Estimating Soil Moisture over Winter Wheat Fields during Growing Season Using RADARSAT-2 Data

Abstract

Soil moisture content (SMC) is a significant factor affecting crop growth and development. However, SMC estimation, based on synthetic aperture radar (SAR), is influenced by a variety of surface parameters, such as vegetation cover and surface roughness. As a result, determining the SMC across agricultural areas (e.g., wheat fields) remotely (i.e., without ground measurement) is difficult to achieve. In this study, a model-based polarization decomposition method was used to decompose the original SAR signal into different scattering components that represented different scattering mechanisms. The different volume scattering models were applied, and then the results were compared in order to remove the scattering contribution from vegetation canopy, and extract the surface scattering components related to the soil moisture. Finally, by combining extensively used surface scattering models (e.g., CIEM and Dubois), and a method of roughness parameters optimization, a lookup table was developed to estimate the soil moisture during the wheat growth period. When CIEM is applied, the R2 and RMSE of the SMC are 0.534, 5.62 vol.%, and for the Dubois model, 0.634, 5.16 vol.%, respectively, which indicates that this approach provides good estimation performance for measuring soil moisture during the wheat growing season.

Keywords

• soil moisture
• polarization decomposition
• CIEM
• Dubois model
• RADARSAT-2