Hammoud, M.A.E.R.; Papagiannopoulos, N.; Brewin, R.J.; Raitsos, D.E.; Knio, O.; Hoteit, I. Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations. Preprints2024, 2024012068. https://doi.org/10.20944/preprints202401.2068.v1
APA Style
Hammoud, M.A.E.R., Papagiannopoulos, N., Brewin, R.J., Raitsos, D.E., Knio, O., & Hoteit, I. (2024). Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations. Preprints. https://doi.org/10.20944/preprints202401.2068.v1
Chicago/Turabian Style
Hammoud, M.A.E.R., Omar Knio and Ibrahim Hoteit. 2024 "Bayesian Neural Networks for Estimating Chlorophyll-A Concentration Based on Satellite-Derived Ocean Colour Observations" Preprints. https://doi.org/10.20944/preprints202401.2068.v1
Abstract
This study explores the use of Bayesian Neural Networks (BNNs) for estimating chlorophyll-a concentration ([CHL-a]) from remotely sensed data. The BNN model enables uncertainty quantification, offering additional layers of information compared to traditional ocean colour models. An extensive in situ bio-optical dataset is utilized, generated by merging 27 data sources across the world’s oceans. The BNN model demonstrates remarkable capability in capturing mesoscale features and ocean circulation patterns, providing comprehensive insights into spatial and temporal variations in [CHL-a] across diverse marine ecosystems. In comparison to established ocean colour algorithms, such as Ocean Colour 4 (OC4), the BNN shows comparable performance in terms of correlation coefficients, errors, and biases when compared with the in situ data. The BNN, however, further provides critical information about the distribution of [CHL-a], which can be used to assess uncertainties in the prediction. Moreover, the BNN model’s ability to provide more information for coastal waters, especially with higher spatial resolution imagery, offers valuable advantages for marine research and management. By quantifying uncertainty, the model builds confidence in [CHL-a] predictions, even in regions with limited data coverage. This is reflected by the spread in the model predictions, where the BNN can detect a range of uncertainty bounds that reflect confidence in the retrievals. Introducing BNNs as probabilistic ocean colour models represents a significant advancement, enabling more accurate and reliable predictions of [CHL-a] in the global oceans.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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