2012 Annual Report 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. 3.Progress Report: The project will be based on a series of synthetic data assimilation experiments designed to clarify whether Soil Moisture Active/Passive (SMAP) surface soil moisture retrievals can be reliably extrapolated to root-zone depths with sufficient accuracy to add significant skill to crop yield predictions. Expected outcomes of the project are: 1) A coherent description of the potential impact of SMAP data products on agricultural productivity monitoring applications. 2) The extension of existing land data assimilation techniques to enhance the integration of SMAP measurements into agricultural crop system models. 3) The development of a general methodology applicable to examining the added utility of SMAP observations for productivity forecasting activities within other ecosystem types. Year 1 activities have targeted project objectives #1 (“a coherent description of the potential impact of SMAP data products on agricultural productivity monitoring applications”) and #2 (“the extension of existing land data assimilation techniques to enhance the integration of SMAP measurements into agricultural crop system models"). Based on year 1 results, year 2 of the project will focus on two specific topics: a. Providing a coherent description as to why Ensemble Kalman Filter (EnKF) and Sequential Importance Resampling Filter (SIRF)-based data assimilation cases fail to improve end of season yield predictions made by crop system models. b. Exploring the potential of alternative data assimilation approaches which adapt either model physical processes or update parameters (as opposed to model states) in response to remotely-sensed surface soil moisture retrievals. These alternative approaches may provide larger benefits to yield prediction than existing data assimilation approaches. Note that both of these topics must be addressed to fully meet project objective #2 (“the extension of existing land data assimilation techniques to enhance the integration of SMAP measurements into agricultural crop system models”).