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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Bibliographic Details
Main Authors: Kristofer Lasko, Megan C. Maloney, Sarah J. Becker, Andrew W. H. Griffin, Susan L. Lyon, Sean P. Griffin
Format: Article
Language:English
Published: MDPI AG 2021-11-01
Series:Remote Sensing
Subjects:
surface water
water index
band ratios
machine learning
random forest
multispectral
Online Access:https://www.mdpi.com/2072-4292/13/22/4531

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