Soil Moisture Retrieval From Multipolarization SAR Data and Potential Hydrological Application

Abstract

The high spatial-temporal variability of soil moisture necessitates monitoring at a high resolution in order to improve our understanding of Earth system processes. Current large-scale soil moistures inferred from the microwave satellites have limited spatial resolution, typically in the range of tens of kilometers. Recent studies have revealed that synthetic aperture radar (SAR) backscatter exhibits qualitative relationships with soil moisture, suggesting the potential for large-scale high-resolution mapping of soil moisture. Here, we proposed a method for directly estimating soil moisture content based on the advanced integral equation model and Mironov dielectric model. The approach involves establishing a series of semiempirical models, independent of preceding surface roughness determination, using two Envisat advanced synthetic aperture radar (ASAR) alternating polarization (AP) model precision products. We generate a time series of high-resolution soil moisture using Envisat ASAR AP data acquired from 2004 to 2011, with an uncertainty of approximately 0.05 m3/m3. Our soil moisture retrievals demonstrate very good agreement with European Space Agency Climate Change Initiative soil moisture products and the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis hourly products, even in the absence of synchronous ground measurements. Furthermore, our study reveals good temporal coherence between drought and heavy rainfall events, and SAR-derived soil moisture, which suggests a potential to capture heavy rainfall and drought events. We conclude that SAR-derived soil moisture is a more direct and efficient method in quantifying soil moisture at a high spatial resolution, making it more suitable for watershed scale hydrological studies.

Keywords

• Advanced integral equation model (AIEM)
• hydrology
• synthetic aperture radar (SAR)
• soil moisture content