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	Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation
	Fang H. L. ; Liang S. L. ; Hoogenboom G.
	2011
关键词	leaf-area index remotely-sensed data sensing data assimilation radiative-transfer models canopy reflectance model crop growth simulation-models genetic algorithm inversion information
英文摘要	Advanced information on crop yield is important for crop management and food policy making. A data assimilation approach was developed to integrate remotely sensed data with a crop growth model for crop yield estimation. The objective was to model the crop yield when the input data for the crop growth model are inadequate, and to make the yield forecast in the middle of the growing season. The Cropping System Model (CSM)-Crop Environment Resource Synthesis (CERES)-Maize and the Markov Chain canopy Reflectance Model (MCRM) were coupled in the data assimilation process. The Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and vegetation index products were assimilated into the coupled model to estimate corn yield in Indiana, USA. Five different assimilation schemes were tested to study the effect of using different control variables: independent usage of LAI, normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), and synergic usage of LAI and EVI or NDVI. Parameters of the CSM-CERES-Maize model were initiated with the remotely sensed data to estimate corn yield for each county of Indiana. Our results showed that the estimated corn yield agreed very well with the US Department of Agriculture (USDA) National Agricultural Statistics Service (NASS) data. Among different scenarios, the best results were obtained when both MODIS vegetation index and LAI products were assimilated and the relative deviations from the NASS data were less than 3.5%. Including only LAI in the model performed moderately well with a relative difference of 8.6%. The results from using only EVI or NDVI were unacceptable, as the deviations were as high as 21% and -13% for the EVI and NDVI schemes, respectively. Our study showed that corn yield at harvest could be successfully predicted using only a partial year of remotely sensed data.
出处	International Journal of Remote Sensing
卷	32
期	4
页	1039-1065
收录类别	SCI
ISSN号	0143-1161
内容类型	SCI/SSCI论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/24059]
专题	地理科学与资源研究所_历年回溯文献
推荐引用方式 GB/T 7714	Fang H. L.,Liang S. L.,Hoogenboom G.. Integration of MODIS LAI and vegetation index products with the CSM-CERES-Maize model for corn yield estimation. 2011.

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