Patterns and dynamics of coastal waters in multi-temporal satellite images: support to water quality monitoring in the Archipelago Sea, Finland

2004 ◽  
Vol 60 (2) ◽  
pp. 165-177 ◽  
Author(s):  
Anne Erkkilä ◽  
Risto Kalliola
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Satellite Images ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Archipelago Sea ◽  
Multi Temporal

scholarly journals Evaluation of Unoccupied Aircraft System (UAS) Remote Sensing Reflectance Retrievals for Water Quality Monitoring in Coastal Waters

2021 ◽  
Vol 9 ◽  
Author(s):  
Anna E. Windle ◽  
Greg M. Silsbe
Keyword(s):  
Water Quality ◽  
Coastal Waters ◽  
Total Suspended Solid ◽  
Water Quality Monitoring ◽  
Suspended Solid ◽  
Quality Monitoring ◽  
Future Research ◽  
Quality Properties ◽  
Water Constituents

Unoccupied aircraft systems (UAS, or drones) equipped with off-the-shelf multispectral sensors originally designed for terrestrial applications can also be used to derive water quality properties in coastal waters. The at-sensor total radiance a UAS measured constitutes the sum of water-leaving radiance (LW) and incident radiance reflected off the sea surface into the detector’s field of view (LSR). LW is radiance that emanates from the water and contains a spectral shape and magnitude governed by optically active water constituents interacting with downwelling irradiance while LSR is independent of water constituents and is instead governed by a given sea-state surface reflecting light; a familiar example is sun glint. Failure to accurately account for LSR can significantly influence Rrs, resulting in inaccurate water quality estimates once algorithms are applied. The objective of this paper is to evaluate the efficacy of methods that remove LSR from total UAS radiance measurements in order to derive more accurate remotely sensed retrievals of scientifically valuable in-water constituents. UAS derived radiometric measurements are evaluated against in situ hyperspectral Rrs measurements to determine the best performing method of estimating and removing surface reflected light and derived water quality estimates. It is recommended to use a pixel-based approach that exploits the high absorption of water at NIR wavelengths to estimate and remove LSR. Multiple linear regressions applied to UAS derived Rrs measurements and in situ chlorophyll a and total suspended solid concentrations resulted in 37 and 9% relative error, respectively, which is comparable to coastal water quality algorithms found in the literature. Future research could account for the high resolution and multi-angular aspect of LSR by using a combination of photogrammetry and radiometry techniques. Management implications from this research include improved water quality monitoring of coastal and inland water bodies in order to effectively track trends, identify and mitigate pollution sources, and discern potential human health risks.

scholarly journals MULTI-TEMPORAL MONITORING OF URBAN RIVER WATER QUALITY USING UAV-BORNE MULTI-SPECTRAL REMOTE SENSING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 1469-1475
Author(s):  
C. Liu ◽  
X. Zhou ◽  
Y. Zhou ◽  
A. Akbar
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Water Environment ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Ecological Environment ◽  
Monitoring Methods ◽  
Large Area ◽  
Monitoring Method ◽  
Multi Temporal

Abstract. Water quality is an important index of the ecological environment, which changes rapidly and needs to be monitored chronically. In urban ecological environment, water quality problem is not only more serious, but also more complex in time and space. Remote sensing water quality monitoring can cover a large area in a short time. Therefore, remote sensing can be adopted to make up for the shortcomings of traditional water quality monitoring methods in space coverage and temporal resolution. In order to monitor the narrow rivers in urban area, low altitude remote sensing is needed. This paper proposes a multi-spectral water quality monitoring method based on UAV platform, which can quickly monitor an entire urban water area and conduct multi-temporal observation for key indices of water quality within one day. It is helpful to find and locate the polluted areas which affect the water environment quickly. Also, it can show the changes of water quality on the time axis. The result can provide a decision-making basis for water environment treatment.

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

2021 ◽  
Vol 9 ◽  
Author(s):  
Joshua P. Harringmeyer ◽  
Karl Kaiser ◽  
David R. Thompson ◽  
Michelle M. Gierach ◽  
Curtis L. Cash ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Water Quality ◽  
Coastal Water ◽  
Coastal Waters ◽  
Imaging Spectroscopy ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Uv Reflectance ◽  
Visible Imaging ◽  
Uv Visible

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.

A Bioassay System for Water-Quality Monitoring Using a Small Fish

2013 ◽  
Vol 133 (8) ◽  
pp. 1616-1624
Author(s):  
Zu Soh ◽  
Kentaro Miyamoto ◽  
Akira Hirano ◽  
Toshio Tsuji
Keyword(s):  
Water Quality ◽  
Water Quality Monitoring ◽  
Quality Monitoring ◽  
Small Fish ◽  
Bioassay System

DEVELOPMENT OF A MULTIPARAMETRIC QUALITY INDEX FOR WATER QUALITY MONITORING

2016 ◽  
Vol 15 (5) ◽  
pp. 1069-1074 ◽  
Author(s):  
Violeta-Monica Radu ◽  
Alexandru Anton Ivanov ◽  
Petra Ionescu ◽  
Gyorgy Deak ◽  
Marian Tudor
Keyword(s):  
Water Quality ◽  
Quality Index ◽  
Water Quality Monitoring ◽  
Quality Monitoring
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