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...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-11-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/13/22/4531 |
| _version_ | 1797508583966900224 |
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| author | Kristofer Lasko Megan C. Maloney Sarah J. Becker Andrew W. H. Griffin Susan L. Lyon Sean P. Griffin |
| author_facet | Kristofer Lasko Megan C. Maloney Sarah J. Becker Andrew W. H. Griffin Susan L. Lyon Sean P. Griffin |
| author_sort | Kristofer Lasko |
| collection | DOAJ |
| description | 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 spectral indexes with optimal band thresholds, whereas the 10 m method achieves similar results using fewer bands and a single spectral index. A spectrally balanced and randomly generated set of training data based on the index values and optimal thresholds is used to fit machine learning classifiers. Statistical validation compares the 20 m ensemble-only method to the 20 m ensemble method with a random forest classifier. Results show the 20 m ensemble-only method had an overall accuracy of 89.5% (±1.7%), whereas the ensemble method combined with the random forest classifier performed better, with a ~4.8% higher overall accuracy: 20 m method (94.3% (±1.3%)) with optimal spectral index and SWIR thresholds of −0.03 and 800, respectively, and 10 m method (93.4% (±1.5%)) with optimal spectral index and NIR thresholds of −0.01 and 800, respectively. Comparison of other supervised classifiers trained automatically with the framework typically resulted in less than 1% accuracy improvement compared with the random forest, suggesting that training data quality is more important than classifier type. This straightforward framework enables accurate surface water classification across diverse geographies, making it ideal for development into a decision support tool for water resource managers. |
| first_indexed | 2024-03-10T05:06:00Z |
| format | Article |
| id | doaj.art-6161a5bed89240a9842e572dcc904218 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T05:06:00Z |
| publishDate | 2021-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-6161a5bed89240a9842e572dcc9042182023-11-23T01:18:52ZengMDPI AGRemote Sensing2072-42922021-11-011322453110.3390/rs13224531Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m ResolutionsKristofer Lasko0Megan C. Maloney1Sarah J. Becker2Andrew W. H. Griffin3Susan L. Lyon4Sean P. Griffin5Geospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAGeospatial Research Laboratory, Engineer Research and Development Center, U.S. Army Corps of Engineers, Alexandria, VA 22315, USAThis 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 spectral indexes with optimal band thresholds, whereas the 10 m method achieves similar results using fewer bands and a single spectral index. A spectrally balanced and randomly generated set of training data based on the index values and optimal thresholds is used to fit machine learning classifiers. Statistical validation compares the 20 m ensemble-only method to the 20 m ensemble method with a random forest classifier. Results show the 20 m ensemble-only method had an overall accuracy of 89.5% (±1.7%), whereas the ensemble method combined with the random forest classifier performed better, with a ~4.8% higher overall accuracy: 20 m method (94.3% (±1.3%)) with optimal spectral index and SWIR thresholds of −0.03 and 800, respectively, and 10 m method (93.4% (±1.5%)) with optimal spectral index and NIR thresholds of −0.01 and 800, respectively. Comparison of other supervised classifiers trained automatically with the framework typically resulted in less than 1% accuracy improvement compared with the random forest, suggesting that training data quality is more important than classifier type. This straightforward framework enables accurate surface water classification across diverse geographies, making it ideal for development into a decision support tool for water resource managers.https://www.mdpi.com/2072-4292/13/22/4531surface waterwater indexband ratiosmachine learningrandom forestmultispectral |
| spellingShingle | Kristofer Lasko Megan C. Maloney Sarah J. Becker Andrew W. H. Griffin Susan L. Lyon Sean P. Griffin Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions Remote Sensing surface water water index band ratios machine learning random forest multispectral |
| title | Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions |
| title_full | Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions |
| title_fullStr | Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions |
| title_full_unstemmed | Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions |
| title_short | Automated Training Data Generation from Spectral Indexes for Mapping Surface Water Extent with Sentinel-2 Satellite Imagery at 10 m and 20 m Resolutions |
| title_sort | automated training data generation from spectral indexes for mapping surface water extent with sentinel 2 satellite imagery at 10 m and 20 m resolutions |
| topic | surface water water index band ratios machine learning random forest multispectral |
| url | https://www.mdpi.com/2072-4292/13/22/4531 |
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