Recuperación dinámica del índice de área foliar verdadera de maíz con KGCNN y TL y modelado de transferencia...

Accurate and real-time monitoring true leaf area index (LAI) is an essential for assessing crop growth status and predicting yields. Conventional LAI inversion approaches have been constrained by insufficient data representativeness and environmental variability, particularly when applied across interannual variations and different phenological stages. This study presented a novel methodology integrating three-dimensional radiative transfer modeling (3D RTM) with knowledge-guided deep learning to address these limitations. We developed a knowledge-guided convolutional neural network (KGCNN) architecture incorporating 3D canopy structural physics, enhanced through transfer learning (TL) techniques for cross-temporal adaptation. The KGCNN model was initially pre-trained on synthetic datasets generated by the large-scale remote sensing scattering model (LESS), followed by domain-specific fine-tuning using 2021 field measurements, and culminating in cross-year validation with 2022-2023 datasets. Our results demonstrated significant improvements over conventional approaches, with the 3D RTM-based KGCNN achieving superior performance compared to 1D RTM implementations (PROSAIL + CNN + TL). Specially, for the 2022 dataset, the overall R² 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.