Soil moisture estimation using ERS 2 SAR data: a case study in the Solani River catchment/Estimation de l’humidité du sol grâce à des données ERS-2 SAR: étude de cas dans le bassin de la rivière Solani

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.

Accuracies of Soil Moisture Estimations Using a Semi-Empirical Model over Bare Soil Agricultural Croplands from Sentinel-1 SAR Data

“…In soil studies, they are site-specific and generally valid for specific soil characteristics [3]. Previous semi-empirical studies have considered single polarization to build a relationship between soil moisture and a backscatter model at 10 cm depth [9] and estimated ϑ v with a root mean square error (RMSE) of 3-6% [10][11][12] using C-band data. There have also been studies that have used the SAR interferometry technique and Sentinel-1 data to estimate soil moisture and compare them with in situ measurements [13].…”

Surface Moisture and Vegetation Cover Analysis for Drought Monitoring in the Southern Kruger National Park Using Sentinel-1, Sentinel-2, and Landsat-8

“…Earth observation-based soil moisture missions, such as SMOS (soil moisture and ocean salinity) and SMAP (soil moisture active passive) are using L-band microwave techniques for the retrieval of global soil moisture information with coarse spatial resolution (around 50 km) [28,29]. During recent years, various studies were carried out utilizing C-band SAR (Synthetic Aperture Radar) data, from e.g., ERS-1/2 (European Remote Sensing) [30][31][32], ENVISAT ASAR (Environmental Satellite-Advanced Synthetic Aperture Radar) [33][34][35], as well as RADARSAT [36][37][38], in various observation modes for soil moisture applications on different scales. The new Sentinel-1 C-band SAR, with high spatial resolution and repetition rate, offers new perspectives in the retrieval of surface moisture information with C-band SAR [39].…”

Spatiotemporal Variability of Arctic Soil Moisture Detected from High-Resolution RADARSAT-2 SAR Data

“…e utility of ANNs in remote sensing (see review in [45]) and as a relatively simple way to invert radar backscatter to surface parameters is well documented [41,56]. Surface parameter inversion from SAR backscatter data is usually carried out by some sort of linear or nonlinear regression approach [17] or through complex empirical inversion models such as IEM and others [57,58]. ANNs, however, show great promise in both simplifying this procedure and increasing the accuracy of the results.…”

“…e demand for spatially distributed soil moisture is clear, but point measurement data are often insufficient, that is, due to soil heterogeneity, land use, and topography. In short, soil moisture may be very different in space and time from one point to another [17][18][19].…”