Downscaling SMOS/SMAP soil moisture product using high resolution Radarsat-2 SAR data: a case study in southern Ontario

Abstract

Soil moisture is a key component in the water cycle. Continuous observations of soil moisture over large spatial scales are important in many earth sciences applications. Current soil moisture products derived from SMOS and SMAP satellites can provide global coverage with 2-3 days cycle but have very coarse resolutions (~40 km), which limits the soil moisture products in many applications where a resolution of 1-10 km is generally needed. SAR imagery is available at high resolution and has high sensitivity to soil moisture. However the soil moisture retrieval from SAR depends on high volume of in-situ soil moisture data and is also complicated by their sensitivity to surface roughness and vegetation. This study proposes an algorithm for retrieving high resolution soil moisture by downscaling SMOS/SMAP soil moisture products using time series dual-polarized (HH and HV) Radarsat-2 data. The approach can overcome the effect of vegetation, surface roughness, and change of scales. Specifically, the effect of vegetation is removed by the water-cloud model, in which the conditions of vegetation are characterized by the backscatter coefficient of Radarsat-2 HV polarization. Time series Radarsat-2 data is used to eliminate the dependence of backscattered signal on soil surface roughness. Different mathematical models including wavelet transform are used for scale change. The algorithm is validated using in-situ soil moisture data collected in Southern Ontario in the spring and summer of 2016. The study shows promising results in soil moisture retrieval over large area.