RS-CGM: a spatial crop growth model based on GIS and RS

Yuping Lei; Tianjun Tang; Li Zheng; Shengwei Zhang; Zhen Wang

doi:10.1117/12.800808

2 October 2008 RS-CGM: a spatial crop growth model based on GIS and RS

Yuping Lei, Tianjun Tang, Li Zheng, Shengwei Zhang, Zhen Wang

Proceedings Volume 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X; 71040X (2008) https://doi.org/10.1117/12.800808
Event: SPIE Remote Sensing, 2008, Cardiff, Wales, United Kingdom

Abstract

Spatial variability of crop growth often needs to be evaluated due to different soil conditions, weather patterns and crop information in a region. To simulate crop growth and productivity at a regional scale, a RS- and GIS-based crop growth model named RS-CGM was developed. The model calculates crop distribution, leaf area index, soil water content using remote sensing data that were integrated in crop growth module by inputting direct forcing variables, re-calibrating specific parameters, and correcting yield prediction using simulation-observation difference of a variable. The main RS-CGM components were intensively calibrated and verified against comprehensive field measurements of soil conditions, irrigation, evapotranspiration (ET), crop leaf area index (LAI) and yields. .The RS-CGM was applied to a county in the North China Plain to simulate winter-wheat yields in spatial and temporal dimensions. The model divides the simulating area into a number of crop growth elements and calculates each element with a set of parameters, then achieves the spatial crop yields and other concerned results aggregating to administrative regions. The simulated results show that the model can effectively express the spatial variety of yields in a region. And suggest that it was feasible to develop a spatial crop growth model combined with GIS, RS, and physiological process-oriented.

Citation Download Citation

Yuping Lei, Tianjun Tang, Li Zheng, Shengwei Zhang, and Zhen Wang "RS-CGM: a spatial crop growth model based on GIS and RS", Proc. SPIE 7104, Remote Sensing for Agriculture, Ecosystems, and Hydrology X, 71040X (2 October 2008); https://doi.org/10.1117/12.800808

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