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Article

Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada

1
Department of Ecology and Evolutionary Biology, University of Toronto, 25 Willcocks Street, Toronto, ON M5S 3B2, Canada
2
Department of Natural Resources Sciences and Bieler School of Environment, McGill University, Macdonald-Stewart Building, Montreal, QC H9X 3V9, Canada
3
Department of Natural Resources Sciences, McGill University, Macdonald-Stewart Building, Montreal, QC H9X 3V9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Jonathan Chipman
Received: 26 February 2021 / Revised: 22 March 2021 / Accepted: 24 March 2021 / Published: 26 March 2021
(This article belongs to the Special Issue Remote Sensing of Lake Properties and Dynamics)
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. View Full-Text
Keywords: Landsat 8; OLI; Secchi disk depth; water clarity; Canadian lakes; empirical algorithm Landsat 8; OLI; Secchi disk depth; water clarity; Canadian lakes; empirical algorithm
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MDPI and ACS Style

Deutsch, E.S.; Cardille, J.A.; Koll-Egyed, T.; Fortin, M.-J. Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada. Remote Sens. 2021, 13, 1257. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071257

AMA Style

Deutsch ES, Cardille JA, Koll-Egyed T, Fortin M-J. Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada. Remote Sensing. 2021; 13(7):1257. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071257

Chicago/Turabian Style

Deutsch, Eliza S., Jeffrey A. Cardille, Talia Koll-Egyed, and Marie-Josée Fortin. 2021. "Landsat 8 Lake Water Clarity Empirical Algorithms: Large-Scale Calibration and Validation Using Government and Citizen Science Data from across Canada" Remote Sensing 13, no. 7: 1257. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13071257

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