Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation

文献类型： 外文期刊

第一作者： Li, Zhenhai

作者： Li, Zhenhai;Xu, Xingang;Zhao, Chunjiang;Yang, Guijun;Feng, Haikuan;Li, Zhenhai;Xu, Xingang;Zhao, Chunjiang;Yang, Guijun;Feng, Haikuan;Li, Zhenhai;Wang, Jihua;Wang, Jihua;Xu, Xingang;Zhao, Chunjiang;Yang, Guijun;Feng, Haikuan;Xu, Xingang;Zhao, Chunjiang;Yang, Guijun;Feng, Haikuan;Jin, Xiuliang

作者机构：

关键词： Hyperspectral;DSSAT-CERES;winter wheat;particle swarm optimization algorithm;yield;grain protein content

期刊名称：REMOTE SENSING （ 影响因子：4.848； 五年影响因子：5.353 ）

ISSN： 2072-4292

年卷期： 2015 年 7 卷 9 期

页码：

收录情况： SCI

摘要： The combination of remote sensing and crop growth models has become an effective tool for yield estimation and a potential method for grain quality estimation. In this study, two assimilation variables (derived from a hyperspectral sensor), called leaf area index (LAI) and canopy nitrogen accumulation (CNA), were jointly used to calibrate the sensitive parameters and initial states of the DSSAT-CERES crop model, to improve simulated output of the grain yield and protein content of winter wheat. The results show that the modified simple ratio (MSR) and normalized difference red edge (NDRE) better estimated LAI and CNA, respectively, compared with the other possible vegetation indices. The integration of both LAI and CNA resulted in a more robust DSSAT-CERES models with than each one alone. The R-2 and RMSE values, respectively, of the regression between the simulated (using the two assimilation variables method) and measured LAI were 0.828 and 0.494, and for CNA were 0.808 and 20.26 kg N.ha(-1). These two assimilation variables resulted in grain yield and protein content estimates of winter wheat with a high precision and R-2 and RMSE values of 0.698 and 0.726 ton.ha(-1), and 0.758% and 1.16%, respectively. This study provides a more robust method for estimating the grain yield and protein content of winter wheat based on the integration of the DSSAT-CERES crop model and remote sensing data.

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