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Modeling dissolved organic carbon in inland waters using an unmanned aerial vehicles-borne hyperspectral camera.

El Alem, Anas; Chokmani, Karem ORCID logoORCID: https://orcid.org/0000-0003-0018-0761; Venkatesan, Aarthi; Lhissou, Rachid; Martins, Sarah; Campbell, Peter G. C. ORCID logoORCID: https://orcid.org/0000-0001-7160-4571; Cardille, Jeffrey; McGeer, James et Smith, Scott (2024). Modeling dissolved organic carbon in inland waters using an unmanned aerial vehicles-borne hyperspectral camera. Science of The Total Environment , vol. 954 . p. 176258. DOI: 10.1016/j.scitotenv.2024.176258.

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Résumé

Remote sensing can provide an alternative solution to quantify Dissolved Organic Carbon (DOC) in inland waters. Sensors embedded on Unmanned Aerial Vehicles (UAV) and satellites that can capture the DOC have already shown good relationships between DOC and the Colored Dissolved Organic Matter absorption (aCDOM.) coefficients in specific spectral regions. However, since the signal recorded by the sensors is reflectance-based, DOC estimates accuracy decreases when inverting the aCDOM. coefficients to reflectance. Thus, the main objective is to study the potential of a UAV-borne hyperspectral camera to retrieve the DOC in inland waters and to develop reflectance-based models using UAV and satellite (Landsat-8 OLI and Sentinel-2 MSI) data. Ensemble based systems (EBS) were favored in this study. The EBSUAV calibration results showed that six spectral regions (543.5, 564.5, 580.5, 609.5, 660, and 684 nm) are sensitive to DOC in waters. The EBSUAV test results showed a good concordance between measured and estimated DOC with an R² = Nash-criterion (NASH) = 0.86, and RMSE (Root Mean Squares Error) = 0.68 mg C/L. The EBSSAT test results also showed a strong concordance between measured and estimated DOC with R² = NASH = 0.92 and RMSE = 0.74 mg C/L. The spatial distribution of DOC estimates showed no dependency to other optically active elements. Nevertheless, estimates were sensitive to haze and sun glint.

Type de document: Article
Mots-clés libres: DOC; aCDOM.; UAV; landsat; Sentinel-2; hyperspectral; inland water; remote sensing
Centre: Centre Eau Terre Environnement
Date de dépôt: 12 nov. 2024 16:29
Dernière modification: 12 nov. 2024 16:29
URI: https://espace.inrs.ca/id/eprint/15993

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