A convolutional neural network model for accurate short-term leaf area index prediction

The leaf area index (LAI) is a crucial biophysical parameter that significantly influences carbon, water, and energy cycles within terrestrial ecosystems. While short-term LAI prediction has been extensively studied, most research has primarily focused on specific ecosystem types. This comprehensive study evaluates the performance of a convolutional neural network (CNN) model across 11 diverse land-cover types within global terrestrial ecosystems. Our results reveal the promising predictive capabilities of the CNN model, achieving an overall R² of 0.845 and an RMSE of 0.301, outperforming all the other baseline models. Notably, seasonal analysis shows higher prediction accuracy (lower SMAPE) in summer than in winter for most of the land cover types studied. We further identify radiation as a key environmental factor influencing LAI prediction accuracy across various land cover types. Overall, this …