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Special Issue "Remote Sensing of Water Quality"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2017)

Special Issue Editors

Guest Editor
Dr. Wesley J. Moses

U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375, USA
Website | E-Mail
Interests: optical properties of water (open-ocean, coastal, and inland waters); sensor noise analysis; atmospheric correction; remote sensing of natural resources
Guest Editor
Dr. W. David Miller

U.S. Naval Research Laboratory, 4555 Overlook Ave SW, Washington, DC 20375, USA
E-Mail
Interests: airborne remote sensing; environmental forcing of phytoplankton dynamics; hyperspectral imaging; bio-optical modeling of optically complex waters

Special Issue Information

Dear Colleagues,

Recent advances in sensor technology and algorithm development enable the use of remote sensing to quantitatively study complex biophysical and biogeochemical processes in open-ocean, estuarine, coastal, and inland waters. However, realizing the operational potential of remote sensing for water quality monitoring has a number of challenges. A special issue on “Remote Sensing of Water Quality” has been dedicated in the journal Remote Sensing to address the current status, challenges, and future research priorities for remote sensing of water quality.

We are therefore inviting your new contribution in this exciting field to our Remote Sensing special issue on “Remote Sensing of Water Quality”.

Dr. Wesley J. Moses
Dr. W. David Miller
Guest Editors

Keywords

  • water quality
  • remote sensing
  • ocean color
  • bio-optical modeling
  • atmospheric correction
  • spatial variability
  • optical complexity
  • end-user engagement

Published Papers (9 papers)

