Real-time Prediction of Global Tropical Deciduous Ecosystem Phenology with Deep Learning

Tropical deciduous ecosystems play a critical role in terrestrial ecological processes and the global carbon cycle, influencing seasonal climates through phenology-induced biophysical and biogeochemical feedbacks. Phenological processes for tropical deciduous ecosystems are complex with multiple intertwining climatic and physiological factors that co-shape the underlying dynamics. Here, we present a deep learning framework based on Temporal Fusion Transformer for predicting tropical deciduous phenology globally in real-time with high accuracy. The framework integrates long-term AVHRR-derived vegetation greenness data, high-resolution climate data from ERA-Land, and land surface features including physical and chemical properties to account for terrestrial spatial heterogeneities that affect phenological processes. Our preliminary results demonstrate the ability of the developed framework to accurately predict historical phenological dynamics across 35 growing seasons in the pan-tropical regions. Key phenological metrics, including the start, peak, and end of the growing season, are identified with high accuracy. We believe the framework provides a powerful tool for real-time predictions and reconstructions of phenological states for tropical deciduous ecosystems, especially in regions where human activities like deforestation and agriculture heavily influence the estimates of tropical carbon cycle potential. With insight into the potential phenological states, this framework may help inform sustainable land management practices in pan-tropical regions.