Ai4Fapar: How artificial intelligence can help to forecast the seasonal earth observation signal
This work addresses forecasting seasonal earth observation signals for environmental monitoring, but it is incremental as it applies an existing method to new data with specific gains.
The paper investigated using a multivariate Transformer model to forecast Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) for short and long horizons in Europe and North Africa, finding it outperforms a climatological benchmark for one-month predictions with RMSE values of 0.02 to 0.04 FAPAR units.
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.