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Research

Open AccessArticle Inherent Optical Properties of the Baltic Sea in Comparison to Other Seas and Oceans
Remote Sens. 2018, 10(3), 418; doi:10.3390/rs10030418
Received: 29 December 2017 / Revised: 2 March 2018 / Accepted: 4 March 2018 / Published: 8 March 2018
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Abstract
In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS
[...] Read more.
In order to retrieve geophysical satellite products in coastal waters with high coloured dissolved organic matter (CDOM), models and processors require parameterization with regional specific inherent optical properties (sIOPs). The sIOPs of the Baltic Sea were evaluated and compared to a global NOMAD/COLORS Reference Data Set (RDS), covering a wide range of optical provinces. Ternary plots of relative absorption at 442 nm showed CDOM dominance over phytoplankton and non-algal particle absorption (NAP). At 670 nm, the distribution of Baltic measurements was not different from case 1 waters and the retrieval of Chl a was shown to be improved by red-ratio algorithms. For correct retrieval of CDOM from Medium Resolution Imaging Spectrometer (MERIS) data, a different CDOM slope over the Baltic region is required. The CDOM absorption slope, SCDOM, was significantly higher in the northwestern Baltic Sea: 0.018 (±0.002) compared to 0.016 (±0.005) for the RDS. Chl a-specific absorption and ad [SPM]*(442) and its spectral slope did not differ significantly. The comparison to the MERIS Reference Model Document (RMD) showed that the SNAP slope was generally much higher (0.011 ± 0.003) than in the RMD (0.0072 ± 0.00108), and that the SPM scattering slope was also higher (0.547 ± 0.188) vs. 0.4. The SPM-specific scattering was much higher (1.016 ± 0.326 m2 g−1) vs. 0.578 m2 g−1 in RMD. SPM retrieval could be improved by applying the local specific scattering. A novel method was implemented to derive the phase function (PF) from AC9 and VSF-3 data. b ˜ was calculated fitting a Fournier–Forand PF to the normalized VSF data. b ˜ was similar to Petzold, but the PF differed in the backwards direction. Some of the sIOPs showed a bimodal distribution, indicating different water types—e.g., coastal vs. open sea. This seems to be partially caused by the distribution of inorganic particles that fall out relatively close to the coast. In order to improve remote sensing retrieval from Baltic Sea data, one should apply different parameterization to these distinct water types, i.e., inner coastal waters that are more influenced by scattering of inorganic particles vs. open sea waters that are optically dominated by CDOM absorption. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Water Quality Drivers in 11 Gulf of Mexico Estuaries
Remote Sens. 2018, 10(2), 255; doi:10.3390/rs10020255
Received: 12 December 2017 / Revised: 19 January 2018 / Accepted: 26 January 2018 / Published: 7 February 2018
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Abstract
Coastal water-quality is both a primary driver and also a consequence of coastal ecosystem health. Turbidity, a measure of dissolved and particulate water-quality matter, is a proxy for water quality, and varies on daily to interannual periods. Turbidity is influenced by a variety
[...] Read more.
Coastal water-quality is both a primary driver and also a consequence of coastal ecosystem health. Turbidity, a measure of dissolved and particulate water-quality matter, is a proxy for water quality, and varies on daily to interannual periods. Turbidity is influenced by a variety of factors, including algal particles, colored dissolved organic matter, and suspended sediments. Identifying which factors drive trends and extreme events in turbidity in an estuary helps environmental managers and decision makers plan for and mitigate against water-quality issues. Efforts to do so on large spatial scales have been hampered due to limitations of turbidity data, including coarse and irregular temporal resolution and poor spatial coverage. We addressed these issues by deriving a proxy for turbidity using ocean color satellite products for 11 Gulf of Mexico estuaries from 2000 to 2014 on weekly, monthly, seasonal, and annual time-steps. Drivers were identified using Akaike’s Information Criterion and multiple regressions to model turbidity against precipitation, wind speed, U and V wind vectors, river discharge, water level, and El Nino Southern Oscillation and North Atlantic Oscillation climate indices. Turbidity variability was best explained by wind speed across estuaries for both time-series and extreme turbidity events, although more dynamic patterns were found between estuaries over various time steps. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Retrieval of Water Constituents from Hyperspectral In-Situ Measurements under Variable Cloud Cover—A Case Study at Lake Stechlin (Germany)
Remote Sens. 2018, 10(2), 181; doi:10.3390/rs10020181
Received: 6 November 2017 / Revised: 17 January 2018 / Accepted: 19 January 2018 / Published: 26 January 2018
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Abstract
Remote sensing and field spectroscopy of natural waters is typically performed under clear skies, low wind speeds and low solar zenith angles. Such measurements can also be made, in principle, under clouds and mixed skies using airborne or in-situ measurements; however, variable illumination
