Roughness characterization and disaggregation of coarse resolution SMAP soil moisture using single-channel algorithm

Abstract

Surface roughness is a crucial parameter for the estimation of soil moisture (SM). The present study attempted to optimize the surface roughness parameter (h) for the estimation of SM from Soil Moisture Active Passive (SMAP) using tau-omega (τ-ω) model and also downscaled the estimated SM product using a polynomial regression relation among Normalized Difference Vegetation Index (NDVI), land surface temperature (LST), and SM. The brightness temperature of SMAP available at two spatial resolutions (36 and 9 km) was used for two seasons intended for SM assessment. After assessment with in-situ SM data, 9-km SM data values were further used for spatial disaggregation to obtain the optimized downscaled soil moisture (ODSM) at 1 km. Results showed that the variation in the value of the roughness parameter strongly affects the performance of the τ-ω model and the downscaling performances. The investigation provided lowest values of root-mean-square error (RMSE) to be 0.0518 (at h = 0.35) and 0.0480 (at h = 0.25) for the SM estimation at 36 km for the different seasons used in this study while the lowest values of RMSE for ODSM were found to be 0.0365 (at h = 0.4) and 0.0252 (at h = 0.25, 0.3) for different seasons. © 2021 Society of Photo-Optical Instrumentation Engineers (SPIE).