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The Error Structure of the SMAP Single and Dual Channel Soil Moisture Retrievals
Author(s) -
Dong Jianzhi,
Crow Wade T.,
Bindlish Rajat
Publication year - 2018
Publication title -
geophysical research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.007
H-Index - 273
eISSN - 1944-8007
pISSN - 0094-8276
DOI - 10.1002/2017gl075656
Subject(s) - environmental science , opacity , vegetation (pathology) , autocorrelation , water content , remote sensing , brightness temperature , moisture , brightness , atmospheric sciences , meteorology , mathematics , statistics , geology , medicine , physics , geotechnical engineering , pathology , optics
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.