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.Comment: Source code available at
https://github.com/earthnet2021/earthnet-models-pytorc