Incorporating Remote Sensing Data in Crop Model to Monitor Crop Growth and Predict Yield in Regional Area

摘要

Accurate crop growth monitoring and yield predicting is very important to food security and agricultural sustainable development. Crop models can be forceful tools for monitoring crop growth status and predicting yield over homogeneous areas, however, their application to a larger spatial domains is hampered by lack of sufficient spatial information about model inputs, such as the value of some of their parameters and initial conditions, which may have great difference between regions even fields. The use of remote sensing data helps to overcome this problem. By incorporating remote sensing data into the WOFOST crop model (through LAI), it is possible to incorporate remote sensing variables (vegetation index) for each point of the spatial domain, and it is possible for this point to re-estimate new values of the parameters or initial conditions, to which the model is particularly sensitive. This paper describes the use of such a method on a local scale, for winter wheat, focusing on the parameters describing emergence and early crop growth. These processes vary greatly depending on the soil, climate and seedbed preparation, and affect yield significantly. The WOFOST crop model is calibrated under standard conditions and then evaluated under test conditions to which the emergence and early growth parameters of the WOFOST model are adjusted by incorporating remote sensing data. The inversion of the combined model allows us to accurately monitoring crop growth status and predicting yield on a regional scale.