Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment

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1 Hydrological Sciences Journal des Sciences Hydrologiques, 49(2) April Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment S. S. HAIDER, S. SAID, U. C. KOTHYARI & M. K. ARORA Department of Civil Engineering, Indian Institute of Technology Roorkee, Roorkee , India umeshfce@iitr.ernet.in Abstract The information regarding spatial and temporal variation of soil moisture in a catchment is of utmost importance in hydrological, as well as many other studies. Point measurements from gravimetric and other methods for soil moisture determination are insufficient to understand the spatial behaviour of soil moisture in a region. Microwave remote sensing data from active sensors on board various satellites are increasingly being used to map spatial distribution of soil moisture within the 0 10 cm top surface. The northern part of India has a network of large rivers and canals and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on the hydrology of the region. In this paper, results on estimation of soil moisture from an ERS-2 SAR image in the catchment of the Solani River (a tributary to the River Ganga) in and around the town of Roorkee, India, have been presented. The radar backscatter coefficient for each pixel of the image has been modelled from the digital numbers of the SAR image. Gravimetric measurements have been made simultaneously during the satellite pass to determine the concurrent value of volumetric soil moisture at a large number of sample points within the satellite sweep area. The backscatter coefficient is found to vary from 30 db to 42 db for a variation in soil moisture from 30 to 75%. Regression analyses between volumetric soil moisture and both the digital numbers and backscatter coefficients were performed. Strong correlations between volumetric soil moisture and digital number were observed with R 2 values of 0.84, 0.75 and 0.83 for bare soil, vegetative and combined surfaces, respectively. A similar trend was observed with the relationship between backscatter and volumetric soil moisture with R 2 values of 0.60, 0.89 and 0.67 for bare soil, vegetative and combined surfaces, respectively. These results demonstrate the utilization of SAR data for estimation of spatial distribution of soil moisture in the region of the present study. Key words backscatter coefficient; digital number; infiltration; soil moisture; areal estimation; remote sensing; ERS-2 SAR; runoff; River Solani, India Estimation de l humidité du sol grâce à des données ERS-2 SAR: étude de cas dans le bassin de la rivière Solani Résumé L information sur la variation spatiale et temporelle de l humidité du sol dans un bassin versant est d une importance capitale dans de nombreuses études, non seulement hydrologiques. Les mesures ponctuelles par gravimétrie, et par d autres méthodes, sont insuffisantes pour comprendre la répartition spatiale de l humidité du sol dans une région. Les données micro-ondes télédétectées fournies par des capteurs actifs embarqués à bord de divers satellites sont de plus en plus utilisées pour cartographier la distribution spatiale de l humidité des 10 premiers centimètres du sol. Le Nord de l Inde présente un réseau de grandes rivières et de canaux et, par conséquent, l humidité du sol y est liée de manière significative avec l hydrologie. Cet article présente des résultats d estimation de l humidité du sol à partir d une image ERS-2 SAR, dans le basin versant de la rivière Solani (affluent du Gange), au sein et autour de la ville de Roorkee, en Inde. Le coefficient de rétrodiffusion de chaque pixel de l image a été modélisé à partir des valeurs numériques de l image SAR. Des mesures gravimétriques ont été réalisées lors du passage du satellite afin de déterminer la valeur correspondante d humidité volumétrique du sol en un grand nombre de points d échantillonnage au sein de la zone vue par le satellite. Le coefficient de rétrodiffusion varie entre 30 et 42 db pour une humidité du sol variant entre 30 et 75%. Des analyses de régression ont été réalisées, mettant en rapport d une part l humidité volumétrique du sol, et d autre part la valeur numérique du pixel et le Open for discussion until 1 October 2004

