Detection and Sourcing of CDOM in Urban Coastal Waters With UV-Visible Imaging Spectroscopy

Ultraviolet (UV)-visible imaging spectroscopy is an emerging and highly anticipated technology, expected to improve the remote sensing of coastal waters and expand its range of applications. Upcoming NASA satellite missions including PACE and GLIMR will feature imaging spectrometers capable of measuring hyperspectral remote-sensing reflectance (Rrs) across the visible range and well into the near-infrared and ultraviolet domains. The availability of UV reflectance is expected to facilitate the remote sensing of chromophoric dissolved organic matter (CDOM) in optically complex waters, thereby improving coastal water-quality monitoring. Although this argument is well supported by the dominance of CDOM absorption in the UV domain, few studies have directly evaluated the potential advantages conferred by UV reflectance for monitoring CDOM-related coastal water quality. Here, we took advantage of a 6-week wastewater diversion event in Santa Monica Bay, California in 2015 and the availability of Portable Remote Imaging SpectroMeter (PRISM) imagery acquired during the diversion to assess if UV-visible imaging spectroscopy could facilitate the detection of CDOM and help differentiate wastewater effluent-derived CDOM from other sources. A comparison of local empirical algorithms with varying amounts of spectral information implemented on PRISM data showed that incorporating UV Rrs as a predictor significantly improved retrieval of CDOM absorption coefficients (ag). Optimal performance was reached when combining Rrs(365), Rrs(400), and Rrs(700) as predictors of ag in a multiple linear regression. The use of the entire UV-visible spectrum (365–700 nm) in a partial-least-squares regression (PLSR) did not improve retrievals, indicating that a few carefully chosen predictors in the UV-visible domain were sufficient to empirically differentiate CDOM from phytoplankton in coastal waters minimally influenced by sediments or bottom reflectance. Finally, the development of a new fluorescence-based indicator of effluent-derived CDOM (effluent fluorescence ratio, EFR) helped demonstrate the feasibility of remotely detecting CDOM from wastewater. A PLSR-based algorithm using Rrs(365–700) provided reasonable EFR retrievals and successfully identified effluent-derived CDOM at the wastewater outfall when implemented on PRISM imagery. Although further work should investigate the influence of effluent-CDOM fluorescence on Rrs more mechanistically, these results confirmed that UV-visible imaging spectrometers can facilitate coastal CDOM-related water quality monitoring and expand its range of applications.

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

Sensors ◽

10.3390/s21124118 ◽

2021 ◽

Vol 21 (12) ◽

pp. 4118

Author(s):

Leonardo F. Arias-Rodriguez ◽

Zheng Duan ◽

José de Jesús Díaz-Torres ◽

Mónica Basilio Hazas ◽

Jingshui Huang ◽

...

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Extreme Learning Machine ◽

Monitoring System ◽

Field Measurements ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Chl A ◽

Learning Machine ◽

Level 2

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.

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A review of research on application of remote sensing in lake water quality monitoring

10.1117/12.2624857 ◽

2021 ◽

Author(s):

Wenrui Han

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Lake Water ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Lake Water Quality ◽

Research On Application

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Water-Quality Classification of Inland Lakes Using Landsat8 Images by Convolutional Neural Networks

Remote Sensing ◽

10.3390/rs11141674 ◽

2019 ◽

Vol 11 (14) ◽

pp. 1674 ◽

Cited By ~ 11

Author(s):

Fangling Pu ◽

Chujiang Ding ◽

Zeyi Chao ◽

Yue Yu ◽

Xin Xu

Keyword(s):

Remote Sensing ◽

Water Quality ◽

Water Quality Monitoring ◽

Quality Monitoring ◽

Quality Data ◽

Inland Lakes ◽

Quality Classification ◽

Water Quality Classification

Water-quality monitoring of inland lakes is essential for freshwater-resource protection. In situ water-quality measurements and ratings are accurate but high costs limit their usage. Water-quality monitoring using remote sensing has shown to be cost-effective. However, the nonoptically active parameters that mainly determine water-quality levels in China are difficult to estimate because of their weak optical characteristics and lack of explicit correlation between remote-sensing images and parameters. To address the problems, a convolutional neural network (CNN) with hierarchical structure was designed to represent the relationship between Landsat8 images and in situ water-quality levels. A transfer-learning strategy in the CNN model was introduced to deal with the lack of in situ measurement data. After the CNN model was trained by spatially and temporally matched Landsat8 images and in situ water-quality data that were collected from official websites, the surface quality of the whole water body could be classified. We tested the CNN model at the Erhai and Chaohu lakes in China, respectively. The experiment results demonstrate that the CNN model outperformed widely used machine-learning methods. The trained model at Erhai Lake can be used for the water-quality classification of Chaohu Lake. The introduced CNN model and the water-quality classification method could cover the whole lake with low costs. The proposed method has potential in inland-lake monitoring.

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