Submitted to: Geophysical Research Letters
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/25/2005
Publication Date: 12/31/2005
Citation: Crow, W.T., Koster, R.D., Reichle, R., Sharif, H. 2005. Relevance of time-varying and time-invariant retrieval error sources on the utility of spaceborne soil moisture. Geophysical Research Letters. (32)l24405, doi: 10.1029/2005GL24889. Interpretive Summary: Conservative economic analysis suggests that improvements in our ability to predict soil moisture are worth several hundred millions of million dollars (per year) to the U.S. agricultural sector. This manuscript demonstrates that traditional methods for evaluating remotely-sensed soil moisture information are flawed in such a way that they underestimate the value of such observations for improving our ability to monitor and predict the terrestrial water cycle. This realization is vital for efforts within the land surface community to ensure that NASA maintains its current commitment to a dedicated soil moisture spaceborne mission (i.e. the Hydros mission). The Hydros mission was the subject of a 2003 MOU between NASA and the USDA and ARS scientists have been very active in the design and formulation of the mission.
Technical Abstract: Errors in remotely-sensed soil moisture retrievals originate from a combination of time-invariant and time-varying sources. For land modeling applications such as forecast initialization, some of the impact of time-invariant sources can be removed given known differences between observed and modeled soil moisture climatologies. Nevertheless, the distinction is seldom made when evaluating remotely-sensed soil moisture products. Here we describe an Observing System Simulation Experiment (OSSE) for radiometer-only soil moisture products derived from the NASA Hydrosphere States (Hydros) mission where the impact of time-invariant errors is explicitly removed via the linear rescaling of retrievals. OSSE results for the 575,000 km Red-Arkansas River Basin indicate that climatological rescaling may significantly reduce the perceived magnitude of Hydros soil moisture retrieval errors and expands the geographic areas over which retrievals demonstrate value for land surface modeling applications.