Monitoring water quality of Valle de Bravo reservoir, Mexico, using entire lifespan of meris data and machine learning approaches
(2020) In Remote Sensing 12(10).- Abstract
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.... (More)
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.
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- author
- Arias-Rodriguez, Leonardo F. ; Duan, Zheng LU ; Sepúlveda, Rodrigo ; Martinez-Martinez, Sergio I. and Disse, Markus
- organization
- publishing date
- 2020-05-16
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Gaussian processes regression, Inland waters, Random forest regression, Remote sensing, Secchi disk depth, Support vector machines, Trophic state, Turbidity
- in
- Remote Sensing
- volume
- 12
- issue
- 10
- article number
- 1586
- publisher
- MDPI AG
- external identifiers
-
- scopus:85085577201
- ISSN
- 2072-4292
- DOI
- 10.3390/rs12101586
- language
- English
- LU publication?
- yes
- id
- 41393d11-6cd2-4363-b207-94cf9f0fd989
- date added to LUP
- 2020-06-17 15:06:36
- date last changed
- 2022-04-18 22:59:29
@article{41393d11-6cd2-4363-b207-94cf9f0fd989,
abstract = {{<p>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 R<sup>2</sup> 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.</p>}},
author = {{Arias-Rodriguez, Leonardo F. and Duan, Zheng and Sepúlveda, Rodrigo and Martinez-Martinez, Sergio I. and Disse, Markus}},
issn = {{2072-4292}},
keywords = {{Gaussian processes regression; Inland waters; Random forest regression; Remote sensing; Secchi disk depth; Support vector machines; Trophic state; Turbidity}},
language = {{eng}},
month = {{05}},
number = {{10}},
publisher = {{MDPI AG}},
series = {{Remote Sensing}},
title = {{Monitoring water quality of Valle de Bravo reservoir, Mexico, using entire lifespan of meris data and machine learning approaches}},
url = {{http://dx.doi.org/10.3390/rs12101586}},
doi = {{10.3390/rs12101586}},
volume = {{12}},
year = {{2020}},
}