
Zheng Duan
Senior lecturer

Integration of remote sensing and Mexican water quality monitoring system using an extreme learning machine
Author
Summary, in English
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.
Department/s
- Dept of Physical Geography and Ecosystem Science
Publishing year
2021-06-02
Language
English
Publication/Series
Sensors
Volume
21
Issue
12
Document type
Journal article
Publisher
MDPI AG
Topic
- Remote Sensing
- Oceanography, Hydrology, Water Resources
Keywords
- Chlorophyll-a
- Extreme learning machine
- Inland waters
- Landsat 8 OLI
- Secchi disk depth
- Sentinel 2 MSI
- Sentinel 3 OLCI
- Support vector regression
- Turbidity
- Water quality monitoring system
Status
Published
ISBN/ISSN/Other
- ISSN: 1424-8220