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

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.

Nondestructive Testing and Visualization of Catechin Content in Black Tea Fermentation Using Hyperspectral Imaging

“…Like any of the principal component input numbers, the penalty coefficient c and the kernel parameter g influence the accuracy of the model, so we used the verification function tunelsssvm for optimization. The results of optimization are shown in Figure 5 c,e,g,i [ 25 ].…”

Towards Synoptic Water Monitoring Systems: A Review of AI Methods for Automating Water Body Detection and Water Quality Monitoring Using Remote Sensing

“…Yet while RS is a useful tool for monitoring water quality parameters, it has not been meaningfully integrated into operational water quality monitoring programs. Existing water quality time series data were used in [ 75 ] and assessed the effectiveness of multiple RS data platforms and ML models in estimating various water quality parameters. The authors showed that some sensors are poorly correlated with water quality parameters, while others are more suitable for water quality monitoring tasks.…”

“… More research needs to be carried out on analyzing the importance of input data to output predictions. See examples in [ 62 , 75 ], each detailed below. The authors in [ 62 ] systematically analyzed relative variable importance to show which sets of input data contributed to the ML models’ performance.…”

“…The red band (wavelength ≈ 0.665 µm) and the product of red and green band (wavelength ≈ 0.560 µm) were influential inputs in both reflectance data sets for estimating SS and turbidity, and the ratio between red and blue band (wavelength ≈ 0.490 µm) as well as the ratio between infrared (wavelength ≈ 0.865 µm) and blue band and green band proved to be more useful for the estimation of Chl-a concentration, due to their sensitivity to high turbidity in the coastal waters”. The authors in [ 75 ] utilized existing water quality time series data and assessed the effectiveness of multiple RS data platforms and ML models in estimating various water quality parameters. One of their interesting findings is that some sensors are poorly correlated with water quality parameters, while others are more suitable for water quality monitoring tasks.…”

“…The authors in [ 75 ] utilized existing water quality time series data and assessed the effectiveness of multiple RS data platforms and ML models in estimating various water quality parameters. One of their interesting findings is that some sensors are poorly correlated with water quality parameters, while others are more suitable for water quality monitoring tasks.…”