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

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 R