[...] Read more.
Remote sensing and field spectroscopy of natural waters is typically performed under clear skies, low wind speeds and low solar zenith angles. Such measurements can also be made, in principle, under clouds and mixed skies using airborne or in-situ measurements; however, variable illumination conditions pose a challenge to data analysis. In the present case study, we evaluated the inversion of hyperspectral in-situ measurements for water constituent retrieval acquired under variable cloud cover. First, we studied the retrieval of Chlorophyll-a (Chl-a) concentration and colored dissolved organic matter (CDOM) absorption from in-water irradiance measurements. Then, we evaluated the errors in the retrievals of the concentration of total suspended matter (TSM), Chl-a and the absorption coefficient of CDOM from above-water reflectance measurements due to highly variable reflections at the water surface. In order to approximate cloud reflections, we extended a recent three-component surface reflectance model for cloudless atmospheres by a constant offset and compared different surface reflectance correction procedures. Our findings suggest that in-water irradiance measurements may be used for the analysis of absorbing compounds even under highly variable weather conditions. The extended surface reflectance model proved to contribute to the analysis of above-water reflectance measurements with respect to Chl-a and TSM. Results indicate the potential of this approach for all-weather monitoring. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Construction of Multi-Year Time-Series Profiles of Suspended Particulate Inorganic Matter Concentrations Using Machine Learning Approach
Remote Sens. 2017, 9(12), 1320; doi:10.3390/rs9121320
Received: 22 September 2017 / Revised: 1 December 2017 / Accepted: 8 December 2017 / Published: 15 December 2017
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Abstract
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along
[...] Read more.
Hydro-sedimentary numerical models have been widely employed to derive suspended particulate matter (SPM) concentrations in coastal and estuarine waters. These hydro-sedimentary models are computationally and technically expensive in nature. Here we have used a computationally less-expensive, well-established methodology of self-organizing maps (SOMs) along with a hidden Markov model (HMM) to derive profiles of suspended particulate inorganic matter (SPIM). The concept of the proposed work is to benefit from all available data sets through the use of fusion methods and machine learning approaches that are able to process a growing amount of available data. This approach is applied to two different data sets entitled “Hidden” and “Observable”. The hidden data are composed of 15 months (27 September 2007 to 30 December 2008) of hourly SPIM profiles extracted from the Regional Ocean Modeling System (ROMS). The observable data include forcing parameter variables such as significant wave heights ( H s and H s 50 (50 days)) from the Wavewatch 3-HOMERE database and barotropic currents ( U b a r and V b a r ) from the Iberian–Biscay–Irish (IBI) reanalysis data. These observable data integrate hourly surface samples from 1 February 2002 to 31 December 2012. The time-series profiles of the SPIM have been derived from four different stations in the English Channel by considering 15 months of output hidden data from the ROMS as a statistical representation of the ocean for ≈11 years. The derived SPIM profiles clearly show seasonal and tidal fluctuations in accordance with the parent numerical model output. The surface SPIM concentrations of the derived model have been validated with satellite remote sensing data. The time series of the modeled SPIM and satellite-derived SPIM show similar seasonal fluctuations. The ranges of concentrations for the four stations are also in good agreement with the corresponding satellite data. The high accuracy of the estimated 25 h average surface SPIM concentrations (normalized root-mean-square error— N R M S E of less than 16%) is the first step in demonstrating the robustness of the method. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Analysis of Suspended Particulate Matter and Its Drivers in Sahelian Ponds and Lakes by Remote Sensing (Landsat and MODIS): Gourma Region, Mali
Remote Sens. 2017, 9(12), 1272; doi:10.3390/rs9121272
Received: 1 September 2017 / Revised: 29 November 2017 / Accepted: 29 November 2017 / Published: 7 December 2017
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Abstract
The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental
[...] Read more.
