Ecological and Agricultural Productivity Forecasting Using Root-Zone Soil Moisture Products Derived from the NASA SMAP Mission
Southwest Watershed Research
2011 Annual Report
1a.Objectives (from AD-416)
ARS is interested in developing viable agricultural applications for remotely-sensed surface soil moisture observations. Objective 2 of our Project Plan (5342-13610-010-00D) specifically addresses this interest: “Develop improved watershed model components and decision support systems that can assimilate and utilize remotely sensed data for parameterization, calibration, and model state adjustments” The COOPERATOR (NASA) requires expertise to design the science requirements for an upcoming satellite mission (the Soil Moisture Active/Passive Mission - SMAP) so that they maximize the utility of SMAP observations for agricultural forecasting and monitoring applications (e.g. the detection of agricultural drought).
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. Other cooperators include investigators from NASA, the University of Arizona, USDA ARS ALARC Maricopa, and USDA ARS HRSL Beltsville.
The NASA Terrestrial Ecology program recently funded work to improve “Ecological and agricultural productivity forecasting using root-zone soil moisture products derived from the NASA SMAP mission.” In 2011, we developed a comprehensive strategy to improve wheat yield estimates by assimilating remote sensing observations of soil moisture and leaf area into a wheat growth model. Results showed that very little uncertainty in wheat yield estimates was mitigated when crops were not stressed. Our recommendation is that satellite measurements will be a beneficial tool for estimating yield in areas of the world where water is limited and crops are not irrigated. The project was managed through close interaction with the SMAP Science Team via regular conference calls, e-mail exchanges, and organized group workshops.