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Article

Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine

1
Chair of Hydrology and River Basin Management, Technical University of Munich, 80333 Munich, Germany
2
Department of Physical Geography and Ecosystem Science, Lund University, S-223 62 Lund, Sweden
3
Center for Research and Assistance in Technology and Design of the State of Jalisco, Colinas de la Normal, 44270 Guadalajara, Jalisco, Mexico
*
Author to whom correspondence should be addressed.
Academic Editors: Mariano Bresciani, Nicole Pinnel and Krista Alikas
Sensors 2021, 21(12), 4118; https://doi.org/10.3390/s21124118
Received: 21 May 2021 / Revised: 10 June 2021 / Accepted: 10 June 2021 / Published: 15 June 2021
(This article belongs to the Special Issue Remote Sensing of Water Quality and Water Environment)
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 (R2 = 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. View Full-Text
Keywords: Landsat 8 OLI; Sentinel 2 MSI; Sentinel 3 OLCI; water quality monitoring system; extreme learning machine; support vector regression; inland waters; turbidity; Chlorophyll-a; secchi disk depth Landsat 8 OLI; Sentinel 2 MSI; Sentinel 3 OLCI; water quality monitoring system; extreme learning machine; support vector regression; inland waters; turbidity; Chlorophyll-a; secchi disk depth
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MDPI and ACS Style

Arias-Rodriguez, L.F.; Duan, Z.; Díaz-Torres, J.d.J.; Basilio Hazas, M.; Huang, J.; Kumar, B.U.; Tuo, Y.; Disse, M. Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors 2021, 21, 4118. https://doi.org/10.3390/s21124118

AMA Style

Arias-Rodriguez LF, Duan Z, Díaz-Torres JdJ, Basilio Hazas M, Huang J, Kumar BU, Tuo Y, Disse M. Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine. Sensors. 2021; 21(12):4118. https://doi.org/10.3390/s21124118

Chicago/Turabian Style

Arias-Rodriguez, Leonardo F., Zheng Duan, José d.J. Díaz-Torres, Mónica Basilio Hazas, Jingshui Huang, Bapitha U. Kumar, Ye Tuo, and Markus Disse. 2021. "Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine" Sensors 21, no. 12: 4118. https://doi.org/10.3390/s21124118

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