Simulation-based inference advances water quality mapping in shallow coral reef environments

Palola, Pirta ORCID: https://orcid.org/0000-0001-5574-0776; Theenathayalan, Varunan; Schröder, Cornelius; Martinez-Vicente, Victor; Collin, Antoine; Wright, Rosalie; Ward, Melissa; Thomson, Eleanor; Lopez-Garcia, Patricia ORCID: https://orcid.org/0000-0002-4689-2775; Hochberg, Eric J. ORCID: https://orcid.org/0000-0001-5400-9252; Malhi, Yadvinder ORCID: https://orcid.org/0000-0002-3503-4783; Wedding, Lisa M. ORCID: https://orcid.org/0000-0002-3782-915X. 2025 Simulation-based inference advances water quality mapping in shallow coral reef environments. Royal Society Open Science, 12 (5). 10.1098/rsos.241471

Before downloading, please read NORA policies.

[thumbnail of palola-et-al-simulation-based-inference-advances-water-quality-mapping-in-shallow-coral-reef-environments.pdf]

Preview

Text
© 2025 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.
palola-et-al-simulation-based-inference-advances-water-quality-mapping-in-shallow-coral-reef-environments.pdf - Published Version
Available under License Creative Commons Attribution 4.0.
Download (6MB) | Preview

Official URL: https://doi.org/10.1098/rsos.241471

Abstract/Summary

Human activities are altering coral reef ecosystems worldwide. Optical remote sensing via satellites and drones can offer novel insights into where and how coral reefs are changing. However, interpretation of the observed optical signal (remote-sensing reflectance) is an ill-posed inverse problem, as there may be multiple different combinations of water constituents, depth and benthic reflectance that result in a similar optical signal. Here, we apply a new approach, simulation-based inference, for addressing the inverse problem in marine remote sensing. The simulation-based inference algorithm combines physics-based analytical modelling with approximate Bayesian inference and machine learning. The input to the algorithm is remote-sensing reflectance, and the output is the likely range (posterior probability density) of phytoplankton and suspended minerals concentrations, coloured dissolved organic matter absorption, wind speed and depth. We compare inference models trained with simulated hyperspectral or multispectral reflectance spectra characterized by different signal-to-noise ratios. We apply the inference model to in situ radiometric data ( n = 4) and multispectral drone imagery collected on the Tetiaroa atoll (South Pacific). We show that water constituent concentrations can be estimated from hyperspectral and multispectral remote-sensing reflectance in optically shallow environments, assuming a single benthic cover. Future developments should consider spectral mixing of multiple benthic cover types.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1098/rsos.241471
ISSN:	2054-5703
Additional Keywords:	neural network, Bayes, remote sensing, statistical inference, coral reef, machine learning, radiative transfer, inverse problem
Date made live:	07 May 2025 09:23 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/539381

Item Type:

Publication - Article

Digital Object Identifier (DOI):

10.1098/rsos.241471

ISSN:

2054-5703

Additional Keywords:

neural network, Bayes, remote sensing, statistical inference, coral reef, machine learning, radiative transfer, inverse problem

Date made live:

07 May 2025 09:23 +0 (UTC)

URI:

https://nora.nerc.ac.uk/id/eprint/539381