Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

Remote Sensing of Lake Water Clarity: Performance and Transferability of Both Historical Algorithms and Machine Learning

There has been little rigorous investigation of the transferability of existing empirical water clarity models developed at one location or time to other lakes and dates of imagery with differing conditions. Machine learning methods have not been widely adopted for analysis of lake optical propertie...

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Bibliographic Details
Main Authors: Hannah J. Rubin, David A. Lutz, Bethel G. Steele, Kathryn L. Cottingham, Kathleen C. Weathers, Mark J. Ducey, Michael Palace, Kenneth M. Johnson, Jonathan W. Chipman
Format: Article
Language:English
Published: MDPI AG 2021-04-01
Series:Remote Sensing
Subjects:
Landsat
lakes
Secchi depth
machine learning
random forest
water clarity
Online Access:https://www.mdpi.com/2072-4292/13/8/1434

Internet

https://www.mdpi.com/2072-4292/13/8/1434

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