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Title:   Author  item
                                            Yilmaz, Mustafaitem
                                            Crow, Wadeitem
                                            Anderson, Marthaitem
                                            HAIN, C - National Oceanic & Atmospheric Administration (NOAA)
  Submitted to: Water Resources Research
                                    Publication Type: Peer Reviewed Journal
                                    Publication Acceptance Date: 9/18/2012
                                    Publication  Date: 11/2/2012
                                    Citation: Yilmaz, M.T., Crow, W.T., Anderson, M.C., Hain, C. 2012. An objective methodology for merging satellite and model-based soil moisture products. Water Resources Research. DOI:10.1029/2011WR011682.Interpretive Summary: Researchers at the USDA Hydrology and Remote Sensing (Beltsville, MD) are actively developing several independent methods for estimating root-zone soil water availability within agricultural landscapes. These estimates are of value for a number of key agricultural applications including: drought forecasting, irrigation scheduling, and optimizing fertilizer usage. The best single estimate of soil water availability would be based on optimally merging information acquired via these independent methods into a single prediction. This manuscript presents a novel mathematical strategy for 1) obtaining the error information for independent soil moisture products acquired from microwave remote sensing, thermal remote sensing and water balance modeling, and 2) using this error information to merge these independent estimates into a single, optimized estimate of root-zone soil water availability. Applying this procedure will enhance the quality of soil water availability estimates commonly used in agricultural decision support systems.
                                Technical Abstract: An objective methodology, that does not require any user-defined parameter assumptions, is introduced to obtain an improved soil moisture product along with associated uncertainty estimates. This new product is obtained by merging model-, thermal infrared remote sensing-, and microwave remote sensing-based soil moisture estimates in a least squares framework, where the uncertainty estimates of each product are obtained using the triple collocation methodology. We have validated the merged product against in-situ based soil moisture data. The merged product was better correlated with the in-situ data than the individual input products; however it was not superior to a naively merged product. The resulting combined soil moisture estimate is an improvement over the currently available soil moisture products due to its reduced uncertainty. The merged soil moisture estimates can be used as a stand alone soil moisture product with available uncertainty estimate.


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