Recurrent Neural Networks for Modelling Gross Primary Production

• URL: http://arxiv.org/abs/2404.12745v1
• Date: Fri, 19 Apr 2024 09:46:45 GMT
• Title: Recurrent Neural Networks for Modelling Gross Primary Production
• Authors: David Montero, Miguel D. Mahecha, Francesco Martinuzzi, César Aybar, Anne Klosterhalfen, Alexander Knohl, Franziska Koebsch, Jesús Anaya, Sebastian Wieneke,
• Abstract summary: Gross Primary Production is the largest atmosphere-to-land CO$$ flux, especially significant for forests. Deep learning offers novel perspectives, and the potential of neural network architectures for estimating daily MME 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)
• Score: 34.819587029115205
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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