Estimation of surface soil moisture by integrating environmental data and remote-sensing satellites

Abstract

Soil moisture (SM) or soil water content is a critical variable in the climate system and a key parameter in earth surface processes. This study aimed to assess citizen observatory (CO) data's suitability to develop a method to estimate surface SM distribution using Sentinel-1B and Landsat 8 data; acquired between January 2019 and June 2019. Three approaches were developed and compared using multiple linear regression (MLR), regression-kriging (RK) and cokriging (CK). MLR provided more realistic spatial patterns over the landscape, even in a data-poor environment. RK was found to be a potential tool to refine the results, while CO was found to be less effective. The obtained results showed that CO data harmonised with Sentinel-1B SAR, Landsat 8, and terrain data could estimate and map soil moisture content.