SMOPS blended soil moisture (SM) provides a combination of currently all available daily global microwave SM retrievals including observations of AMSR-2 onboard the Global Change Observation Mission-Water (GCOM-W) satellite, the Global Precipitation Measurement (GPM) satellite contains the GPM Microwave Imager (GMI), Soil Moisture and Ocean Salinity (SMOS) satellite of the European Space Agency (ESA), and National Aeronautics and Space Administration (NASA)’s Soil Moisture Active Passive (SMAP). Using the Triple Collocation Error Model (TCEM) to quantify the root mean square errors (RMSEs) of these individual SM data, we developed the RMSE-weighted average SMOPS blended SM product. Relative to the equal weights-based product in the older versions, the uncertainties-weighted SMOPS blended shows significantly improvements with reducing the uncertainties with respect to in situ measurements and raising the correlation coefficients with respect to precipitation observations and vegetation dynamics.
Assimilation of the newer version SMOPS blended SM data may reduce unbiased-root-mean-square error for Noah model-based 0-10 cm SM estimations over the simulations with benefits of assimilating each individual SM product. Relative to assimilation of individual SM retrievals, Noah model-based 0-10 cm SM simulations with benefits of assimilating SMOPS blended SM data have better correlation with the data source of Global Land Data Assimilation System (GLDAS) precipitation and the Enhanced Vegetation Index (EVI) from the Moderate Resolution Imaging Spectroradiometers (MODIS).
Additionally, the need for consistent, high space- and time-resolution, integrated water analyses, predictions, and data to address critical unmet information and service gaps related to floods, droughts, water quality, water availability, and climate change has become more and more urgent in the past decade. To meet these growing stakeholder demands and needs, NOAA has undertaken a major effort to improve its hydrological forecast services through the development of a new National Water Model (NWM) at the National Water Center (NWC). The current operational NWM is using precipitation data to estimate soil moisture for the initialization of forecasts. With respect to the SCAN SM observations, Stage IV precipitation and MODIS EVI products, comprehensive evaluations show the newer version SMOPS blended SM data are comparable to NWM-based 0-10cm SM estimations. Results present that the developed SMOPS blended SM data are able to potentially improve drought monitoring capability, and the accuracy of weather forecast and flooding prediction.
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