The Sahelian region is characterized by significant variations in precipitation, impacting water quantity and quality. Suspended particulate matter (SPM) dynamics has a significant impact on inland water ecology and water resource management. In-situ data in this region are scarce and, consequently, the environmental factors triggering SPM variability are yet to be understood. This study addresses these issues using remote sensing optical data. Turbidity and SPM of the Agoufou Lake in Sahelian Mali were measured from October 2014 to present, providing a large range of `values (SPM ranging from 106 to 4178 mg/L). These data are compared to satellite reflectance from Landsat (ETM+, OLI) and MODIS (MOD09GQ, MYD09GQ). For each of these sensors, a spectral band in the near infrared region is found to be well suited to retrieve turbidity and SPM, up to very high values (R2 = 0.70) seldom addressed by remote sensing studies. The satellite estimates are then employed to assess the SPM dynamics in the main lakes and ponds of the Gourma region and its links to environmental and anthropogenic factors. The main SPM seasonal peak is observed in the rainy season (June to September) in relation to precipitation and sediment transport. A second important peak occurs during the dry season, highlighting the importance of resuspension mechanisms in maintaining high values of SPM. Three different periods are observed: first, a relatively low winds period in the early dry season, when SPM decreases rapidly due to deposition; then, a period of wind-driven resuspension in January‒March; and lastly, an SPM deposition period in April–May, when the monsoon replaces the winter trade wind. Overall, a significant increase of 27% in SPM values is observed between 2000 and 2016 in the Agoufou Lake. The significant spatio-temporal variability in SPM revealed by this study highlights the importance of high resolution optical sensors for continuous monitoring of water quality in these poorly instrumented regions. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Mapping Water Quality Parameters with Sentinel-3 Ocean and Land Colour Instrument imagery in the Baltic Sea
Remote Sens. 2017, 9(10), 1070; doi:10.3390/rs9101070
Received: 2 August 2017 / Revised: 22 September 2017 / Accepted: 12 October 2017 / Published: 20 October 2017
Cited by 1 | PDF Full-text (6046 KB) | HTML Full-text | XML Full-text
Abstract
The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI
[...] Read more.
The launch of Ocean and Land Colour Instrument (OLCI) on board Sentinel-3A in 2016 is the beginning of a new era in long time, continuous, high frequency water quality monitoring of coastal waters. Therefore, there is a strong need to validate the OLCI products to be sure that the technical capabilities provided will be used in the best possible way in water quality monitoring and research. The Baltic Sea is an optically complex waterbody where many ocean colour products, performing well in other waterbodies, fail. We tested the performance of standard Case-2 Regional/Coast Colour (C2RCC) processing chain in retrieving water reflectance, inherent optical properties (IOPs), and water quality parameters such as chlorophyll a, total suspended matter (TSM) and coloured dissolved organic matter (CDOM) in the Baltic Sea. The reflectance spectra produced by the C2RCC are realistic in both shape and magnitude. However, the IOPs, and consequently the water quality parameters estimated by the C2RCC, did not have correlation with in situ data. On the other hand, some tested empirical remote sensing algorithms performed well in retrieving chlorophyll a, TSM, CDOM and Secchi depth from the reflectance produced by the C2RCC. This suggests that the atmospheric correction part of the processor performs relatively well while IOP retrieval part of the neural network needs extensive training with actual IOP data before it can produce reasonable estimates for the Baltic Sea. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Evaluation of MODIS-Aqua Atmospheric Correction and Chlorophyll Products of Western North American Coastal Waters Based on 13 Years of Data
Remote Sens. 2017, 9(10), 1063; doi:10.3390/rs9101063
Received: 26 July 2017 / Revised: 9 September 2017 / Accepted: 8 October 2017 / Published: 19 October 2017
Cited by 1 | PDF Full-text (6214 KB) | HTML Full-text | XML Full-text
Abstract
There is an increasing need for satellite-derived accurate chlorophyll-a concentration (chla) products to improve fisheries management in coastal regions. However, the methods used to derive these products have to be evaluated, so the associated uncertainties are known. The performance of
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There is an increasing need for satellite-derived accurate chlorophyll-a concentration (chla) products to improve fisheries management in coastal regions. However, the methods used to derive these products have to be evaluated, so the associated uncertainties are known. The performance of three atmospheric correction methods, the near infrared (NIR), the shortwave infrared (SWIR), and the Management Unit of the North Seas Mathematical Models with an additional modification (MUMM + SWIR), and derived chla products based on the Moderate Resolution Imaging Spectroradiometer AQUA (MODIS) images acquired from 2002 to 2014 over the west coast of Canada and the United States were evaluated. The atmospherically corrected products and above-water reflectance were compared with in situ AERONET (N ~ 650) and above-water reflectance (N ~ 34) data, and the Ocean Color 3 MODIS (OC3M)-derived chla were compared with in situ chla measurements (N ~ 82). The statistical analysis indicated that the MUMM + SWIR method was the most appropriate for this region, with relatively good retrievals of the atmospheric products, improved retrieval of remote sensing reflectance with bias lower than 20% for the OC3M bands, and improved retrievals of chla (r = 0.83, slope = 0.89, logRMSE = 0.33 mg m−3 for ±1 h). The poorest chla retrievals were achieved with the SWIR and NIR methods. These results represent the most comprehensive satellite data analysis of MODIS retrievals for this region and provide a framework for the MUMM + SWIR method that can be further tested in other coastal regions of the world. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Effects of Small-Scale Gold Mining Tailings on the Underwater Light Field in the Tapajós River Basin, Brazilian Amazon
