Tools
Tools
Patricio-Valerio, Larissa, Schroeder, Thomas, Devlin, Michelle J. 
ORCID: https://orcid.org/0000-0003-2194-2534, 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).
ISSN 2072-4292
Preview |
PDF (remotesensing-14-03503-v4)
- Published Version
Available under License Creative Commons Attribution. Download (6MB) | Preview |
Abstract
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 Type: | Article |
|---|---|
| Additional Information: | Data Availability Statement: The data presented in this study are available on request from the corresponding author. |
| Uncontrolled Keywords: | artificial neural networks,coastal waters,great barrier reef,himawari-8,machine learning,ocean colour,total suspended solids,water quality,general earth and planetary sciences ,/dk/atira/pure/subjectarea/asjc/1900/1900 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences |
| UEA Research Groups: | Faculty of Science > Research Groups > Collaborative Centre for Sustainable Use of the Seas |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 18 Jun 2026 09:59 |
| Last Modified: | 18 Jun 2026 21:02 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/103426 |
| DOI: | 10.3390/rs14143503 |
Downloads
Downloads per month over past year
Actions (login required)
 |
View Item |