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Special Issue "Satellite Monitoring of Water Quality and Water Environment"

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

Deadline for manuscript submissions: 30 June 2019

Special Issue Editors

Guest Editor
Dr. Wei Yang

Center for Environmental Remote Sensing, Chiba University, 1-33, Yayoi-cho, Inage-ku, Chiba-shi, Chiba-ken, 263-8522, Japan
Website | E-Mail
Phone: +8143-290-2967
Interests: remote sensing of inland waters; remote sensing of vegetation; atmospheric correction; algoirthm development; environmental modeling; climate change
Guest Editor
Dr. Bunkei Matsushita

Faculty of Life and Environmental Science, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba, Ibaraki, 305-8572, Japan
Website | E-Mail
Phone: +8129-853-7190
Interests: remote sensing of case-2 waters; water quality; water environment; land cover/use changes; estimation of impervious surface area of watersheds
Guest Editor
Dr. Ronghua Ma

Nanjing Institute of Geography & Limnology, Chinese Academy of Sciences, No. 73, Beijingdong Road, Nanjing, 210-008, China
Website | E-Mail
Phone: +8625-8688-2168
Interests: remote sensing of inland lakes; water quality; water environment; aquatic ecology
Guest Editor
Dr. Steven Loiselle

University of Siena, Via Aldo Moro 2, 53100 Siena SI, Italy
Website | E-Mail
Interests: environmental spectroscopy; optical analysis and modelling of aquatic ecosystems; analysis of organic matter in aquatic ecosystems

Special Issue Information

Dear Colleagues,

Variety of water bodies plays important roles in human societies, in terms of providing multiple ecosystem services. However, in recent decades, they have encountered intensive pollution problems, all over the world. For example, the water quality of inland waters is strongly influenced by land use/cover changes (LUCC) in their corresponding watersheds. Understanding the interactions between water quality and their watersheds is, therefore, crucial for sustainable management of water resources.

Remote sensing is an important technique for monitoring water environments and watershed LUCC, being able to cover large spatial areas at frequent intervals. Mapping of regional LUCC, based on satellite observations, is well established. In contrast, satellite remote sensing of water quality, especially for inland and coastal water areas is still very challenging, due mainly to complex interactions among optically active substances, diversity of specific inherent optical properties, as well as difficulties in atmospheric correction above many inland and coastal waterbodies. The application of satellite monitoring to inland waters has been far less successful than those in the oceanic areas. In recent years, field surveys for inland waterbody remote sensing have increased. Meanwhile, the rapid development of mathematic techniques (e.g., machine learning) and cloud computation platforms (e.g., Google Earth Engine) provide new opportunities to improve the utility of satellite remote sensing for inland water monitoring. However, there is a clear need to share approaches and new ideas that can be used to expand and strengthen an integrated approach to catchment and waterbody management.

To meet this urgent need, a Special Issue on “Satellite Monitoring of Water Quality and Water Environment” is being planned by the international journal, Remote Sensing, to address the technical challenges for satellite monitoring of inland and coastal waters and demonstrate successful applications of remote sensing on the links between water quality/resource and watershed LUCC.

We solicit your contributions in this field to our Remote Sensing Special Issue. Research or review articles with respect to the following topics are welcome:

  • Remote estimation of water quality parameters
  • Remote estimation of water optical properties
  • Optical classification of specific inherent optical properties of world-wide inland and coastal waters
  • Application of machine learning algorithms to remote sensing of water environment
  • Application of Google Earth Engine to remote sensing of water environment
  • Validation and development of atmospheric correction algorithms for inland and coastal waters
  • Satellite mapping of macrophytes in inland waters
  • Satellite monitoring of inland water resources
  • Long-term satellite monitoring of watershed LUCC
  • Linkage between water qualities/resources and watershed LUCC
  • Environmental modeling of inland waters and its watershed based on application of remote sensing

Dr. Wei Yang
Dr. Bunkei Matsushita
Dr. Ronghua Ma
Dr. Steven Loiselle
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • water quality
  • water environment
  • watersheds
  • land use/land cover
  • remote sensing
  • atmospheric correction
  • algorithm development
  • environmental monitoring
  • machine learning
  • Google Earth Engine

Published Papers (8 papers)

