Combining Remote Sensing and Simulation Modeling to Study Hydrologic and Crop Growth Processes of Agricultural Systems
Water Management and Conservation Research
2012 Annual Report
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.
A graduate student for the project was hired at the University of Arizona in August 2011, and a specific cooperative agreement was established to fund the student’s work. This project is directly related to Objective 3: "Develop and evaluate decision support systems that integrate remote sensing and crop growth modeling for assessing crop water alternatives at field-level and watershed scales". 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.