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.

利用 Transformer-based 深度学习模型，我们在考虑气候变化的短期动态的同时，通过转移与大气状态有关的全球学习表示，仅依靠气象数据全球性地估计了 0.25° 分辨率的植被活动，并且证明了与从头学习的 NDVI 模型相比，利用预训练的天气模型可以提高 NDVI 估计结果。