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 on the assimilation of soil moisture profile information into the water balance component of a crop systems model. Documents Reimbursable with NASA IA Space Grant Consortium.Log 39758.
3. Progress Report:
This project is directly related to objectives 2 and 3 of the inhouse parent project, "Develop and verify remote sensing methods, tools, and decision support systems for managing spatially and temporally variable crop water stress", and "Develop and evaluate decision support tools that integrate remote sensing, geographic information systems, and cropping systems simulation modeling". Activities for the project are solely computer-focused. This permits weekly meetings over the Internet via AT&T Connect to facilitate discussion of results. The graduate student on the project has developed a simulation methodology to inform crop yield predictions from remotely sensed soil moisture time series. The methodology uses HYDRUS-1D with a coupled crop model to simulate yield responses and soil moisture time series for each 2% change in soil texture on the USDA Soil Texture Triangle. The simulation results are being used to identify the type of soil moisture observations that are most related to crop yield in terms of observation depth, timing, and spatial scale. The results will be used to assess the ability of the National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite to forecast crop yield and predict the impact of drought.