Ecological and Agricultural Productivity Forecasting Using Root-Zone Soil Moisture Products Derived from the Nasa Smap Mission
Water Management and Conservation Research
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 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.
A graduate student for the project was hired at the University of Arizona in August 2011. This project is directly related to objective 2 of the parent project: "Develop and verify remote sensing methods, tools, and decision supoprt systems". 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 student has completed an initial evaluation of HYDRUS-1D using data from a wheat field study in Arizona. The student has added a simple crop growth model to the HYDRUS-1D model to permit the simulation of both water balances and crop growth and yield. He has also begun programming a modeling framework for conducting ensemble Kalman filtering with the model. This will permit testing of techniques for assimilating remotely sensed soil moisture data into the HYDRUS-1D crop growth model simulations.