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Correction: Al-Kharusi, E.S., et al. Large-Scale Retrieval of Coloured Dissolved Organic Matter in Northern Lakes Using Sentinel-2 Data. Remote Sensing 2020, 12(1), p.157
Open AccessArticle

Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches

1
Hydrology and River Basin Management, Technical University of Munich, Arcisstrasse 21, 80333 Munich, Germany
2
Department of Physical Geography and Ecosystem Science, Lund University, Sölvegatan 12, S-223 62 Lund, Sweden
3
Department of Sanitary and Environmental Engineering, National Autonomous University of Mexico, Ciudad Universitaria, Mexico City 04510, Mexico
4
Center of Design and Construction Sciences, Autonomous University of Aguascalientes, Av. Universidad 940, 20131 Aguascalientes, Mexico
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(10), 1586; https://doi.org/10.3390/rs12101586
Received: 17 April 2020 / Revised: 12 May 2020 / Accepted: 14 May 2020 / Published: 16 May 2020
(This article belongs to the Special Issue Remote Sensing of Inland Waters and Their Catchments)
Remote-sensing-based machine learning approaches for water quality parameters estimation, Secchi Disk Depth (SDD) and Turbidity, were developed for the Valle de Bravo reservoir in central Mexico. This waterbody is a multipurpose reservoir, which provides drinking water to the metropolitan area of Mexico City. To reveal the water quality status of inland waters in the last decade, evaluation of MERIS imagery is a substantial approach. This study incorporated in-situ collected measurements across the reservoir and remote sensing reflectance data from the Medium Resolution Imaging Spectrometer (MERIS). Machine learning approaches with varying complexities were tested, and the optimal model for SDD and Turbidity was determined. Cross-validation demonstrated that the satellite-based estimates are consistent with the in-situ measurements for both SDD and Turbidity, with R2 values of 0.81 to 0.86 and RMSE of 0.15 m and 0.95 nephelometric turbidity units (NTU). The best model was applied to time series of MERIS images to analyze the spatial and temporal variations of the reservoir’s water quality from 2002 to 2012. Derived analysis revealed yearly patterns caused by dry and rainy seasons and several disruptions were identified. The reservoir varied from trophic to intermittent hypertrophic status, while SDD ranged from 0–1.93 m and Turbidity up to 23.70 NTU. Results suggest the effects of drought events in the years 2006 and 2009 on water quality were correlated with water quality detriment. The water quality displayed slow recovery through 2011–2012. This study demonstrates the usefulness of satellite observations for supporting inland water quality monitoring and water management in this region. View Full-Text
Keywords: turbidity; secchi disk depth; trophic state; remote sensing; gaussian processes regression; support vector machines; random forest regression; inland waters turbidity; secchi disk depth; trophic state; remote sensing; gaussian processes regression; support vector machines; random forest regression; inland waters
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MDPI and ACS Style

Arias-Rodriguez, L.F.; Duan, Z.; Sepúlveda, R.; Martinez-Martinez, S.I.; Disse, M. Monitoring Water Quality of Valle de Bravo Reservoir, Mexico, Using Entire Lifespan of MERIS Data and Machine Learning Approaches. Remote Sens. 2020, 12, 1586.

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