Testing regression equations to derive long-term global soil moisture datasets from passive microwave observations

• Published in: Remote sensing of environment 2016-07, Vol.180, p.453-464
• Main Authors: Al-Yaari, A., Wigneron, J.P., Kerr, Y., de Jeu, R., Rodriguez-Fernandez, N., van der Schalie, R., Al Bitar, A., Mialon, A., Richaume, P., Dolman, A., Ducharne, A.
• Format: Article
• Language: English
• Subjects: AMSR-E; Calibration; Computer simulation; Continental interfaces, environment; Correlation; Earth Sciences; Hydrology; Microwaves; Regression; Remote sensing; Retrieval; Sciences of the Universe; SMOS; Soil moisture; Statistical regression
• ISSN: 0034-4257; 1879-0704
• DOI: 10.1016/j.rse.2015.11.022

Within the framework of the efforts of the European Space Agency (ESA) to develop the most consistent and complete record of surface soil moisture (SSM), this study investigated a statistical approach to retrieve a global and long-term SSM dataset from space-borne observations. More specifically, this study investigated the ability of physically based statistical regressions to retrieve SSM from two passive microwave remote sensing observations: the Advanced Microwave Scanning Radiometer (AMSR-E; 2003–Sept. 2011) and the Soil Moisture and Ocean Salinity (SMOS) satellite. Regression coefficients were calibrated using AMSR-E horizontal and vertical brightness temperature (TB) observations and SMOS level 3 SSM (SMOSL3; as a training dataset). This calibration process was carried out over the June 2010–Sept. 2011 period, over which both SMOS and AMSR-E observations coincide. Based on these calibrated coefficients, a global SSM product (referred here to as AMSR-reg) was computed from the AMSR-E TB observations during the 2003–2011 period. The regression quality was assessed by evaluating the AMSR-reg SSM product against the SMOSL3 SSM product over the period of calibration, in terms of correlation (R) and Root Mean Square Error (RMSE). A good agreement (mean global R=0.60 and mean global RMSE=0.057m3/m3), was obtained between the AMSR-reg and SMOSL3 SSM products particularly over Australia, central USA, central Asia, and the Sahel. In a second step, the AMSR-reg SSM retrievals and commonly used AMSR-E SSM retrievals derived from the Land Parameter Retrieval Model (AMSR-LPRM), were evaluated against two kinds of SSM references (i) the global MERRA-Land SSM simulations and (ii) in situ measurements over 2003–2009. The results demonstrated that both AMSR-reg and AMSR-LPRM (better when considering global simulations) successfully captured the temporal dynamics of the references used having comparable correlation values. AMSR-reg was more consistent with MERRA-land than AMSR-LPRM in terms of unbiased RMSE (ubRMSE) with a global average of ubRMSE of 0.055m3/m3 for AMSR-reg and 0.084m3/m3 for AMSR-LPRM. In conclusion, the statistical regression, which is tested here for the first time using long-term spaceborne TB datasets, appears to be a promising approach for retrieving SSM from passive microwave remote sensing TB observations. •Global maps of soil moisture were computed from AMSR-E using regression equations.•Regression coefficients were calibrated using SMOS soil