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

ABSTRACT: 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 proc

SUBMITTER: Deng X 

PROVIDER: S-EPMC11769944 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature