Synergetic utilization of sentinel-1 SAR and sentinel-2 optical remote sensing data for surface soil moisture estimation for Rupnagar, Punjab, India

Abstract

Due to improper agricultural and soil management, there has been a drop in crop yield over the last few years and food security has become a major issue. For a country like India, with a huge population to cater, the problem becomes more serious. Since the inception of remote sensing in scientific agriculture management, optical remote sensing along with field data has been used for soil health monitoring and mapping. SAR or microwave remote sensing has an all-weather and high temporal data availability which has found applications for various domains. For soil health studies of multiple soil classes and sub-classes of same type, both aerial and spaceborne SAR remote sensing is currently in use for a multitude of monitoring and parameter modelling approaches. This study utilizes Sentinel 1 A, C-band SAR remote sensing data with VV and VH polarization channels for surface soil moisture estimation for alluvial soil and its sub-types in Rupnagar of Punjab state in India. While Index based OLS Regression method for soil moisture estimation was done using backscatter from Sentinel-1A SAR data, it was validated using Normalized Differential Moisture Index (NDMI) generated from Sentinel 2 optical datasets. This approach though did not consider the actual soil moisture data from field yet gave a low Root Mean Squared Error (RMSE) of 0.5 and R2-statistics of 0.72 (72%) in training and testing phases. The Index based OLS (Ordinary Least Squares) Regression method for soil moisture estimation aims to establish a technique for cases when field data is either not available or the study area is not easily accessible. In the statistical approach with field data, the same OLS model, when replaced by on-field surface soil moisture data gave a RMSE of 1.9 and R2-statistics of 0.968 and 0.948 in training and testing phases respectively at 97.5% confidence level. The study is significant for using freely available optical and SAR remote sensing data parameters synergistically, in a simplified manner for surface soil moisture estimation. The results have comparable accuracies given by studies using commercial data and complex modelling approaches.