Estimating Leaf Area Index and Leaf Nitrogen Content for Individual Populus Tree Using UAV Hyperspectral Data With Dual-Branch Deep Learning Architecture

Uncrewed aerial vehicles (UAVs) hyperspectral remote sensing provides a flexible approach for quantifying leaf area index (LAI) and leaf nitrogen content (LNC) of individual Populus tree. However, current methods neglect the influence of spatial scale, feature form, and model structure, resulting in unclear scale determination and limited spectral–spatial feature utilization. This study proposed a dual-branch deep learning (DL) architecture to fully exploit spatial and spectral features across multiple scales and feature forms. Hyperspectral image cubes of individual trees were generated at four scales. Three feature forms were evaluated, including all bands, selected bands, and vegetation indices (VIs). The dual-branch comprises parallel spectral and spatial branches, where spectral branch processes the averaged canopy reflectance, and the spatial branch uses the canopy image as input. The spectral branch integrates 1-D convolutional neural network (1-D CNN), attention mechanism (AM), and long short-term memory (LSTM) modules, while the spatial branch employs 2-D CNN, residual learning, and AM modules. In each branch, the optimal feature forms and spatial scales were determined and then used for developing the dual-branch models. The results showed that the optimal spatial resolutions for LAI and LNC estimation were 0.10 and 0.05 m, respectively. Both traits achieved optimal accuracy with selected bands used in the spectral branch and VIs used in the spatial branch. The proposed dual-branch models outperformed conventional DL models, with R2 of 0.861 ± 0.006 for LAI and 0.842 ± 0.009 for LNC. This method provides an accurate high-throughput approach for monitoring of Populus germplasm, facilitating precision breeding and phenotyping applications.

Recommended citation: ‘Chen Zhulin, Zou, Huimin, Wang Xuefeng, Zhang Naijing, Tao Guofeng, Li Jie, Qiao Shijiao, Xu Sheng "Estimating Leaf Area Index and Leaf Nitrogen Content for Individual Populus Tree Using UAV Hyperspectral Data With Dual-Branch Deep Learning Architecture. " IEEE Transactions on Geoscience and Remote Sensing. VOL. 64, pp. 1-18, 2026, Art no. 4404718.’