Title
Assimilation of Two Variables Derived from Hyperspectral Data into the DSSAT-CERES Model for Grain Yield and Quality Estimation
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 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.
Year
DOI
Venue
2015
10.3390/rs70912400
REMOTE SENSING
Keywords
Field
DocType
Hyperspectral,DSSAT-CERES,winter wheat,particle swarm optimization algorithm,yield,grain protein content
Leaf area index,Vegetation,Remote sensing,Mean squared error,Hyperspectral imaging,DSSAT,Geology,Grain quality,Canopy,Red edge
Journal
Volume
Issue
Citations 
7
9
3
PageRank 
References 
Authors
0.49
5
7
Name
Order
Citations
PageRank
zhenhai14514.06
Jihua Wang219153.78
Xingang Xu3319.89
J.-C. Zhao413552.42
xiuliang jin5317.50
Gui-Jun Yang614833.61
Haikuan Feng76910.14