  Climate change is global, yet its concrete impacts can strongly vary between
different locations in the same region. Seasonal weather forecasts currently
operate at the mesoscale (> 1 km). For more targeted mitigation and adaptation,
modelling impacts to < 100 m is needed. Yet, the relationship between driving
variables and Earth's surface at such local scales remains unresolved by
current physical models. Large Earth observation datasets now enable us to
create machine learning models capable of translating coarse weather
information into high-resolution Earth surface forecasts. Here, we define
high-resolution Earth surface forecasting as video prediction of satellite
imagery conditional on mesoscale weather forecasts. Video prediction has been
tackled with deep learning models. Developing such models requires
analysis-ready datasets. We introduce EarthNet2021, a new, curated dataset
containing target spatio-temporal Sentinel 2 satellite imagery at 20 m
resolution, matched with high-resolution topography and mesoscale (1.28 km)
weather variables. With over 32000 samples it is suitable for training deep
neural networks. Comparing multiple Earth surface forecasts is not trivial.
Hence, we define the EarthNetScore, a novel ranking criterion for models
forecasting Earth surface reflectance. For model intercomparison we frame
EarthNet2021 as a challenge with four tracks based on different test sets.
These allow evaluation of model validity and robustness as well as model
applicability to extreme events and the complete annual vegetation cycle. In
addition to forecasting directly observable weather impacts through
satellite-derived vegetation indices, capable Earth surface models will enable
downstream applications such as crop yield prediction, forest health
assessments, coastline management, or biodiversity monitoring. Find data, code,
and how to participate at www.earthnet.tech .
