Submitted to: IEEE Transactions on Geoscience and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 1/31/2005
Publication Date: 6/1/2005
Citation: Crow, W.T., Chan, T., Entekhabi, E., Houser, P.R., Hsu, A.Y., Jackson, T.J., Njoku, E., O'Neill, P.E., Shi, J.C., Zhan, X. 2005. An observing system simulation experiment for Hydros radiometer soil moisture products. IEEE Transactions on Geoscience and Remote Sensing. 43(6):1289-1303. Interpretive Summary: Hydros is an exploratory NASA satellite mission that seeks to deploy the first spaceborne sensor optimally designed to globally measure surface soil moisture. Data products from Hydros have the potential to aid a range of climatic and hydrologic applications (e.g. seasonal weather forecasting, drought monitoring, and flood forecasting) relevant to agricultural management issues. This paper describes results for a simulation experiment designed to quantify expected levels of errors in Hydros soil moisture products. Results will be used to help refine retrieval algorithms and improve the accuracy of Hydros soil moisture products. The Hydros mission is currently in formulation stage within NASA's Earth System Pathfinder Program and has an expected launch data sometime in 2010/2011. USDA ARS scientists have been involved in the project since its inception and are actively working to develop agricultural applications for Hydros data products.
Technical Abstract: Based on 1-km land surface model predictions within the United States Southern Great Plains (Red-Arkansas River basin), an observing system simulation experiment (OSSE) is carried out to assess the impact of land surface heterogeneity, instrument error, and retrieval strategy on soil moisture products derived from the NASA Hydrosphere State (Hydros) mission. Simulated retrieved soil moisture products are created using three distinct retrieval algorithms based on the characteristics of passive microwave measurements expected from Hydros. The accuracy of retrieval products is evaluated through comparisons with benchmark soil moisture fields obtained from direct aggregation of the original simulated soil moisture fields. Results quantify the relative impact of dense vegetation, land surface heterogeneity, and inland water on radiometer-based soil moisture retrieval. All three retrieval algorithms exhibit acceptably low retrieval errors (< 3 % volumetric) within the OSSE domain. Algorithm robustness is also evaluated for the case of artificially enhanced vegetation water content (VWC) values within the basin. For large VWC (> 3 kg m-2), a distinct positive bias, attributable to the impact of sub-footprint scale landcover heterogeneity, is identified in soil moisture retrievals. Prospects for the removal of this bias via a correction strategy and/or the implementation of an alternative aggregation strategy for surface vegetation and roughness parameters are discussed.