Analysis of crop leaf area index, leaf chlorophyll content, and canopy chlorophyll content based on deep learning and hyperspectral remote sensing

作者：Mengdie Leng, Yang Liu, Bing Li, Yinchao Che, Haikuan Feng, Meiyan Shu, Xin Xu, Hongbo Qiao, Jibo Yue · 发表于：International Journal of Remote Sensing · 年份：2025 · DOI：10.1080/01431161.2025.2549533 · 被引用次数：3 · 研究领域：Remote Sensing in Agriculture、Leaf Properties and Growth Measurement、Remote Sensing and Land Use

Canopy chlorophyll content (CCC) is a critical indicator for assessing crop photosynthetic capacity, nitrogen status, and the occurrence of diseases. Accurate estimation of CCC holds significant importance for precision agriculture, providing a scientific basis for crop management, yield prediction, and stress detection. CCC is commonly defined as the product of leaf area index (LAI) and leaf chlorophyll content (LCC). Traditional methods of acquiring CCC rely on destructive sampling, which limits large-scale application. Hyperspectral remote sensing enables non-destructive acquisition of rich spectral information from the crop canopy across the visible to near-infrared spectrum, offering a promising approach for CCC estimation. This study proposes a convolutional neural network-based model, CanopyChlNet, to jointly estimate LAI and LCC, thereby deriving CCC. The model utilizes a one-dimensional CNN structure to effectively extract deep spectral features from hyperspectral data, improving estimation accuracy. Field-measured canopy hyperspectral reflectance and corresponding LAI and LCC data from winter wheat and potato were used to train and validate the model. The CanopyChlNet model outperformed both Random Forest (RF) and Partial Least Squares Regression (PLSR) in estimating LAI, LCC, and CCC, achieving R2 values of 0.709, 0.775, and 0.718, with RMSE values of 0.803 m2 ·m−2, 5.288 µg·cm− 2, and 34.938 µg·cm− 2, respectively. In comparison, RF yielded R2 values of 0.636, 0.6...