kotlyar, “Utilizing a P-band scatterometer to assess soil water saturation percent of a bare sandy soil,” J. Hydrol., vol. 318, no. 1–4, pp. 374–378, Mar. 2006. [18] H. S. Srivastava, P. Patel, and R. R. Navalgund, “Incorporating soil texture in soil moisture estimation from extended low-1 beam mode RADARSAT-1 SAR data,” Int. J. Remote Sens., vol. 27, no. 12, pp. 2587– 2598, Jun. 2006. [19] H. S. Srivastava, P. Patel, M. L. Manchanda, and S. Adiga, “Use of multi-incidence angle RADARSAT-1 SAR data to incorporate the effect of surface roughness in soil moisture estimation,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 7, pp. 1638–1640, Jul. 2003.


[20] H. S. Srivastava, P. Patel, M. L. Manchanda, and S. Adiga, “An attempt to incorporate the effect of crop cover in soil moisture estimation using multi-incidence angle RADARSAT-1 SAR data,” Asian J. Geoinf., vol. 2, no. 3, pp. 33–40, 2002. [21] A. K. Fung, Microwave Scattering and Emission Models and Their Applications. Norwood, MA: Artech House, 1994. [22] M. Davidson, T. Le Toan, F. Mattia, G. Satalino, T. Manninen, and M. Borgeaud, “On the characterization of agricultural soil roughness for radar remote sensing studies,” IEEE Trans. Geosci. Remote Sens., vol. 38, no. 2, pp. 630–640, Mar. 2000. [23] J. Alvarez-Mozos, J. Casali, M. Gonzalez-Audicana, and N. E. C. Verhoest, “Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 4, pp. 913–924, Apr. 2006. [24] F. Mattia, G. Satalino, L. Dente, and G. Pasquariello, “Using a priori information to improve soil moisture retrieval from ENVISAT ASAR AP data in semiarid regions,” IEEE Trans. Geosci. Remote Sens., vol. 44, no. 4, pp. 900–912, Apr. 2006. [25] F. T. Ulaby, R. K. Moore, and A. K. Fung, Microwave Remote Sensing: Active and Passive, vol. III. Norwood, MA: Artech House, 1990b, pp. 1823–1830. [26] T. F. Bush and F. T. Ulaby, “An evaluation of radar as a crop classifier,” Remote Sens. Environ., vol. 7, pp. 15–36, 1978. [27] S. C. M. Brown, S. Quegan, K. Morrison, J. C. Bennet, and G. Cookmartin, “High-resolution measurements of scattering in wheat canopies—Implications for crop parameters retrieval,” IEEE Trans. Geosci. Remote Sens., vol. 41, no. 7, pp. 1602–1609, Jul. 2003. [28] B. Brisco, R. J. Brown, J. G. Grains, and B. Snider, “Temporal groundbased scatterometer observations of crops in Western Canada,” Can. J. Remote Sens., vol. 18, no. 1, pp. 14–21, 1992. [29] J. F. Paris, “Radar backscattering properties of corn and soybeans at frequencies of 1.6, 4.75 and 13.3 GHz,” IEEE Trans. Geosci. Remote Sens., vol. GRS-21, no. 3, pp. 392–400, Jul. 1983. [30] K. S. Shanmugan, F. T. Ulaby, V. Narayanan, and C. Dobson, “Crop classification using multidate/multifrequency radar data,” Remote Sens. Lab., Univ. Kansas, Lawrence, KS, Tech. Rep., 360-17, 1981. [31] RSI, RADARSAT Illuminated. Your Guide to Products and Services, 2000, Richmond, BC, Canada: RADARSAT Int., Client Serv. [32] J. S. Lee, “Speckle suppression and analysis of SAR images,” Opt. Eng., vol. 25, no. 5, pp. 636–643, May 1986. [33] M. J. Duggin and C. J. Robinove, “Assumptions implicit in remote sensing data acquisition and analysis,” Int. J. Remote Sens., vol. 11, no. 10, pp. 1669–1694, Oct. 1990. [34] A. S. Robert, Techniques for Image Processing and Classification in Remote Sensing. New York: Academic, 1983. [35] G. P. Brooks and R. S. Barcikowski, “Precision power and its application to the selection of regression sample sizes,” Mid-West. Educ. Res., vol. 9, no. 4, pp. 10–17, 1996.

