Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions

Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions

This study presents an automated methodology to generate training data for surface water mapping from a single Sentinel-2 granule at 10 m (4 band, VIS/NIR) or 20 m (9 band, VIS/NIR/SWIR) resolution without the need for ancillary training data layers. The 20 m method incorporates an ensemble of three...

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Autores principales: Kristofer Lasko, Megan C. Maloney, Sarah J. Becker, Andrew W. H. Griffin, Susan L. Lyon, Sean P. Griffin
Formato: article
Lenguaje:EN
Publicado: MDPI AG 2021
Materias:
surface water
water index
band ratios
machine learning
random forest
multispectral
Science
Q
Acceso en línea:https://doaj.org/article/6161a5bed89240a9842e572dcc904218
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https://doaj.org/article/6161a5bed89240a9842e572dcc904218

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