Soil moisture evaluation using machine learning techniques on synthetic aperture radar (SAR) and land surface model

Abstract

There have been several efforts to utilize satellite-based synthetic aperture radar (SAR) measurements to determine surface soil moisture conditions of agricultural regions. The results have been mixed since the relation between the SAR signal and surface soil moisture is confounded by variations in topographic features, surface roughness and vegetation density etc. We designed an experiment to investigate SAR based soil moisture retrieval using different machine learning techniques. In addition, a high resolution land surface model customized and deployed for generating soil moisture at 250m resolution using various static and dynamics input data.