1a.Objectives (from AD-416)
Our central goal here will be to prepare for the application of measurements obtained from the NASA SMAP mission to a specific ecological forecasting activity – yield and productivity prediction for agricultural and rangeland ecosystems. This preparation will be based on conducting a series of synthetic data assimilation experiments designed to clarify whether SMAP surface soil moisture retrievals can be reliably extrapolated to root-zone depths with sufficient accuracy to add significant skill to end-of-season yield and productivity forecasts.
1b.Approach (from AD-416)
Research will be based on the design and execution of a series of complete end-to-end observing system simulation experiments (OSSE’s) to isolate the added utility of SMAP soil moisture products for agricultural and rangeland forecasting activities. All OSSE experiments will contain three separate components :. 1)an algorithm test-bed facility to generate synthetic SMAP soil moisture data products,. 2)a data assimilation system to integrate these synthetic products into a multi-layered land surface model, and. 3)a crop forecasting system to obtain end-of-season crop yield and rangeland productivity forecasts based the mid-growing season initialization of a crop model with profile soil moisture measurements obtained from a crop growth model.
Completed development of a data assimilation system to simultaneously ingest remotely-sensed surface soil moisture retrievals and leaf area index estimates into a crop system model (DSSAT). Evaluated the data assimilation system using a synthetic twin analysis applied over a winter wheat site and compared the relative performance of an Ensemble Kalman filter and a Particle filter in enhancing winter yield predictions at the site. Currently preparing a manuscript describing this analysis for submission to a peer-review journal.