Retrieval and validation of soil moisture from FRS-1 data set of radar imaging satellite (RISAT-1)

Abstract

In this work, we estimated soil moisture (SM) by using dielectric constant properties of soil with radar backscattering coefficient (bc) from simulated annealing techniques of RISAT-1 (radar imaging satellite, based on synthetic aperture radar (SAR) technique) data. We examined the performance of simulated annealing in retrieving SM where the vegetation cover is not very high (NDVI ≈ 0.35 for wheat-dominated area on January 19, 2013).To overcome the land surface model limits on SM estimation accuracy, point measurement spatial coverage limits, and microwave remote sensing spatial-temporal sampling limits, we reduced uncertainties through a combination of these approaches. Near-surface SM measurements from the 5.35-GHz (C-band) channels of RISAT-1 were collocated against ground-truth data (collected during the flight time of RISAT-1 over the study area), to establish SAR-SM relationships for FRS-1(circular horizontal (RH) and circular vertical (RV)) data set of RISAT-1. Comparison with the limited ground-based point (total 24 points) measurements of SM content exhibited a net improvement when near-surface SM observations were assimilated. Comparison of the SM derived from the sigma naught (σ 0) (of RISAT-1 data set FRS-1) using the inversion algorithm with the observed measurements (using time domain reflectometry) of SM showed root mean square error of 0.24, nRMSE = 0.03, R-RMSE = 0.38, MAE = 0.63, NRMSE = 1.02, NSE = 1, d = 0.87, r 2 = 0.65, and RMSE% = 12.79 for RH polarized image while RV polarized image failed each statistical test for predicting good SM with respect to the observed SM. The ability to extract additional information comes at the expense of including more measurements, especially at frequencies lower than the L-band. This approach is therefore intended for future space-borne systems.