An Improved Soil Moisture Retrieval Algorithm Based on the Land Parameter Retrieval Model for Water–Land Mixed Pixels Using AMSR-E Data

Abstract

The land parameter retrieval model (LPRM) has been widely used for the retrieval of microwave-based global surface soil moisture (SSM). In the original LPRM algorithm, soil surface effective temperature (SSET) was retrieved from the Ka-band (36.5 GHz) brightness temperature (BT) and then used as input data for the estimation of SSM. In this study, SSET retrieved from the Advanced Microwave Scanning Radiometer of the Earth Observing System Radiometer (AMSR-E) Ka-band BT was validated against MODIS land surface temperature (LST) data, and the results show that when the fraction of water surface (FWS) within an AMSR-E pixel increases in the range from 0–0.01 to 0.15–0.4, the root-mean-square error (RMSE) increases drastically from 1.98 to 11.42 Kelvin (K). When validation was conducted on SSET data retrieved with the K-band (18.7 and 23.8 GHz) BT, the RMSEs were maintained below 2.0 K for FWS ranging from 0 to 0.4. Therefore, in the original LPRM, we substituted the Ka-band BT with the K-band BT to produce more accurate estimations of SSET and hence improved SSM retrievals. Our results show that for FWS ranges of 0–0.01, 0.01–0.05, 0.05–0.15, and 0.15–0.4, the correlation coefficients ( $R$ -values) between SSM retrieved with the improved LPRM and Global Land Data Assimilation System (GLDAS) measurements are 0.78, 0.75, 0.77, and 0.46, respectively, compared with the corresponding $R$ -values of 0.78, 0.65, 0.56, and 0.34 for SSM retrieved with the original LPRM. This demonstrates that against the original LPRM, our improved LPRM can produce more accurate SSM retrievals under water–land mixed pixels.