Assimilating remotely sensed LAI into GIS-based EPIC model for yield assessment on regional scale

Abstract

Many crop growth models have been used successfully for simulating the physiological development, growth, and yield of a crop on field scale. To upscale crop models to large areas and to improve accuracy of yield assessment on regional scale, this article describes the development of a methodology for assimilating remotely sensed LAI into crop growth model. In this study, the Environmental Policy Integrated Climate (EPIC) model from United States Department of Agriculture (USDA) is integrated with Geographical Information System (GIS) by a Loose Coupling Approach, firstly. Then, the empirical Leaf Area Index (LAI) maps, which are retrieved from multi-temporal Landsat TM images by Regression Analysis Procedure, are assimilated with the GIS-based EPIC model by an optimization algorithm and Lookup-Table method. Some key parameters for LAI simulation in EPIC model, such as DMLA (the maximum potential LAI) and DLA (the fraction of growing season when leaf area started declining), are recalibrated through data assimilation. Finally, the methodology is applied during the 2004 crop season in 28 counties of North China Plain to simulate the yield of winter wheat. The result of yield assessment indicates that the data assimilation has significantly improved the accuracy of yield simulation by the GIS-based crop growth model on regional scale. The correlation coefficients (R2) between statistic yield and simulated yield at county level have been changed from 0.12 to 0.51.