Enhancing the USDA global crop assessment decision support system using SMAP L3 Soil Moisture data

The root zone soil moisture estimates used by USDA-FAS are generated using the modified Palmer model. This baseline model was extended to by adding a data assimilation (DA) unit that allow us to routinely ingest satellite-based soil moisture observations. The current DA system relies on observations obtained from the Soil Moisture Ocean Salinity Mission (SMOS). Preliminary analysis, however, demonstrated that the accuracy of the model predictions can be improved if we perform dual assimilation. This dual assimilation system is intended to ingest Soil Moisture Active Passive (SMAP)-based soil moisture retrievals and soil moisture observations form the Advanced Scatterometer instrument (ASCAT). Currently the system has been tested using SMOS data as a proxy form SMAP. We assess the performance of this dual assimilation system framework using a global lag-correlation analysis with ranked SMOS and ASCAT soil moisture observations, and MODIS-based vegetation observations.