Global Vegetation Modeling with Pre-Trained Weather Transformers
Pascal Janetzky Florian Gallusser Simon Hentschel Andreas Hotho Anna Krause
Accurate vegetation models can produce further insights into the complex
interaction between vegetation activity and ecosystem processes. Previous
research has established that long-term trends and short-term variability of
temperature and precipitation affect vegetation activity. Motivated by the
recent success of Transformer-based Deep Learning models for medium-range
weather forecasting, we adapt the publicly available pre-trained FourCastNet to
model vegetation activity while accounting for the short-term dynamics of
climate variability. We investigate how the learned global representation of
the atmosphere's state can be transferred to model the normalized difference
vegetation index (NDVI). Our model globally estimates vegetation activity at a
resolution of \SI{0.25}{\degree} while relying only on meteorological data. We
demonstrate that leveraging pre-trained weather models improves the NDVI
estimates compared to learning an NDVI model from scratch. Additionally, we
compare our results to other recent data-driven NDVI modeling approaches from
machine learning and ecology literature. We further provide experimental
evidence on how much data and training time is necessary to turn FourCastNet
into an effective vegetation model. Code and models will be made available upon
publication.