Ai4Fapar: How artificial intelligence can help to forecast the seasonal
  earth observation signal
Filip Sabo Martin Claverie Michele Meroni Arthur Hrast Essenfelder
This paper investigated the potential of a multivariate Transformer model to
forecast the temporal trajectory of the Fraction of Absorbed Photosynthetically
Active Radiation (FAPAR) for short (1 month) and long horizon (more than 1
month) periods at the regional level in Europe and North Africa. The input data
covers the period from 2002 to 2022 and includes remote sensing and weather
data for modelling FAPAR predictions. The model was evaluated using a leave one
year out cross-validation and compared with the climatological benchmark.
Results show that the transformer model outperforms the benchmark model for one
month forecasting horizon, after which the climatological benchmark is better.
The RMSE values of the transformer model ranged from 0.02 to 0.04 FAPAR units
for the first 2 months of predictions. Overall, the tested Transformer model is
a valid method for FAPAR forecasting, especially when combined with weather
data and used for short-term predictions.