The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals

• Published in: Geophysical research letters Vol. 45; no. 2; pp. 758 - 765
• Main Authors: Dong, Jianzhi, Crow, Wade T., Bindlish, Rajat
• Format: Journal Article
• Language: English
• Published: United States John Wiley & Sons, Inc 28.01.2018
• Subjects: Algorithms; Annual variations; Autocorrelation; Brightness temperature; Climate; Climate studies; Climatology; Data processing; dual‐channel algorithm; error autocorrelation; Error detection; Errors; Hydrology; Interannual variability; Mathematical models; Meteorological satellites; Opacity; Remote sensing; Retrieval; single‐channel algorithm; SMAP; Soil; Soil improvement; Soil moisture; Soil surfaces; Soil temperature; Surface radiation temperature; triple collocation; Variance analysis; Vegetation
• ISSN: 0094-8276; 1944-8007; 1944-8007
• DOI: 10.1002/2017GL075656

Knowledge of the temporal error structure for remotely sensed surface soil moisture retrievals can improve our ability to exploit them for hydrologic and climate studies. This study employs a triple collocation analysis to investigate both the total variance and temporal autocorrelation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the vertically polarized brightness temperature‐based single‐channel algorithm (SCA‐V, the current baseline SMAP algorithm) and the dual‐channel algorithm (DCA). A key assumption made in SCA‐V is that real‐time vegetation opacity can be accurately captured using only a climatology for vegetation opacity. Results demonstrate that while SCA‐V generally outperforms DCA, SCA‐V can produce larger total errors when this assumption is significantly violated by interannual variability in vegetation health and biomass. Furthermore, larger autocorrelated errors in SCA‐V retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the autocorrelated error in SCA‐V is attributable to the violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA‐V algorithm would reduce the magnitude of autocorrelated soil moisture retrieval errors. Key Points Retrieval errors for the SMAP single and dual‐polarization algorithms are compared The single‐channel algorithm provides poorer estimates for regions with high interannual variability in vegetation opacity The single‐channel algorithm has stronger autocorrelated errors, which is attributed to its use of a climatology for vegetation opacity