Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index
Source abstract
Abstract
Chlorophyll content in wheat leaves reflects its growth and nutritional status, which can be used as a health index for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content in wheat leaves, this study focused on the leaves of wheat at the flag-picking stage, flowering stage, grain-filling stage, and maturity stage. Based on the framework of five vegetation indexes, the spectral index was constructed by using the combination of 400–1000 nm bands. The correlation between the constructed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened using the correlation coefficient. Based on the optimal spectral index, polynomial regression, random forest, decision tree, and artificial neural network were used to establish the estimation model for chlorophyll value, and the optimal model for estimating the chlorophyll value of wheat leaves was selected through model evaluation. The results showed that the five optimal spectral indices at the four growth stages were primarily composed of the red band, red edge band, and near-infrared band. The five optimal spectral indices during the grain-filling stage had the highest correlation with the chlorophyll value, and the absolute value of the correlation coefficient was greater than 0.73. The accuracy of the estimation model established in the four growth stages was different, with the estimation accuracy of the flag stage being the best, showing an R2 and RMSE of 0.79 and 2.63, respectively. These results indicate that hyperspectral data are suitable for estimating the chlorophyll value of wheat leaves, and the polynomial regression model of the flag-picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Remote Sensing in Agriculture · Spectroscopy and Chemometric Analyses · Water Quality Monitoring and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Survey Branch of National Nuclear Power Planning and Design Institute Co., Ltd., Market Operation Room, Beijing 100095, China
School of Geosciences and Surveying and Mapping Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Funding
National Natural Science Foundation of China
References (41)
Show source-provided references
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