2 324 S. S. Haider et al. coefficient de rétrodiffusion. Des corrélations fortes ont été observées entre l humidité volumétrique du sol et la valeur numérique du pixel, avec des valeurs de R² de 0.84, 0.75 et 0.83 pour, respectivement, un sol nu, une surface végétale et une surface mixte. Une tendance similaire a été observée pour la relation entre le coefficient de rétrodiffusion et l humidité volumétrique du sol, avec des valeurs de R² de 0.60, 0.89 et 0.67 pour, respectivement, un sol nu, une surface végétale et une surface mixte. Ces résultats correspondent à l utilisation de données SAR pour l estimation de la distribution spatiale de l humidité du sol dans la région de cette étude. Mots clefs coefficient de rétrodiffusion; valeur numérique; infiltration; humidité du sol; estimation spatiale; télédétection; ERS-2 SAR; écoulement; rivière Solani, Inde INTRODUCTION Knowledge about the spatial distribution of soil moisture in a region is a pre-requisite for many hydrological studies, such as river flow forecasting resulting from rainfall storm events, planning, designing and scheduling of irrigation systems and soil conservation programmes. Soil moisture also influences the infiltration and runoff processes, as the hydraulic conductivity and water intake capacity of soil during a rainfall event are markedly determined by the soil moisture content. However, despite its importance, information on soil moisture has not found a widespread application in hydrological modelling processes. This may be mainly due to the difficulty in the measurement of soil moisture at spatial and temporal scales within the catchment areas. The conventional point measurement based methods, such as the neutron probe or gravimetric method, are not appropriate for understanding of the spatial and temporal behaviour of soil moisture. Due to the heterogeneity of soil type, land use and topography, soil moisture may change considerably in space during any given time interval. Fortunately, advances in microwave remote sensing, both passive and active, have demonstrated the potential to map the spatial extent of soil moisture both for bare and thinly vegetated surfaces. The brightness temperature from the passive sensor and backscatter coefficient from the active sensor are strongly related to volumetric soil moisture (Chauhan, 1997). However, the fine resolution data obtained from active microwave sensors such as Synthetic Aperture Radar (SAR) on board many satellites namely ERS-1 and 2, Radarsat, JERS-1 and ENVISAT, can be more useful for estimating the spatial variation of soil moisture for its application in hydrological studies. Perhaps the most important feature of the microwave sensor data feature is the capability to penetrate clouds and, to some extent, vegetation. The degree of penetration into the ground surface depends upon the moisture content and the density of the vegetation as well as the wavelength of the microwaves. For example, longer wavelengths penetrate deeper than the shorter wavelengths. A number of scientific experiments using microwave sensors on trucks, aircraft and satellites (Blanchard & Chang, 1983; Dubois & Engman, 1995; Jackson et al., 1984; Mohan et al., 1994) have shown that the moisture within a thin layer of surface soil, of the order of 0 5 cm, can be accurately determined using microwave remote sensing for bare soil and thinly vegetated surfaces. However, the measurement of soil moisture is affected by a number of target characteristics such as surface roughness, vegetation cover, dielectric constant and topography (Ulaby et al., 1978). Nevertheless, judicial choice of sensor parameters, such as the angle of incidence, polarization and frequency, can minimize these effects. For instance, although surface roughness is related to wavelength, an

