Location: Hydrology and Remote Sensing LaboratoryTitle: The error structure of the SMAP single and dual channel soil moisture retrievals
|DONG, J. - US Department Of Agriculture (USDA)
Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 12/7/2017
Publication Date: 12/20/2017
Citation: Dong, J., Crow, W.T. 2017. The error structure of the SMAP single and dual channel soil moisture retrievals. Geophysical Research Letters. 45:758-765. https://doi.org/10.1002/2017JD027397.
Interpretive Summary: Satellite-derived surface soil moisture products are increasingly being applied to improve our ability to track the extent, severity and duration of agricultural drought. However, before these products can be fully exploited, we need to understand the statistical nature of their errors and the degree to which these errors will hamper our ability to accurately detect soil moisture anomalies present during drought. This paper applies a newly-developed mathematical approach to examine the amount of statistical error auto-correlation present in satellite-derived soil moisture estimates (i.e., the tendency for errors in current soil moisture estimates to be related to errors in future estimates). The results of the analysis are then used to clarify conditions under which this type of error is significant and assess strategies for reducing its impact. Eventually, the results of this paper will be used by remotely-sensed data set developers to improve the value of their products for agricultural drought monitoring.
Technical Abstract: Knowledge of the temporal error structure for remotely-sensed surface soil moisture retrievals can improve our ability to exploit them for hydrology and climate studies. This study employs a triple collocation type analysis to investigate both the total variance and temporal auto-correlation of errors in Soil Moisture Active and Passive (SMAP) products generated from two separate soil moisture retrieval algorithms, the Single Channel Algorithm (SCA, the current baseline SMAP algorithm) and the Dual Channel Algorithm (DCA). A key assumption made in the SCA is that real-time vegetation opacity can be accurately captured using only a vegetation opacity climatology. Results demonstrate that while the SCA generally outperforms the DCA with regards to overall total error, SCA can contain larger total errors when its vegetation opacity assumption is significantly violated due to strong inter-annual variability in vegetation health and biomass. Furthermore, larger auto-correlated errors in SCA retrievals are found in areas with relatively large vegetation opacity deviations from climatological expectations. This implies that a significant portion of the auto-correlated error in SCA is attributable to violation of its vegetation opacity climatology assumption and suggests that utilizing a real (as opposed to climatological) vegetation opacity time series in the SCA algorithm would reduce the magnitude of auto-correlated soil moisture retrieval errors.