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Remote Sensing
Volume 17
Issue 11
10.3390/rs17111826
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data

by
Mohamad Abed El Rahman Hammoud
1,†,
Nikolaos Papagiannopoulos
2,†,
George Krokos
3,
Robert J. W. Brewin
4,
Dionysios E. Raitsos
5,
Omar Knio
6 and
Ibrahim Hoteit
7,*
1
Department of Civil and Environmental Engineering, Princeton University, Princeton, NJ 08540, USA
2
Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
3
Institute of Oceanography, Hellenic Centre for Marine Research, 19013 Anavyssos, Greece
4
Centre for Geography and Environmental Science, University of Exeter, Exeter EX4 4PY, UK
5
Department of Biology, National and Kapodistrian University of Athens, 15772 Athens, Greece
6
Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
7
Physical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955, Saudi Arabia
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2025, 17(11), 1826; https://doi.org/10.3390/rs17111826
Submission received: 9 March 2025 / Revised: 11 May 2025 / Accepted: 20 May 2025 / Published: 23 May 2025
(This article belongs to the Special Issue Recent Advances in Water Quality Monitoring)
Versions Notes

Abstract

This work introduces the use of Bayesian Neural Networks (BNNs) for inferring chlorophyll-a concentration ([CHL-a]) from remotely sensed data. BNNs are probabilistic models that associate a probability distribution to the neural network parameters and rely on Bayes’ rule for training. The performance of the proposed probabilistic model is compared to that of standard ocean color algorithms, namely ocean color 4 (OC4) and ocean color index (OCI). An extensive in situ bio-optical dataset was used to train and validate the ocean color models. In contrast to established methods, the BNN allows for enhanced modeling flexibility, where different variables that affect phytoplankton phenology or describe the state of the ocean can be used as additional input for enhanced performance. Our results suggest that BNNs perform at least as well as established methods, and they could achieve 20–40% lower mean squared errors when additional input variables are included, such as the sea surface temperature and its climatological mean alongside the coordinates of the prediction. The BNNs offer means for uncertainty quantification by estimating the probability distribution of [CHL-a], building confidence in the [CHL-a] predictions through the variance of the predictions. Furthermore, the output probability distribution can be used for risk assessment and decision making through analyzing the quantiles and shape of the predicted distribution.
Keywords: ocean color; chlorophyll-a; remote sensing; Bayesian neural network ocean color; chlorophyll-a; remote sensing; Bayesian neural network

Share and Cite

MDPI and ACS Style

Hammoud, M.A.E.R.; Papagiannopoulos, N.; Krokos, G.; Brewin, R.J.W.; Raitsos, D.E.; Knio, O.; Hoteit, I. On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data. Remote Sens. 2025, 17, 1826. https://doi.org/10.3390/rs17111826

AMA Style

Hammoud MAER, Papagiannopoulos N, Krokos G, Brewin RJW, Raitsos DE, Knio O, Hoteit I. On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data. Remote Sensing. 2025; 17(11):1826. https://doi.org/10.3390/rs17111826

Chicago/Turabian Style

Hammoud, Mohamad Abed El Rahman, Nikolaos Papagiannopoulos, George Krokos, Robert J. W. Brewin, Dionysios E. Raitsos, Omar Knio, and Ibrahim Hoteit. 2025. "On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data" Remote Sensing 17, no. 11: 1826. https://doi.org/10.3390/rs17111826

APA Style

Hammoud, M. A. E. R., Papagiannopoulos, N., Krokos, G., Brewin, R. J. W., Raitsos, D. E., Knio, O., & Hoteit, I. (2025). On the Potential of Bayesian Neural Networks for Estimating Chlorophyll-a Concentration from Satellite Data. Remote Sensing, 17(11), 1826. https://doi.org/10.3390/rs17111826

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