3 Soil moisture estimation using ERS 2 SAR data 325 angle of incidence less than 15 or 20 may minimize the effects of roughness (Ulaby et al., 1978). Over the years, there have been many studies conducted on the use of microwave remote sensing for soil moisture estimation in various regions of the world (Ijjas & Rao, 1992). In India, most of the studies have focused on understanding the behaviour of soil moisture in relation to agricultural plot studies (Mohan et al., 1994; Rao et al., 1987; Singh, 1994) and, thus, there appears to be lack of reported work on soil moisture assessment by microwave remote sensing that is directed towards its utility for hydrological studies. In particular, northern India has a vast network of rivers and canals, and, therefore, spatial and temporal distribution of soil moisture in this region has a significant bearing on understanding the hydrological processes in the region. Since spatial distribution of soil moisture is a key factor that affects the process of partitioning rainfall into runoff and other components, the present study has the objective of investigating the usefulness of microwave remote sensing data obtained from the ERS-2 SAR (C-band) sensor to establish the relationship between the radar backscatter and the volumetric soil moisture content in the region of interest. The results obtained from the study are presented. SOIL MOISTURE ESTIMATION FROM ERS-2 SAR DATA The backscatter coefficient, σ, obtained from SAR sensors, is related to the local topographic conditions, surface roughness and dielectric constant of the soil. The significantly high difference in dielectric constant between water and dry soil, and its variation, is an indicator of soil moisture concentration. The coefficient σ is composed of backscatter from vegetation, σ v, and from soil, σ s, and the attenuation caused by the vegetation canopy, L. This relationship can be expressed as (Engman & Gurney, 1990): σ = σ v + σ s /L (1) The parameter σ s has a direct association with volumetric soil moisture, M v, given by (Engman & Gurney, 1990): σ s = R s S M v (2) where R s and S are surface roughness and soil moisture sensitivity terms, respectively. Although these terms may vary with the variation in wavelength, polarization and incidence angle of the radar beam, there is no satisfactory theoretical model suitable to estimate these terms independently. Therefore, an empirical relationship between the measured backscatter and soil moisture, which is approximately linear, is generally established. Thus, a linear regression between the SAR data, in the form of digital numbers (DN), and M v can be stated as: M v = A + B(DN) (3) where A and B are constants. Since, DN is quantized value of the backscatter coefficient, a similar empirical relationship between σ and volumetric soil moisture may be written as (Schultz & Engman, 2000):

4 326 S. S. Haider et al. σ = A exp( B M ) (4) v where A and B are constants and º is measured on a linear scale. Equation (4) may be rewritten in db as: σ ( db ) = 10log A B M v (5) which can further be simplified to: σ db ) = A + S( M ) (6) ( v where A = 10logA and S = 4.34B. HereS is defined as the radar sensitivity to soil moisture. Thus, for regression analysis, the quantities M v and either DN (equation (3)) or º (db) (equation (6)) are required at some sample locations. The value of M v may be determined concurrently by the gravimetric method applied in situ on soil samples from those locations. The DN values are the 16-bit SAR data. The value of º(dB) may be derived from these DN values using the following algorithm. The ERS-2 SAR data can be obtained in four forms, namely raw (RAW), precision product (PRI), single look complex (SLC) and geocoded (GEC) products. Among these, PRI is the standard product for SAR radiometric precision analysis, and is used in this study also. A direct and simple derivation procedure described at is used here for extracting the backscatter coefficient from PRI data. The procedure is based on the following assumptions: (a) The terrain is flat. The variation in incidence angle is solely due to the reference ellipsoid. It varies from about 19.5 at near range to about 26.5 at far range of the radar beam. (b) Any change in incidence angle across a distributed target is neglected (a distributed target corresponds to an average value of the incidence angle within a window of say 3 3 pixels). Based on these assumptions, the relationship between DN and º may be given as: σ [ DN] 2 = Cons tan t = f ( α) σ (7) sin α where f( ) is a function that depends on local incidence angle, and can be expressed as: sin α ref f ( α) = k (8) sin α where k is the calibration constant and and ref are the average and reference incidence angles, respectively. The value of k is specific to the type of data product and the location of the processing centre. In fact, k is valid only for data acquired after 13 July 1995, since ERS-2 SAR (PRI) images were considered uncalibrated before this date. The value of k for ERS-2 SAR (PRI) data used in this study acquired on 23 July 2001 and processed at the National Remote Sensing Agency (NRSA), Hyderabad, India is (on a linear scale). Further, since the local incidence angle varies from about 19.5 at near range to about 26.5 at far range, the reference incidence angle may be taken as the average of these two angles (i.e. 23 ). Taking into account various sources of radiometric and stability errors, the backscatter coefficient of a distributed target is given by:

