Physics-Driven Machine-Learning Retrieval and Uncertainty Quantification of Crop Leaf Area Index

Abstract

Leaf Area Index (LAI) is a key biophysical descriptor of crop canopies and is essential for growth monitoring and yield estimation. We present a physics-driven machine-learning framework for operational LAI retrieval and end-to-end uncertainty quantification that couples the PROSAIL radiative transfer model with a genetic-algorithm-optimised multilayer perceptron (NN–GA). PROSAIL is sampled across plausible parameter priors and spectra are convolved with Sentinel-2B spectral response functions to build a 30,000-sample training library; a GA is used to globally optimise network weights and biases. Total retrieval uncertainty is decomposed into a simulation component (PROSAIL parameter variability) and a training component (variability across repeated NN–GA trainings) and combined via the law of propagation of uncertainty. The model was developed in Minqin (modelling/testing area; entirely maize) and transferred to Zhangye (transfer/validation area; predominantly maize, with one sunflower plot). Sentinel-2B validation results were RMSE/R2 = 0.44/0.73 (Minqin) and 0.40/0.56 (Zhangye), indicating reasonable cross-site generalisation. The uncertainty split indicates physical-driven contributions of 11.42% and 11.48% and machine-learning contributions of 18.06% and 12.96%, respectively. The framework improves 10 m LAI retrieval accuracy and supplies a reproducible, per-pixel uncertainty budget to guide product use and refinement.

Plant phenotyping relevance

作物キャノピーのLAIという明示的な植物形質を、放射伝達モデルと機械学習で衛星データから推定し、不確実性定量化とサイト間検証まで行う手法中心の研究。

Code and data availability

The supplied blocks describe field LAI measurements (LAI-2200), Sentinel-2B imagery, a PROSAIL synthetic training library, and an NN–GA retrieval pipeline, but contain no authors' public deposit of these datasets, images, code, or trained models. The only public URLs are a cited prior-work Zenodo record (Python SL2P, a

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