8 November 2014 Inter-comparison of soil moisture products from SMOS, AMSR-E, ECWMF and GLDAS over the Mongolia Plateau
Author Affiliations +
In this study, we inter-compare soil moisture from in situ measurement, reanalysis data (ERA-interim), land data assimilation system simulations (the Global Land Data Assimilation System, GLDAS) and two satellite remote sensing retrievals: L-band products from Soil Moisture Ocean Salinity (SMOS) and C-band products from the Japan Aerospace Exploration Agency Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E). The stationaveraged surface soil moisture data, measured during May to September 2010, from the CEOP Mongolia network are used as “ground truth”. Major findings are: (1) from the point view of root mean square error (RMSE), the accuracy of the remote sensing products is clearly higher than the ERA-interim and GLDAS. AMSR-E has the smallest RMSE (0.032), while the highly-expected SMOS has an RMSE of 0.065, larger than the mission requirement (RMSE<0.04). Both GLDAS (RMSE=0.132) and ERA-interim (RMSE=0.115) evidently overestimate soil moisture. (2) According to the correlation coefficient (R), ERA-interim has the highest one (0.77), and next came AMSR-E (0.47), GLDAS (0.06) and SMOS (0.04), indicating that both GLDAS and SMOS fails to capture the soil moisture temporal dynamics. Our results reveal that the remote sensing product still need further develop, for both C-Band algorithm (AMSR-E) and Lband one (SMOS). The coincident of high R of ERA-interim and low RMSE of AMSR-E implies a potential for integration within a land data assimilation system.
© (2014) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xin Wen, Xin Wen, Hui Lu, Hui Lu, Chengwei Li, Chengwei Li, Toshio Koike, Toshio Koike, Ichirou Kaihotsu, Ichirou Kaihotsu, "Inter-comparison of soil moisture products from SMOS, AMSR-E, ECWMF and GLDAS over the Mongolia Plateau", Proc. SPIE 9260, Land Surface Remote Sensing II, 92600O (8 November 2014); doi: 10.1117/12.2068952; https://doi.org/10.1117/12.2068952

Back to Top