5 Soil moisture estimation using ERS 2 SAR data 327 ij 1 σ = = N ij= N sin α DN ij k sin α ref C Product replica power Reference replica power Power loss where C is the factor that accounts for updating the gain due to the elevation antenna pattern and N is the number of pixels within the area of interest (AOI) (i.e. the distributed target), DN ij is the digital number corresponding to the pixel at location (i,j) and the average in the square parentheses may be calculated following the application of mean filter using a 3 3 pixel window to reduce the speckle effect in the SAR data. The detailed description of other terms is provided at adeos, and has not been given here. For the ERS-2 SAR (PRI) product used in the present study, the factor C is already applied by the data-procuring agency. There is no variation in the replica pulse power with respect to calibration (reference) pulse power in ERS-2 data. Also, correction for power loss due to analogue digital converter (ADC) is required to be applied in these data only when roughly measured backscatter coefficient is greater than 2 db. Since for present data this is not the case therefore the backscatter coefficient for the distributed target is thus finally given by: 1 ij σ 2 = = N DN N k ij = 1 1 sin α ij (10) sin α ref Further, when the AOI is only a single pixel, then the backscatter coefficient for the pixel at location (i,j) is given by: 2 1 sin α i σ = DN ij (11) k sin α ref where α i is the average incidence angle for a pixel at location i. (9) STUDY AREA AND DATA The present study was conducted in the catchment area of the Solani River, which is a tributary of the River Ganga and flows through the vicinity of Roorkee, India. The area is covered with a variety of mixed vegetation in addition to bare land and built-up areas. The vegetation constitutes short as well as tall agricultural plants. Microwave remote sensing data from the ERS-2 SAR sensor (C band; 5.3 GHz frequency, VV polarization) collected on 23 July 2001 were acquired from NRSA. The georeferenced image of the full scene, with scene centre coordinates, N latitude and longitude is shown in Fig. 1. From this image, a subset image of size ( ) pixels was considered. Since soil moisture conditions change quickly over a period of time, a field survey was also conducted to collect soil samples at the selected locations on the date of acquisition of SAR data. A total of 23 samples (both vegetated and bare soil surfaces) were taken from the top 10-cm thick soil layer. An existing land cover classification of the same area, produced from the IRS-1B LISS II sensor, was used to demarcate the land cover type of the soil sample locations as vegetated or bare.

6 328 S. S. Haider et al. Fig. 1 Georeferenced image of the ERS-2 SAR (PRI) data. METHODOLOGY A series of operations was performed to derive soil moisture from the ERS-2 SAR data, as described below. Georeferencing of the ERS-2 SAR (PRI) image Georeferencing is performed to conform the image to a ground reference system such as a map projection system. The ERS-2 SAR (PRI) image was georeferenced to geographical coordinates of five ground control points (GCP) (four at the corners and one at centre of the image) extracted from the topographical map (Survey of India map, Sheet no. 53 G/13). The georeferenced image containing the study area is given in Fig. 1. Determination of soil moisture using the gravimetric method From the soil samples collected during the field survey, soil moisture was determined using the gravimetric method. The computed soil moisture values for these samples are given in the last two columns of Table 1. Identification of digital number (DN) at sample locations The geographical coordinates of the sample locations were used to identify those on the georeferenced ERS-2 SAR image, and the corresponding digital numbers of the pixels at those sampling locations were obtained (Table 1). Derivation of local incidence angle for sampling locations The local incidence angles for all the sampling locations were derived using an inhouse program written in C-language according to the algorithm provided at

