Deep learning-enabled exploration of global spectral features for photosynthetic capacity estimation

Spectral analysis is a widely used method for monitoring photosynthetic capacity. However, vegetation indices-based linear regression exhibits insufficient utilization of spectral information, while full spectra-based traditional machine learning has limited representational capacity (partial least squares regression) or uninterpretable (convolution). In this study, we proposed a deep learning model with enhanced interpretability based on attention and vegetation indices calculation for global spectral feature mining to accurately estimate photosynthetic capacity. We explored the ability of the model to uncover the optimal vegetation indices form and illustrated its advantage over traditional methods. Furthermore, we verified that power compression was an effective method for spectral processing. Our results demonstrated that the new model outperformed traditional models, with an increase in the coefficient of determination (R²) of 0.01-0.43 and a decrease in root mean square error (RMSE) of 1.58-12.48 μmol m^-2 s^-1. The best performance of our model in R² was 0.86 and 0.81 for maximum carboxylation rate (V ) and maximum electron transport rate (J ), respectively. The photosynthesis-sensitive spectral bands identified by our model were predominantly in the visible range. The most sensitive vegetation indices form discovered by our model was $$ . Our model provides a new framework for interpreting spectral information and accurately estimating photosynthetic capacity.