Recurrent Neural Networks for Modelling Gross Primary Production
<|reference_start|>Recurrent Neural Networks for Modelling Gross Primary Production: Accurate quantification of Gross Primary Production (GPP) is crucial for understanding terrestrial carbon dynamics. It represents the largest atmosphere-to-land CO$_2$ flux, especially significant for forests. Eddy Covariance (EC) measurements are widely used for ecosystem-scale GPP quantification but are globally sparse. In areas lacking local EC measurements, remote sensing (RS) data are typically utilised to estimate GPP after statistically relating them to in-situ data. Deep learning offers novel perspectives, and the potential of recurrent neural network architectures for estimating daily GPP remains underexplored. This study presents a comparative analysis of three architectures: Recurrent Neural Networks (RNNs), Gated Recurrent Units (GRUs), and Long-Short Term Memory (LSTMs). Our findings reveal comparable performance across all models for full-year and growing season predictions. Notably, LSTMs outperform in predicting climate-induced GPP extremes. Furthermore, our analysis highlights the importance of incorporating radiation and RS inputs (optical, temperature, and radar) for accurate GPP predictions, particularly during climate extremes.<|reference_end|>
@article{montero2024recurrent,
    title={Recurrent Neural Networks for Modelling Gross Primary Production},
    author={David Montero, Miguel D. Mahecha, Francesco Martinuzzi, C'esar Aybar,
  Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Jes'us Anaya,
  Sebastian Wieneke},
    journal={arXiv preprint arXiv:2404.12745},
    year={2024},
    doi={10.1109/IGARSS53475.2024.10640715},
    archivePrefix={arXiv},
    eprint={2404.12745},
    primaryClass={cs.LG}
}