We present a novel approach for modeling vegetation response to weather in Europe as measured by the Sentinel 2 satellite. Existing satellite imagery forecasting approaches focus on photorealistic quality of the multispectral images, while derived vegetation dynamics have not yet received as much attention. We leverage both spatial and temporal context by extending state-of-the-art video prediction methods with weather guidance. We extend the EarthNet2021 dataset to be suitable for vegetation modeling by introducing a learned cloud mask and an appropriate evaluation scheme. Qualitative and quantitative experiments demonstrate superior performance of our approach over a wide variety of baseline methods, including leading approaches to satellite imagery forecasting. Additionally, we show how our modeled vegetation dynamics can be leveraged in a downstream task: inferring gross primary productivity for carbon monitoring. To the best of our knowledge, this work presents the first models for continental-scale vegetation modeling at fine resolution able to capture anomalies beyond the seasonal cycle, thereby paving the way for predictive assessments of vegetation status.

翻译：我们提出了一种新方法，用于模拟欧洲地区植被对天气的响应（基于哨兵2号卫星观测数据）。现有卫星图像预测方法主要关注多光谱图像的真实感质量，而衍生的植被动态尚未得到足够重视。通过将先进视频预测方法与气象引导相结合，我们充分利用了空间与时间上下文信息。我们扩展了EarthNet2021数据集，引入学习型云掩膜与适配评估方案，使其适用于植被建模。定性与定量实验表明，本方法在包括主流卫星图像预测方法在内的多种基线方法中均展现出卓越性能。此外，我们展示了模拟的植被动态如何用于下游任务：通过推断总初级生产力实现碳监测。据我们所知，本研究首次实现了高分辨率大陆尺度植被建模，能够捕捉超越季节周期的异常现象，从而为植被状态预测评估铺平了道路。