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

Arias-Rodriguez LF, Duan Z, Díaz-Torres JdJ, Basilio Hazas M, Huang J, Udhaya Kumar B, Tuo Y, Disse M (2021)

Publication Language: English

Publication Type: Journal article

Publication year: 2021

Journal

Sensors MDPI

URI: https://www.mdpi.com/1424-8220/21/12/4118

DOI: 10.3390/s21124118

Open Access Link: https://www.mdpi.com/1424-8220/21/12/4118

Abstract

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.

Authors with CRIS profile

Monica Basilio Hazas Professur für Angewandte Geologie und Modellierung von Umweltsystemen

Involved external institutions

Technische Universität München (TUM) DE Germany (DE) Lund University / Lunds universitet SE Sweden (SE) Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco A.C. (CIATEJ) MX Mexico (MX)

How to cite

APA:

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

MLA:

Arias-Rodriguez, Leonardo F., et al. "Integration of Remote Sensing and Mexican Water Quality Monitoring System Using an Extreme Learning Machine." Sensors (2021).

BibTeX: Download