7 Soil moisture estimation using ERS 2 SAR data 329 Table 1 Digital number and angle of incidence of the ERS-2 SAR (PRI) data and soil moisture obtained by the gravimetric method for various sampling locations. Sample no. Land cover type Digital number, DN Angle of incidence, α Backscatter coefficient (db) Moisture content by weight (%) Volumetric soil moisture, M v (%) 1 Bare soil Bare soil Bare soil Vegetated soil Vegetated soil Bare soil Bare soil Vegetated soil Bare soil Bare soil Vegetated soil Bare soil Bare soil Bare soil Vegetated soil Bare soil Vegetated soil Bare soil Bare soil Bare soil Bare soil Vegetated soil Vegetated soil The values of the incidence angles,, thus computed are given in Table 1. Extraction of backscatter coefficient for sampling locations The backscatter coefficient for each sampling location was extracted using the method described above (equation (9)), applicable at pixel level. The calibration constant (k) for the data set considered is on a linear scale. The ERS-2 reference incidence angle (α ref ) is taken as 23 (i.e. the average of the near range incidence angle 19.5 and the far range angle 26.5 ). There are three correction factors in equation (9): two of them elevation antenna pattern correction and replica pulse power variations correction are not applicable to present the data set used here. The third correction factor (i.e. correction for ADC power loss), the parameter 10log(Intensity/k), was calculated for all the sampling locations and it was found that this parameter ranged between 13 and 20 db. Since these values are less than 2 db, the correction for ADC power loss was also not applied. Hence, for the present case, equation (9) reduced to equation (11), which was used to derive the backscatter coefficient in the PRI product. Accordingly, the extracted backscatter coefficients for all the sampling locations are listed in Table 1.

8 330 S. S. Haider et al. RESULTS AND DISCUSSION Regression analysis was performed to investigate the relationship between measured volumetric soil moisture and the corresponding DN values, and backscatter coefficient extracted from ERS-2 SAR (PRI) data. The regression analysis was performed individually using samples from bare soil and from vegetated soil. The combined effect of these variables on soil moisture was also investigated. Only 23 soil moisture samples were available. Therefore, all of these were utilized for developing the regression relationships. Relationship between volumetric soil moisture and DN The logarithmic model of the regression relationship between DN and volumetric soil moisture given by equation (3) was used. The variation of soil moisture from bare and vegetated soils, individually and in combination, was studied. The analysis of ERS-2 SAR (PRI) data shows that the DN values ranged between 100 and 200 for a variation in volumetric soil moisture of 30 75%. Figure 2(a) (c) shows the relationship between the volumetric soil moisture and the digital number for bare soil, vegetated soil and for the combined data, respectively. The regression relationships derived for these areas are: Bare soil: M v = ln( DN) R 2 = 0.84 (12) Vegetated soil: M v = ln( DN) R 2 = 0.75 (13) Combined data set: M v = ln( DN) R 2 = 0.83 (14) Relatively higher R 2 values obtained for equations (12) (14) illustrate the high correlation between volumetric soil moisture and the DN values of the ERS-2 SAR data. Relationship between volumetric soil moisture and backscatter coefficient Regression analysis between soil moisture and the backscatter coefficient was also performed using equation (6), separately for bare soil and vegetated soil, and for the combined data. Figure 3(a) (c) graphically describes the relationship between the volumetric soil moisture and the backscatter coefficient for bare soil, vegetated soil and for the combined data. The backscatter coefficient was found to vary from 30 to 42 db for the present data. The following relationships were derived: Bare soil: M = 6.01σ R 2 = (15) v Vegetated soil: M = 3.44σ R 2 = 0.89 (16) v Combined data set: M = 5.43σ R 2 = 0.67 (17) v Relatively high R 2 values again demonstrate strong correlations between backscatter coefficient and the volumetric soil moisture. However, in this case, the

9 Soil moisture estimation using ERS 2 SAR data 331 (a) (b) (c) Fig. 2 Relationship between soil moisture (0 10 cm depth) and ERS-2 SAR digital number: (a) for bare soil, (b) for vegetated soil, and (c) for the combined data set (bare and vegetated soil). correlation between M v and σ for the vegetated surface is higher than that for the bare surface, which is counterintuitive. This may be ascribed to the possible error in estimation of σ from DN values, since corrections for surface roughness and

10 332 S. S. Haider et al. (a) (b) (c) Fig. 3 Relationship between volumetric soil moisture (0 10 cm depth) and ERS-2 SAR backscatter coefficient (a) for bare soil, (b) for vegetated soil, and (c) for the combined data set (bare and vegetated soil). topography could not be applied. Nevertheless, the accuracy of the proposed relationships for estimation of soil moisture is considered to be satisfactory because the cartographic and measurement errors are endemic in such an analysis. Other functional forms of the relationships for soil moisture were also attempted, but those were found

