Recurrent Neural Networks for Modelling Gross Primary Production

arXiv:2404.12745v1 Announce Type: new
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 …

abstract arxiv atmosphere carbon covariance cs.lg dynamics ecosystem forests modelling networks neural networks production quantification recurrent neural networks scale type understanding