El-Alem, Anas et Chokmani, Karem 
ORCID: https://orcid.org/0000-0003-0018-0761
(2022).
A Machine Learning-Based Regional Hybrid Model for Remote Retrieving Turbidity From Landsat Imagery.
IEEE Geoscience and Remote Sensing Letters
, vol. 19
.
pp. 1-5.
DOI: 10.1109/LGRS.2021.3115986.
Résumé
Turbidity [nephelometric turbidity unit (NTU)] monitoring is of great interest to water quality stakeholders. Traditional monitoring programs are limited in time and space, are expensive, and do not reflect the true extent of NTU. In contrast, remote sensing data are able to model the NTU, to monitor its spatial expansion, and are cost-effective. Models developed are usually a single-based function. This study presents a simple machine learning-based Regional hybrid model (R-HM) for NTU retrieval. The R-HM allows prior recognition of the NTU level concentration (high or low) before estimation. The calibration step highlighted that low and high NTUs are sensitive to different spectral regions, but mainly controlled by the red part. Validation was satisfactory with R²=0.99, although high NTUs tend to be underestimated (BIAS = −14%). Landsat (LS) NTU products derived from R-HM were found to be only sensitive to turbidity, even under conditions of high algal blooms.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | inland waters; Landsat (LS); machine learning; nephelometric turbidity unit (NTU); remote sensing; turbidity |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 23 juin 2022 14:25 |
| Dernière modification: | 23 juin 2022 14:25 |
| URI: | https://espace.inrs.ca/id/eprint/12719 |
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