Hari Shanker Srivastava received the Postgraduate degree in physics (gold medalist) from Kanpur University, Kanpur, India, in 1990. Since 1997, he has been with the Regional Remote Sensing Service Centre, Indian Space Research Organisation (ISRO), Dehradun, India, whereas he was with the Space Applications Centre from 1991 to 1997. He has contributed significantly in various projects on soil moisture estimation, agricultural studies, crop yield estimation, wetland, forestry, human settlement, InSAR, PolSAR, and PolInSAR using multiparametric microwave data from a variety of sensors and platforms, e.g., ERS-1/ERS-2 SAR, JERS-1 SAR, SIR-C/X-SAR, RADARSAT-1 SAR, ENVISAT-1 ASAR, ISRO Airborne SAR, DLR E-SAR, AMSR-E, and ground-based scatterometer. He is also a Coinvestigator of the Canadian Space Agency’s RADARSAT-2 Announcement of Opportunity (A.O.) project on “Soil moisture, surface roughness, and vegetation parameter retrieval using SAR polarimetry” and the European Space Agency’s SMOS Cal/Val A.O. project on “Validation of SMOS soil moisture information by upscaling field measurements to multi-parametric SAR derived spatial soil moisture map at 1 km grid and further upscaling to 25 km grid.”

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Parul Patel received the Postgraduate degree in statistics from The Maharaja Sayajirao University of Baroda, Baroda, India, in 1985. Since 1988, she has been with the Space Applications Centre, Indian Space Research Organisation, Ahmedabad, India. She is actively involved in microwave remote sensing activities being carried out in India. She has carried out a number of investigative studies on target interaction using data from a variety of ground-based to spaceborne sensors. She is the Principal Investigator of the Canadian Space Agency’s RADARSAT-2 Announcement of Opportunity (A.O.) project on “Soil moisture, surface roughness, and vegetation parameter retrieval using SAR polarimetry” and the European Space Agency’s SMOS Cal/Val A.O. project on “Validation of SMOS soil moisture information by upscaling field measurements to multi-parametric SAR derived spatial soil moisture map at 1 km grid and further upscaling to 25 km grid.”

Yamini Sharma received the Postgraduate degree in physics from the Indian Institute of Technology (IIT), Kharagpur, India, and the Ph.D. degree in solid state plasma from Chhatrapati Shahu Ji Maharaj University, Kanpur, India. Since 1989, she has been a Reader with the Department of Physics, Feroz Gandhi Post Graduate College, Rae Bareli, India. Her research interests include microwave remote sensing and condensedmatter physics related to electronic properties of materials. She is also involved in ab initio calculations of electronic properties of semiconductors, which are experimentally verified through measurements of electronic momentum densities using a Compton spectrometer. She has several collaborative projects with the Compton Profile Laboratory, Mohanlal Sukhadia University, Udaipur, India, and the Inorganic Materials and Nanocomposites Laboratory, Department of Chemistry, IIT-Kharagpur.

Ranganath R. Navalgund received the Postgraduate degree in physics from the Indian Institute of Technology, Mumbai, India, in 1970 and the Ph.D. degree in physics from the Tata Institute of Fundamental Research, Mumbai, in 1977. Since 1977, he has been with the Indian Space Research Organisation, Ahmedabad, India, whereas he was the Director of the National Remote Sensing Centre, Hyderabad, from 2001 to 2005 and is currently the Distinguished Scientist and Director of the Space Applications Centre (SAC). SAC is engaged in the design and development of communication and navigation payloads, electro-optical and microwave sensors for Earth and planetary observation, associated data processing, and ground systems and applications. His main area of work relates to development and execution of space applications for societal benefits. Dr. Navalgund is the recipient of the Maharana Udaisingh Award for Environment (2008–2009), the Bhaskara Award of ISRS (2006), the VASVIK Award (2003), the ASI Award, the Indian National Remote Sensing Award (1992), the Prof. K. R. Ramanathan Memorial Lecture Gold Medal (2002), and the Outstanding Leadership Award by the International Society for Photogrammetry and Remote Sensing (2000–2004). He represents India on the Group on Earth Observations.