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Remote Sens. 2015, 7(9), 12400-12418; doi:10.3390/rs70912400

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

1,2,3,†
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3,4,†
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1,2,5,6
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1,2,5,6,* , 7
,
1,2,5,6
and
1,2,5,6
1
Beijing Research Center for Information Technology in Agriculture, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
2
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
3
Institute of Agricultural Remote Sensing and Information Application, Zhejiang University, Hangzhou 310029, China
4
Beijing Research Center for Agricultural Standards and Testing, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
5
Key Laboratory for Information Technologies in Agriculture, The Ministry of Agriculture, Beijing 10097, China
6
Beijing Engineering Research Center of Agricultural Internet of Things, Beijing 100097, China
7
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
The author contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editors: Mutlu Ozdogan, Clement Atzberger and Prasad S. Thenkabail
Received: 29 June 2015 / Revised: 31 August 2015 / Accepted: 7 September 2015 / Published: 22 September 2015
(This article belongs to the Special Issue Remote Sensing in Precision Agriculture)
View Full-Text   |   Download PDF [820 KB, uploaded 22 September 2015]   |  

Abstract

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. View Full-Text
Keywords: Hyperspectral; DSSAT-CERES; winter wheat; particle swarm optimization algorithm; yield; grain protein content Hyperspectral; DSSAT-CERES; winter wheat; particle swarm optimization algorithm; yield; grain protein content
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Li, Z.; Wang, J.; Xu, X.; Zhao, C.; Jin, X.; Yang, G.; Feng, H. Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation. Remote Sens. 2015, 7, 12400-12418.

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