A spatio-temporal cross comparison framework for the accuracies of remotely sensed soil moisture products in a climate-sensitive grassland region

Abstract

Abstract Soil moisture is a key variable in hydrological processes and is included in a wide range of global datasets; however, accurate indications of spatio-temporal characteristics of soil moisture remain limited for East Asia. This study focused on Hulunbeir, a typical climate-sensitive grassland area in East Asia. Four soil moisture datasets were retrieved from Global Land Data Assimilation System (GLDAS), CMA Land Data Assimilation System (CLDAS), Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP), and the differences between these datasets and measured soil moisture data were analyzed by correlation analysis. The key factors affecting soil water, such as slope, precipitation and normalized difference vegetation index (NDVI), were selected to identify the main causes of errors between various product data and measured data by multiple stepwise regression analysis. The four products tended to underestimate the soil moisture, but exaggerated the temporal variation in winter and spring. However, the CLDAS and SMAP products well captured the amplitude and spatio-temporal variation of the soil moisture. The GLDAS product performed well with acceptable error metrics but failed to capture the vertical variation of soil moisture. The SMOS product had a missing data problem, and dampen it in the study area. Notably, NDVI and precipitation were the main factors leading to the underestimation by the above four products. This study provides an important reference for accurately describing the hydrological characteristics of climate-sensitive grassland areas to achieve efficient management of water resources.