Gao, G.; Zhang, L.; Wu, L.; Yuan, D. Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index. Appl. Sci.2024, 14, 703.
Gao, G.; Zhang, L.; Wu, L.; Yuan, D. Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index. Appl. Sci. 2024, 14, 703.
Gao, G.; Zhang, L.; Wu, L.; Yuan, D. Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index. Appl. Sci.2024, 14, 703.
Gao, G.; Zhang, L.; Wu, L.; Yuan, D. Estimation of Chlorophyll Content in Wheat Based on Optimal Spectral Index. Appl. Sci. 2024, 14, 703.
Abstract
Accurate estimation of chlorophyll content in wheat leaves can not only reflect its growth and nutritional status, but also provide a scientific basis for field management. In order to evaluate the potential of hyperspectral data to estimate the chlorophyll content of 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 con-structed by using the combination of 400-1000nm bands, and the correlation between the con-structed spectral index and the measured chlorophyll value was analyzed, and the optimal spectral index was screened by 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 of 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 mainly composed of red band, red edge band and near-infrared band, and the five optimal spectral indices at the grain filling stage had the highest correlation with the chlorophyll value, and the absolute value of the cor-relation coefficient was greater than 0.73, the accuracy of the estimation model established in the four growth stages was different, and the estimation accuracy of the flag stage was the best, with R² and RMSE of 0.79 and 2.63, respectively. The above results show that hyperspectral data is suitable for estimating the chlorophyll value of wheat leaves, and the PR model of flag picking period can be used as the optimal model for estimating the chlorophyll value of wheat leaves.
Keywords
winter wheat; chlorophyll content; hyperspectral
Subject
Environmental and Earth Sciences, Remote Sensing
Copyright:
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.
