Vegetation phenology that characters the periodic life cycles of plants is indicative of the interactions between the biosphere and the atmosphere. Robust modelling of vegetation phenology metrics that correspond to canopy development events is essential to our understanding of how plants and ecosystems respond to a changing climate. Given considerable uncertainties associated with vegetation phenology modelling using numerical models, we explore the deep learning approach to predicting the timing of global vegetation phenology metrics.

中文翻译：

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基于深度学习的全球陆地植被物候模型性能基准测试实验

表征植物周期性生命周期的植被物候表明生物圈和大气之间的相互作用。与冠层发育事件相对应的植被物候指标的稳健建模对于我们理解植物和生态系统如何应对气候变化至关重要。鉴于与使用数值模型的植被物候建模相关的相当大的不确定性，我们探索了深度学习方法来预测全球植被物候指标的时间。