Comparison of Passive and Active Remotely Sensed Microwave Soil Moisture Retrievals using Soil Moisture Simulations (GLDAS) over Different Land Covers in East Asia: using SMOS, ASCAT, AMSR2, and FY-3B

Abstract:

Soil moisture is a key variable in environmental systems since water and energy fluxes at the surface and atmosphere interface are strongly dependent on soil moisture. Furthermore, soil moisture has been identified as one of the “Essential Climate Variables” expected to improve climate predictions and near-future forecasting. Several studies have been conducted to acquire soil moisture estimates from spaceborne microwave instruments. As a results, soil moisture data is now globally available using several kinds of satellites with different temporal or spatial resolutions. In this study, we investigate four satellite-based soil moisture products, Soil Moisture and Ocean Salinity (SMOS), Advanced Scatterometer (ASCAT), Advanced Microwave Scanning Radiometer-2 (AMSR2), and Fengyun-3B (FY-3B), compared to an independent reference, Global Land Data Assimilation System (GLDAS) soil moisture datasets over East Asia. Biosphere Atmosphere Transfer Scheme (BATS) dataset was utilized for land cover classification. The relationship between the GLDAS soil moisture and satellite products was analyzed by using of temporal correlation, unbiased root mean square difference, mean bias, and lagged variables. Especially, over the arid regions (deserts and semi deserts), SMOS showed the best consistency with GLDAS and it was found that ASCAT soil moisture exhibit best correlation versus GLDAS except desert and semi desert regions (Figure 1.). In addition, performances of AMSR2 soil moisture products based on Land Parameter Retrieval Model (LPRM) and FY-3B over East Asia were also very encouraging (the period 2013).