The Geospatial Research Laboratory and partner organizations are collecting stationary and roving COSMOS observations. This research investigates the verification of land surface model soil moisture products using contiguous and non-contiguous COSMOS observations. Machine learning is then used to determine if Noah land surface model output can be improved through post processing when COSMOS observations are available and the length of record necessary is available. Significant impacts of this long-term project include a) an understanding of the trade-off between spot size and signal intensity for the COSMOS sensors and its impact in the verification process, b) verification of land surface model conditions using COSMOS sensors, and c) develop an understanding of the confidence intervals associated with the land surface model and downscaled data for future use in analytic tools or further modeling.
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