Abstract
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.
Abstract (translated)
我们提出了一种新方法，用于模拟欧洲根据Sentinel 2卫星测量的气象对植被反应。现有的卫星图像预测方法主要关注多光谱图像的逼真质量，但相关的植被动态还未得到足够的关注和重视。我们利用时空 context 的优势，通过引入先进的视频预测方法，结合气象指导，将 EarthNet2021 数据集扩展为适合植被建模，并引入了学习 cloud mask 和适当的评估 scheme。定性和定量实验表明，我们的方法比许多基准方法表现更好，包括卫星图像预测的领先地位。此外，我们展示了我们模型的植被动态如何在后续任务中利用：推断碳监测的 gross primary productivity。据我们所知，这项工作提出了大陆尺度植被建模的高精度模型，能够捕捉到季节之外异常情况，为预测植被状态进行评估开辟了道路。
URL
https://arxiv.org/abs/2303.16198
