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Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine
Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232681/ https://www.ncbi.nlm.nih.gov/pubmed/34203863 http://dx.doi.org/10.3390/s21124118 |
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author | Arias-Rodriguez, Leonardo F. Duan, Zheng Díaz-Torres, José de Jesús Basilio Hazas, Mónica Huang, Jingshui Kumar, Bapitha Udhaya Tuo, Ye Disse, Markus |
author_facet | Arias-Rodriguez, Leonardo F. Duan, Zheng Díaz-Torres, José de Jesús Basilio Hazas, Mónica Huang, Jingshui Kumar, Bapitha Udhaya Tuo, Ye Disse, Markus |
author_sort | Arias-Rodriguez, Leonardo F. |
collection | PubMed |
description | Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R(2) = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs. |
format | Online Article Text |
id | pubmed-8232681 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82326812021-06-26 Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine Arias-Rodriguez, Leonardo F. Duan, Zheng Díaz-Torres, José de Jesús Basilio Hazas, Mónica Huang, Jingshui Kumar, Bapitha Udhaya Tuo, Ye Disse, Markus Sensors (Basel) Article Remote Sensing, as a driver for water management decisions, needs further integration with monitoring water quality programs, especially in developing countries. Moreover, usage of remote sensing approaches has not been broadly applied in monitoring routines. Therefore, it is necessary to assess the efficacy of available sensors to complement the often limited field measurements from such programs and build models that support monitoring tasks. Here, we integrate field measurements (2013–2019) from the Mexican national water quality monitoring system (RNMCA) with data from Landsat-8 OLI, Sentinel-3 OLCI, and Sentinel-2 MSI to train an extreme learning machine (ELM), a support vector regression (SVR) and a linear regression (LR) for estimating Chlorophyll-a (Chl-a), Turbidity, Total Suspended Matter (TSM) and Secchi Disk Depth (SDD). Additionally, OLCI Level-2 Products for Chl-a and TSM are compared against the RNMCA data. We observed that OLCI Level-2 Products are poorly correlated with the RNMCA data and it is not feasible to rely only on them to support monitoring operations. However, OLCI atmospherically corrected data is useful to develop accurate models using an ELM, particularly for Turbidity (R(2) = 0.7). We conclude that remote sensing is useful to support monitoring systems tasks, and its progressive integration will improve the quality of water quality monitoring programs. MDPI 2021-06-15 /pmc/articles/PMC8232681/ /pubmed/34203863 http://dx.doi.org/10.3390/s21124118 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Arias-Rodriguez, Leonardo F. Duan, Zheng Díaz-Torres, José de Jesús Basilio Hazas, Mónica Huang, Jingshui Kumar, Bapitha Udhaya Tuo, Ye Disse, Markus Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title | Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title_full | Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title_fullStr | Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title_full_unstemmed | Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title_short | Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine |
title_sort | integration of remote sensing and mexican water quality monitoring system using an extreme learning machine |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8232681/ https://www.ncbi.nlm.nih.gov/pubmed/34203863 http://dx.doi.org/10.3390/s21124118 |
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