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Article

Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data

Institute of Geosciences, Vilnius University, M. K. Čiurlionio 21/27, LT-03101 Vilnius, Lithuania
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Academic Editors: Xing Fang, Jiangyong Hu and Suresh Sharma
Water 2022, 14(11), 1732; https://doi.org/10.3390/w14111732
Received: 29 April 2022 / Revised: 24 May 2022 / Accepted: 26 May 2022 / Published: 28 May 2022
(This article belongs to the Special Issue Water Quality Modeling and Monitoring)
Inland waters are dynamic systems that are under pressure from anthropogenic activities, thus constant observation of these waters is essential. Remote sensing provides a great opportunity to have frequent observations of inland waters. The aim of this study was to create a data-driven model that uses a machine learning algorithm and Sentinel-2 data to classify lake observations into four biophysical classes: Clear, Moderate, Chla-dominated, and Turbid. We used biophysical variables such as water transparency, chlorophyll concentration, and suspended matter to define these classes. We tested six machine learning algorithms that use spectral features of lakes as input and chose random forest classifiers, which yielded the most accurate results. We applied our two-step model on 19,292 lake spectra for the years 2015–2020, from 226 lakes. The prevalent class in 67% of lakes was Clear, while 19% of lakes were likely affected by strong algal blooms (Chla-dominated class). The models created in this study can be applied to lakes in other regions where similar lake classes are found. Biophysical lake classification using Sentinel-2 MSI data can help to observe long-term and short-term changes in lakes, thus it can be a useful tool for water management experts and for the public. View Full-Text
Keywords: lakes; inland waters; classification; machine learning; Sentinel-2 lakes; inland waters; classification; machine learning; Sentinel-2
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MDPI and ACS Style

Grendaitė, D.; Stonevičius, E. Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data. Water 2022, 14, 1732. https://doi.org/10.3390/w14111732

AMA Style

Grendaitė D, Stonevičius E. Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data. Water. 2022; 14(11):1732. https://doi.org/10.3390/w14111732

Chicago/Turabian Style

Grendaitė, Dalia, and Edvinas Stonevičius. 2022. "Machine Learning Algorithms for Biophysical Classification of Lithuanian Lakes Based on Remote Sensing Data" Water 14, no. 11: 1732. https://doi.org/10.3390/w14111732

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