Dynamic maize true leaf area index retrieval with KGCNN and TL and integrated 3D radiative transfer modeling for crop phenotyping

作者：Dan Zhao, Guijun Yang, Tongyu Xu, Fenghua Yu, Chengjian Zhang, Zhida Cheng, Lipeng Ren, Hao Yang · 发表于：Plant Phenomics · 年份：2025 · DOI：10.1016/j.plaphe.2025.100004 · 被引用次数：12 · 研究领域：Remote Sensing in Agriculture、Remote Sensing and LiDAR Applications、Land Use and Ecosystem Services

increased by 0.27 and RMSE decreased by 2.46; for the 2023 dataset, the overall RMSE decreased by 1.62, compared to the PROSAIL + TL method. Our method (3D RTM + KGCNN + TL) delivered superior LAI retrieval accuracy on the two-year datasets compared to LSTM + TL, RNN + TL, and 3D RTM + RF models. This study also introduced an effective 3D scene modeling strategy that integrates scenarios representing the measured data range with additional synthetic scenes generated through random combinations of structural parameters. By incorporating detailed 3D crop structural information into the KGCNN network and fine-tuning the model with measured data, the approach significantly enhanced the model's adaptability to varying data distributions across different years and growth stages. This approach thus improved both the accuracy and stability of true LAI retrieval.