Estimation of Winter Wheat Canopy Chlorophyll Content Based on Canopy Spectral Transformation and Machine Learning Method

• Published in: Agronomy (Basel) Vol. 13; no. 3; p. 783
• Main Authors: Chen, Xiaokai, Li, Fenling, Shi, Botai, Fan, Kai, Li, Zhenfa, Chang, Qingrui
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Agriculture; agronomy; Calibration; Canopies; canopy; canopy chlorophyll content; canopy spectral transformation; China; Chlorophyll; Correlation coefficient; Correlation coefficients; Crop growth; Crops; data collection; Decision trees; Estimation; Fertilizers; Genetic transformation; hyperspectral remote sensing; Independent variables; leaf reflectance; Learning algorithms; Leaves; Machine learning; Methods; narrow-band spectral index; Nitrogen; Pests; Photosynthesis; Plant pigments; Potassium; Precision agriculture; prediction; Prediction models; Reflectance; Regression; Regression analysis; Remote sensing; Seasons; Triticum aestivum; Vegetation; Wheat; Winter; Winter wheat; China; Shaanxi China
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy13030783

Canopy chlorophyll content (CCC) is closely related to crop nitrogen status, crop growth and productivity, detection of diseases and pests, and final yield. Thus, accurate monitoring of chlorophyll content in crops is of great significance for decision support in precision agriculture. In this study, winter wheat in the Guanzhong Plain area of the Shaanxi Province, China, was selected as the research subject to explore the feasibility of canopy spectral transformation (CST) combined with a machine learning method to estimate CCC. A hyperspectral canopy ground dataset in situ was measured to construct CCC prediction models for winter wheat over three growth seasons from 2014 to 2017. Sensitive-band reflectance (SR) and narrow-band spectral index (NSI) were established based on the original spectrum (OS) and CSTs, including the first derivative spectrum (FDS) and continuum removal spectrum (CRS). Winter wheat CCC estimation models were constructed using univariate regression, partial least squares (PLS) regression, and random forest (RF) regression based on SR and NSI. The results demonstrated the reliability of CST combined with the machine learning method to estimate winter wheat CCC. First, compared with OS-SR (683 nm), FDS-SR (630 nm) and CRS-SR (699 nm) had a larger correlation coefficient between canopy reflectance and CCC; secondly, among the parametric regression methods, the univariate regression method with CRS-NDSI as the independent variable achieved satisfactory results in estimating the CCC of winter wheat; thirdly, as a machine learning regression method, RF regression combined with multiple independent variables had the best winter wheat CCC estimation accuracy (the determination coefficient of the validation set (Rv2) was 0.88, the RMSE of the validation set (RMSEv) was 3.35 and relative prediction deviation (RPD) was 2.88). Thus, this modeling method could be used as a basic method to predict the CCC of winter wheat in the Guanzhong Plain area.