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

Remote Sensing

Zhenhai Li²  Jihua Wang¹  Xingang Xu²  Chunjiang Zhao²  Xiuliang Jin⁴  Guijun Yang²  Haikuan Feng²  Mutlu Ozdogan³  Clement Atzberger³ 

[1] Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China; E-Mail:;Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China; E-Mails:Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China;;Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China; E-Mail:

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

DOI  :  10.3390/rs70912400

来源: mdpi

【 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 R² 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 R² 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.