ESA soil moisture downscaling using NOAA images

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate soil moisture. Hence Soil moisture retrieved from satellite microwave remote sensing normally has spatial resolution on the order of tens of kilometers, which are too coarse for many hydrological applications such as agriculture monitoring and drought prediction. Various downscaling methods have been proposed to enhance the spatial resolution of satellite soil moisture products. The aim of this study is to propose a statistical assimilative method to downscale European Space Agency's Water Cycle Multi-mission Observation Strategy and Climate Change Initiative (ESA CCI) Microwave (MW) remote sensing soil moisture products. For this purpose, firstly we used the NOAA images and NDVI, LST and albedo indices in regression process to International Soil Moisture Network (ISMN) in-situ SM. Then we downscaled the ESA products by making proportion between results and ESA products. Because of some limitations, we operated on three study area, Kyeamba Creek catchment area in Australia and two areas in Parsabad in Iran. Validation results of each area were evaluated using ground data and results showed average R2 in 0.77, for Australia, and 0.59 and 0.34 for two case study in Iran. According to the results, it can be said that the proposed method, in addition to scalability and simplicity, has a higher productivity in uniform and unmixed areas such as Kyeamba Creek catchment, than agricultural land such as Parsabad.