11 Soil moisture estimation using ERS 2 SAR data 333 to be less satisfactory than those presented here. The spatial distribution of soil moisture derived through the use of the proposed relationships should find applications in many research areas such as rainfall runoff modelling, scheduling of irrigation systems, etc. CONCLUDING REMARKS The effects of soil moisture from bare and vegetative soils, individually and in combination, on digital number and backscatter coefficient of the ERS-2 SAR data have been studied. The analysis of ERS-2 SAR (PRI) data carried out for this purpose shows that the DN values ranged between 100 and 200 for a variation in volumetric soil moisture of 30 75%. The backscatter coefficient for these varied from 30 to 42 db. The Linear Imaging Self Scanning Sensor-III (LISS-III) data were also analysed and their classification into barren and vegetated areas was performed through the supervised classification technique. Empirical relationships for estimating volumetric soil moisture from digital number and from backscatter coefficient of the ERS-2 SAR data were obtained with relatively higher values of R 2. The backscatter coefficient was found to be linearly related with the volumetric soil moisture according to the relationship given by Ulaby et al., (1978). It was also found from the regression analysis that the volumetric soil moisture is proportional to the logarithm of the ERS-2 SAR digital number. Relationships proposed herein for estimation of soil moisture may have useful applications for hydrological modelling and other such studies. Acknowledgements The authors wish to sincerely thank Professor Jerry C. Ritchie and the other anonymous reviewer whose comments greatly improved the quality of the paper. REFERENCES Blanchard, B. J. & Chang, A. T. C. (1983) Estimation of soil moisture from Seaset SAR data. Paper no , Water Resour. Bull. 19(5), Chauhan, N. S. (1997) Soil moisture estimation under a vegetation cover: combined active passive microwave remote sensing technique. Int. J. Remote Sens. 18(5), Dubois, P. C. & Engman, T. (1995) Measuring soil moisture with imaging radars. IEEE Trans. Geosci. Remote Sens. 33(4), Engman, E. T. & Gurney, R. J. (1990) Remote Sensing in Hydrology. Chapman & Hall, London, UK. Ijjas, G. & Rao, Y. S. (1992) Passive microwave remote sensing of soil moisture from aircraft in Hungary. Int. J. Remote Sens. 13(3), Jackson, T. J., Schmugge, T. J. & O Neill, P. (1984) Passive microwave remote sensing of soil moisture from an aircraft platform. Remote Sens. Environ. 14, John, B. (1992) Soil moisture detection with airborne passive and active microwave sensors. Int. J. Remote Sens. 13(3), Mohan, S., Mehta, N. S., Mehta, R. L., Patel, P., Rajak, D. R., Sristava, H. S., Das, D. K., Sharma, S., Saxena, C. M. & Sutrodhar, A. K. (1994) A methodology for soil moisture estimation using ERS-1 SAR data. Nat. Symp. on Microwave Remote Sensing & Users meet, Rao, K. S., Chandra, G. & Raju, C. S. (1987) A comparative study of different dielectric models computation of representative dielectric profiles of black soils. J. Ind. Soc. Remote Sens. 15(2), Schultz, G. A. & Engman, E. T. (2000) Remote Sensing in Hydrology and Water Management. Springer Verlag, Berlin, Germany. Singh, A. N. (1994) Monitoring change in the extent of salt affected soils in northern India. Int. J. Remote Sens. 15(16),

12 334 S. S. Haider et al. Ulaby, F. T., Batlivala, P. B. & Dobson, M. C. (1978) Microwave backscatter dependence on surface roughness, soil moisture, and soil texture: Part I Bare soil. IEEE Trans. Geosci. Electron. GE-16(4), Received 21 March 2003; accepted 24 January 2004