Comparative Evaluation of Machine Learning Models for Predicting Leaf Gas Exchange Traits from Hyperspectral Reflectance
Abstract
Hyperspectral remote sensing has been successfully used to retrieve parameters such as chlorophyll and nitrogen content. However, effective models for inverting dynamic physiological processes like gas exchange are lacking, and the predictive accuracy and applicable limits of such inversions remain unclear. In a controlled water and nitrogen stress experiment, 350–1150 nm leaf reflectance spectra were obtained along with simultaneous measurements of 12 physiological parameters. PLSR, RF, XGBoost, and LightGBM models were constructed, and SHAP was utilized for model interpretation. The results showed that predictive accuracies could be categorized into high, medium, and low tiers (R2 approximately 0.8, 0.5, and
Fields of Study & Subjects
Remote Sensing in Agriculture
Plant Water Relations and Carbon Dynamics
Soil Moisture and Remote Sensing
Publication details
- Journal / source
- Agriculture
- Publisher
- MDPI AG
- Volume
- 16
- Issue
- 9
- Pages
- 1015–1015
- Publication date
- 6 May 2026
- Language
- EN