Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada
Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-03-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/13/7/1257 |
| _version_ | 1797539955934756864 |
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| author | Eliza S. Deutsch Jeffrey A. Cardille Talia Koll-Egyed Marie-Josée Fortin |
| author_facet | Eliza S. Deutsch Jeffrey A. Cardille Talia Koll-Egyed Marie-Josée Fortin |
| author_sort | Eliza S. Deutsch |
| collection | DOAJ |
| description | Water clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data. |
| first_indexed | 2024-03-10T12:53:13Z |
| format | Article |
| id | doaj.art-5fba650ab9bf4d42a6c04396c4ade969 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-10T12:53:13Z |
| publishDate | 2021-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-5fba650ab9bf4d42a6c04396c4ade9692023-11-21T12:05:36ZengMDPI AGRemote Sensing2072-42922021-03-01137125710.3390/rs13071257Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across CanadaEliza S. Deutsch0Jeffrey A. Cardille1Talia Koll-Egyed2Marie-Josée Fortin3Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, CanadaDepartment of Natural Resources Sciences and Bieler School of Environment, McGill University, Macdonald-Stewart Building, Montreal, QC H9X 3V9, CanadaDepartment of Natural Resources Sciences, McGill University, Macdonald-Stewart Building, Montreal, QC H9X 3V9, CanadaDepartment of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, CanadaWater clarity has been extensively assessed in Landsat-based remote sensing studies of inland waters, regularly relying on locally calibrated empirical algorithms, and close temporal matching between field data and satellite overpass. As more satellite data and faster data processing systems become readily accessible, new opportunities are emerging to revisit traditional assumptions concerning empirical calibration methodologies. Using Landsat 8 images with large water clarity datasets from southern Canada, we assess: (1) whether clear regional differences in water clarity algorithm coefficients exist and (2) whether model fit can be improved by expanding temporal matching windows. We found that a single global algorithm effectively represents the empirical relationship between in situ Secchi disk depth (SDD) and the Landsat 8 Blue/Red band ratio across diverse lake types in Canada. We also found that the model fit improved significantly when applying a median filter on data from ever-wider time windows between the date of in situ SDD sample and the date of satellite overpass. The median filter effectively removed the outliers that were likely caused by atmospheric artifacts in the available imagery. Our findings open new discussions on the ability of large datasets and temporal averaging methods to better elucidate the true relationships between in situ water clarity and satellite reflectance data.https://www.mdpi.com/2072-4292/13/7/1257Landsat 8OLISecchi disk depthwater clarityCanadian lakesempirical algorithm |
| spellingShingle | Eliza S. Deutsch Jeffrey A. Cardille Talia Koll-Egyed Marie-Josée Fortin Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada Remote Sensing Landsat 8 OLI Secchi disk depth water clarity Canadian lakes empirical algorithm |
| title | Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada |
| title_full | Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada |
| title_fullStr | Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada |
| title_full_unstemmed | Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada |
| title_short | Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada |
| title_sort | landsat 8 lake water clarity empirical algorithms large scale calibration and validation using government and citizen science data from across canada |
| topic | Landsat 8 OLI Secchi disk depth water clarity Canadian lakes empirical algorithm |
| url | https://www.mdpi.com/2072-4292/13/7/1257 |
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