A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef

Patricio-Valerio, Larissa, Schroeder, Thomas, Devlin, Michelle J., Qin, Yi, and Smithers, Scott (2022) A Machine Learning Algorithm for Himawari-8 Total Suspended Solids Retrievals in the Great Barrier Reef. Remote Sensing, 14 (14). 3503.

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Remote sensing of ocean colour has been fundamental to the synoptic-scale monitoring of marine water quality in the Great Barrier Reef (GBR). However, ocean colour sensors onboard low orbit satellites, such as the Sentinel-3 constellation, have insufficient revisit capability to fully resolve diurnal variability in highly dynamic coastal environments. To overcome this limitation, this work presents a physics-based coastal ocean colour algorithm for the Advanced Himawari Imager onboard the Himawari-8 geostationary satellite. Despite being designed for meteorological applications, Himawari-8 offers the opportunity to estimate ocean colour features every 10 min, in four broad visible and near-infrared spectral bands, and at 1 km2 spatial resolution. Coupled ocean–atmosphere radiative transfer simulations of the Himawari-8 bands were carried out for a realistic range of in-water and atmospheric optical properties of the GBR and for a wide range of solar and observation geometries. The simulated data were used to develop an inverse model based on artificial neural network techniques to estimate total suspended solids (TSS) concentrations directly from the Himawari-8 top-of-atmosphere spectral reflectance observations. The algorithm was validated with concurrent in situ data across the coastal GBR and its detection limits were assessed. TSS retrievals presented relative errors up to 75% and absolute errors of 2 mg L−1 within the validation range of 0.14 to 24 mg L−1, with a detection limit of 0.25 mg L−1. We discuss potential applications of Himawari-8 diurnal TSS products for improved monitoring and management of water quality in the GBR.

Item ID: 76480
Item Type: Article (Research - C1)
ISSN: 2072-4292
Keywords: artificial neural networks, coastal waters, Great Barrier Reef, Himawari-8, machine learning, ocean colour, total suspended solids, water quality
Copyright Information: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Date Deposited: 08 Mar 2023 01:08
FoR Codes: 41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410402 Environmental assessment and monitoring @ 50%
41 ENVIRONMENTAL SCIENCES > 4104 Environmental management > 410404 Environmental management @ 50%
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