  The innovative application of precise geospatial vegetation forecasting holds
immense potential across diverse sectors, including agriculture, forestry,
humanitarian aid, and carbon accounting. To leverage the vast availability of
satellite imagery for this task, various works have applied deep neural
networks for predicting multispectral images in photorealistic quality.
However, the important area of vegetation dynamics has not been thoroughly
explored. Our study breaks new ground by introducing GreenEarthNet, the first
dataset specifically designed for high-resolution vegetation forecasting, and
Contextformer, a novel deep learning approach for predicting vegetation
greenness from Sentinel 2 satellite images with fine resolution across Europe.
Our multi-modal transformer model Contextformer leverages spatial context
through a vision backbone and predicts the temporal dynamics on local context
patches incorporating meteorological time series in a parameter-efficient
manner. The GreenEarthNet dataset features a learned cloud mask and an
appropriate evaluation scheme for vegetation modeling. It also maintains
compatibility with the existing satellite imagery forecasting dataset
EarthNet2021, enabling cross-dataset model comparisons. Our extensive
qualitative and quantitative analyses reveal that our methods outperform a
broad range of baseline techniques. This includes surpassing previous
state-of-the-art models on EarthNet2021, as well as adapted models from time
series forecasting and video prediction. 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 predicting vegetation health and behaviour in response to
climate variability and extremes.
