1a. Objectives (from AD-416):
The objectives of the study are 1) to develop techniques to combine remote sensing information into the simulations of cropping system models and 2) to evaluate the ability of remote sensing information to improve model simulations of key agricultural system processes.
1b. Approach (from AD-416):
Research will focus on the development and evaluation of diverse computational methods, including both data assimilation and parameter estimation approaches, which can be used to merge remote sensing information into the simulations of cropping system models. Synthetic simulation experiments will be conducted to determine the added skill that remote sensing will provide for the adjustment of key model state variables, such as leaf area index and soil moisture, to improve key model outputs, including evapotranspiration and crop yield. Field experiments conducted at Maricopa, Arizona and other locations will provide measurements for evaluation of the results.
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.