Merging two passive microwave remote sensing (SMOS and AMSR_E) datasets to produce a long term record of Soil Moisture
作者： A. Al-Yaari , J.-P Wigneron , A. Ducharne , Y. Kerr , P. de Rosnay
DOI: 10.1109/IGARSS.2014.6946922
关键词: Microwave remote sensing 、 Mean squared error 、 Remote sensing 、 Term (time) 、 Meteorology 、 Water content 、 Regression analysis 、 Brightness 、 Radiometer 、 Environmental science 、 Calibration (statistics)
摘要: This study investigated the use of physically based statistical regressions to retrieve a global and long term (e.g. 2003-2014) surface soil moisture (SSM) record on combination passive microwave remote sensing observations from Advanced Microwave Scanning Radiometer (AMSR-E; 2003-Sept. 2011) Soil Moisture Ocean Salinity (SMOS; 2010-2014) sensors. Statistical regression methods bi-polarization (horizontal vertical) brightness temperatures (Tb) obtained AMSR-E. The coefficients these equations were calibrated using SMOS level 3 SSM maps (SMOSL3) as reference. calibration process was carried out over June 2010-Sept. 2011 period, which both AMSR-E coincide. Based could be computed Tb whole 2003-2011 period. In this study, successfully evaluated against SMOSL3 products period (Jun. 2011). Correlations (R) Root Mean Square Error (RMSE) between retrievals reference products. R (mostly > 0.75) RMSE <; 0.04 m 3 /m 3 ) showed good agreement retrieved particularly Australia, central USA, Asia, Sahel. conclusion, method is capable retrieving coherent "SMOS-AMSR-E" time series for 2003-2014.
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