Remote Sens. 2017, 9(8), 861; doi:10.3390/rs9080861
Received: 15 June 2017 / Revised: 12 August 2017 / Accepted: 15 August 2017 / Published: 21 August 2017
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Abstract
Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers
[...] Read more.
Artisanal and Small-scale Gold Mining (ASGM) within the Amazon region has created several environmental impacts, such as mercury contamination and changes in water quality due to increased siltation. This paper describes the effects of water siltation on the underwater light environment of rivers under different levels of gold mining activities in the Tapajós River Basin. Furthermore, it investigates possible impacts on the phytoplankton community. Two field campaigns were conducted in the Tapajós River Basin, during high water level and during low water level seasons, to measure Inherent and Apparent Optical Properties (IOPs, AOPs), including scattering (b) and absorption (a) coefficients and biogeochemical data (sediment content, pigments, and phytoplankton quantification). The biogeochemical data was separated into five classes according to the concentration of total suspended solids (TSS) ranging from 1.8 mg·L−1 to 113.6 mg·L−1. The in-water light environment varied among those classes due to a wide range of concentrations of inorganic TSS originated from different levels of mining activities. For tributaries with low or no influence of mining tailings (TSS up to 6.8 mg·L−1), waters are relatively more absorbent with b:a ratio of 0.8 at 440 nm and b660 magnitude of 2.1 m−1. With increased TSS loadings from mining operations (TSS over 100 mg·L−1), the scattering process prevails over absorption (b:a ratio of 10.0 at 440 nm), and b660 increases to 20.8 m−1. Non-impacted tributaries presented a critical depth for phytoplankton productivity of up to 6.0 m with available light evenly distributed throughout the spectra. Whereas for greatly impacted waters, attenuation of light was faster, reducing the critical depth to about 1.7 m, with most of the available light comprising of red wavelengths. Overall, a dominance of diatoms was observed for the upstream rivers, whereas cyanobacteria prevailed in the low section of the Tapajós River. The results suggest that the spatial and temporal distribution of phytoplankton in the Tapajós River Basin is not only a function of light availability, but rather depends on the interplay of factors, including flood pulse, water velocity, nutrient availability, and seasonal variation of incoming irradiance. Ongoing research indicates that the effects of mining tailings on the aquatic environment, described here, are occurring in several rivers within the Amazon River Basin. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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Open AccessArticle Spatiotemporal Variation in Particulate Organic Carbon Based on Long-Term MODIS Observations in Taihu Lake, China
Remote Sens. 2017, 9(6), 624; doi:10.3390/rs9060624
Received: 8 May 2017 / Revised: 26 May 2017 / Accepted: 15 June 2017 / Published: 17 June 2017
Cited by 1 | PDF Full-text (7360 KB) | HTML Full-text | XML Full-text
Abstract
In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer
[...] Read more.
In situ measured values of particulate organic carbon (POC) in Taihu Lake and remote sensing reflectance observed by three satellite courses from 2014 to 2015 were used to develop an near infrared-red (NIR-Red) empirical algorithm of POC for the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) satellite image. The performance of the POC algorithm is highly consistent with the in situ measured POC, with root mean square error percentage (RMSPs) of 38.9% and 31.5% for two independent validations, respectively. The MODIS-derived POC also shows an acceptable result, with RMSPs of 53.6% and 61.0% for two periods of match-up data. POC from 2005 to 2007 is much higher than it is from 2002 to 2004 and 2008 to 2013, due to a large area of algal bloom. Riverine flux is an important source of POC in Taihu Lake, especially in the lake’s bank and bays. The influence of a terrigenous source of POC can reach the center lake during periods of heavy precipitation. Sediment resuspension is also a source of POC in the lake due to the area’s high dynamic ratio (25.4) and wind speed. The source of POC in an inland shallow lake is particularly complex, and additional research on POC is needed to more clearly reveal its variation in inland water. Full article
(This article belongs to the Special Issue Remote Sensing of Water Quality)
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