Comparing Stacking Ensemble Learning and 1D-CNN Models for Predicting Leaf Chlorophyll Content in Stellera chamaejasme from Hyperspectral Reflectance Measurements

Stellera chamaejasme, a toxic invasive species widespread in degraded alpine grasslands, Qinghai Province, causes a significant threat to the local ecological balance. Accurate monitoring of the leaf chlorophyll content is essential for preventing its expansion over large areas. This study presents an optimal approach by integrating hierarchical dimensionality reduction, stacking ensemble learning, and 1D-CNN models to estimate leaf chlorophyll content in S. chamaejasme using hyperspectral reflectance data. Field spectrometry analysis demonstrates that the combination of Pearson correlation, first derivative, and SPA algorithms can efficiently select the most chlorophyll-sensitive wavelengths, red-edge parameters, and spectral indices related to S. chamaejasme leaves. The stacking ensemble model outperforms the 1D-CNN model in predicting leaf chlorophyll content of S. chamaejasme over the whole growth stage, while the 1D-CNN excels at prediction in each individual growth stage. Comparatively, the 1D-CNN model achieved higher accuracy (R-2 > 0.5) in all five growth stages, with optimal performance during the flower bud stage (R-2 = 0.787, RMSE = 2.476). This study underscores the potential of combining feature spectra selection with machine learning and deep learning models to monitor S. chamaejasme growth, offering valuable insights for invasive species control and ecological management.