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Title:   Author  item
                                            Coopersmith, Evanitem
                                            Cosh, Michael
  Submitted to: American Geophysical Union
                                    Publication Type: Abstract Only
                                    Publication Acceptance Date: 10/3/2014
                                    Publication  Date: 12/15/2014
                                    Citation: Coopersmith, E.J., Cosh, M.H. 2014. Generating large-scale estimates from sparse, in-situ networks: multi-scale soil moisture modeling at ARS watersheds for NASA’s soil moisture active passive (SMAP) calibration/validation mission [abstract]. American Geophysical Union Fall Meeting. Abstract H11M-01.Interpretive Summary:
                                Technical Abstract: NASA’s SMAP satellite, launched in November of 2014, produces estimates of average volumetric soil moisture at 3, 9, and 36-kilometer scales.  The calibration and validation process of these estimates requires the generation of an identically-scaled soil moisture product from existing in-situ networks.  This can be achieved via the assimilation of NLDAS precipitation data to perform calibration of models at each ¬in-situ gauge.  In turn, these models and the gauges’ volumetric estimations are used to generate soil moisture estimates at the 500m scale throughout a given test watershed by leveraging, at each location, the gauge-calibrated models deemed most appropriate in terms of proximity, calibration efficacy, soil-textural similarity, and topography.  Four ARS watersheds, located in Iowa, Oklahoma, Georgia, and Arizona are employed to demonstrate the utility of this approach.  The South Forks watershed in Iowa represents the simplest case – the soil textures and topography are relative constants and the variability of soil moisture is simply tied to the spatial variability of precipitation.  The Little Washita watershed in Oklahoma adds soil textural variability (but remains topographically trivial), while the Little River watershed in Georgia incorporates topographic classification in additional to soil texture.  Finally, the Walnut Gulch watershed in Arizona adds a dense precipitation network to be employed for even finer-scale modeling estimates.   Results suggest RMSE values at or below the 4% volumetric standard adopted for the SMAP mission are attainable over the desired spatial scales via this integration of modeling efforts and existing in-situ networks.


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