Fan, XW; Liu, YB; Gan, GJ; Wu, GP (2020). SMAP underestimates soil moisture in vegetation-disturbed areas primarily as a result of biased surface temperature data. REMOTE SENSING OF ENVIRONMENT, 247, 111914.

The Soil Moisture Active Mission (SMAP) baseline V-pol single channel algorithm (SCA-V) retrieves soil moisture (SM) based on the 2000-2010 Moderate-resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) climatology. This parameterization scheme might affect SM retrievals in the context of vegetation disturbances, e.g., as a result of drought. By referencing the European Space Agency (ESA) Climate Change Initiative (CCI), the Global Land Data Assimilation System (GLDAS), the Soil Moisture and Ocean Salinity (SMOS) L3 (SMOS-L3) and the SMOS-IC SM datasets, this study investigated the effects of SMAP vegetation parameterization on SMAP-reference SM differences in global land areas that are subject to differing vegetation disturbances. The results show that SMAP might underestimate SM by similar to 0.007 m(3).m(-3) for a relative NDVI decrease of 10%. The underestimation might be primarily caused by low biases in the Global Modeling and Assimilation Office (GMAO) surface temperature. Although using NDVI climatology might cause an overestimation of SM (due to an overestimation of vegetation effects and an underestimation of surface roughness effects), the concurrent underestimation of the GMAO surface temperature might cause an even larger underestimation of SM. The vegetation biases, particularly the surface temperature biases, should be considered for SMAP SM retrieval. These results have implications for future updates of SMAP SCA-V and applications of time series SMAP SM data for hydroecological studies, especially in the future, which is projected to have strong and long-lasting droughts.