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Research

Open AccessArticle
Aquarius Sea Surface Salinity Gridding Method Based on Dual Quality–Distance Weighting
Remote Sens. 2019, 11(9), 1131; https://doi.org/10.3390/rs11091131
Received: 9 April 2019 / Revised: 6 May 2019 / Accepted: 8 May 2019 / Published: 11 May 2019
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Abstract
A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data [...] Read more.
A new method for improving the accuracy of gridded sea surface salinity (SSS) fields is proposed in this paper. The method mainly focuses on dual quality–distance weighting of the Aquarius level 2 along-track SSS data according to quality flags, which represent nonnominal data conditions for measurements. In the weighting progress, 14 data conditions were considered, and their geospatial distributions and influences on the SSS were also visualized and evaluated. Three interpolation methods were employed, and weekly gridded SSS maps were produced for the period from September 2011 to May 2015. These maps were evaluated via comparisons with concurrent Argo buoy measurements. The results show that the proposed method improved the accuracy of the SSS fields by approximately 36% compared to the officially released weekly level 3 products and yielded root mean squared difference (RMSD), correlation and bias values of 0.19 psu, 0.98 and 0.01 psu, respectively. These findings indicate a significant improvement in the accuracy of the SSS fields and provide a better understanding of the influences of different conditions on salinity. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Retrieving Phytoplankton Size Class from the Absorption Coefficient and Chlorophyll A Concentration Based on Support Vector Machine
Remote Sens. 2019, 11(9), 1054; https://doi.org/10.3390/rs11091054
Received: 4 April 2019 / Revised: 1 May 2019 / Accepted: 1 May 2019 / Published: 4 May 2019
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Abstract
The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a [...] Read more.
The phytoplankton size class (PSC) plays an important role in biogeochemical processes in the ocean. In this study, a regional model of PSCs is proposed to retrieve vertical PSCs from the total minus water absorption coefficient (at-w(λ)) and Chlorophyll a concentration (Chla). The PSC model is developed by first reconstructing phytoplankton absorption and Chla from at-w(λ), and then extracting PSC from them using the support vector machine (SVM). In situ bio-optical data collected in the South China Sea from 2006 to 2013 were used to train the SVM. The proposed PSC model was subsequently validated using an independent PSC dataset from the Northeast South China Sea Cruise in 2015. The results indicate that the PSC model performed better than the three components model, with a value of r2 between 0.35 and 0.66, and the absolute percentage difference between 56% and 181%. On the whole, our PSC model shows a remarkable utility in terms of inferring vertical PSCs from the South China Sea. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Remote Sensing Estimation of Sea Surface Salinity from GOCI Measurements in the Southern Yellow Sea
Remote Sens. 2019, 11(7), 775; https://doi.org/10.3390/rs11070775
Received: 11 March 2019 / Revised: 26 March 2019 / Accepted: 27 March 2019 / Published: 31 March 2019
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Abstract
Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the [...] Read more.
Knowledge about the spatiotemporal distribution of sea surface salinity (SSS) provides valuable and important information for understanding various marine biogeochemical processes and ecosystems, especially for those coastal waters significantly affected by human activities. Remote-sensing techniques have been used to monitor salinity in the open ocean with their advantages of wide-area surveys and real-time monitoring. However, potential challenges remain when using satellite data with coarse spatiotemporal resolutions, leading to a loss of valuable information. In the current study, based on the local dataset collected over the southern Yellow Sea (SYS), a region-customized algorithm was developed to estimate SSS by using the remote sensing reflectance. The model evaluations indicated that our algorithm yielded good SSS estimation, with a root-mean-square error (RMSE) of 0.29 psu and a mean absolute percentage error (MAPE) of 0.75%. Satellite-derived SSS results compared well with those derived from in situ observations, further suggesting the good performance of our developed algorithm for the study regions. We applied this algorithm to Geostationary Ocean Color Imager (GOCI) data for the month of August from 2011 to 2018 in the SYS, and produced the spatial distribution patterns of the SSS for August of each year. The SSS values were high in offshore waters and lower in coastal waters, especially in the Yangtze River estuary. The negative correlation between the monthly Changjiang River discharge (CRD) and SSS (R = −0.71, p < 0.001) near the Yangtze River estuary was observed, suggesting that the SSS distribution in the Yangtze River estuary was potentially influenced by the CRD. In offshore waters, the correlation between SSS and CRD was weak (R < 0.2), suggesting that the riverine discharge’s effect might be weak. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Retrieving the Lake Trophic Level Index with Landsat-8 Image by Atmospheric Parameter and RBF: A Case Study of Lakes in Wuhan, China
Remote Sens. 2019, 11(4), 457; https://doi.org/10.3390/rs11040457
Received: 8 February 2019 / Revised: 17 February 2019 / Accepted: 19 February 2019 / Published: 22 February 2019
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Abstract
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor [...] Read more.
The importance of atmospheric correction is pronounced for retrieving physical parameters in aquatic systems. To improve the retrieval accuracy of trophic level index (TLI), we built eight models with 43 samples in Wuhan and proposed an improved method by taking atmospheric water vapor (AWV) information and Landsat-8 (L8) remote sensing image into the input layer of radical basis function (RBF) neural network. All image information taken in RBF have been radiometrically calibrated. Except model(a), image data used in the other seven models were not atmospherically corrected. The eight models have different inputs and the same output (TLI). The models are as follows: (1) model(a), the inputs are seven single bands; (2) model(c), besides seven single bands (b1, b2, b3, b4, b5, b6, b7), we added the AWV parameter k1 to the inputs; (3) model(c1), the inputs are AWV difference coefficient k2 and the seven bands; (4) model(c2), the input layers include seven single bands, k1 and k2; (5) model(b), seven band ratios (b3/b5, b1/b2, b3/b7, b2/b5, b2/b7, b3/b6, and b3/b4) were used as input parameters; (6) model(b1), the inputs are k1 and seven band ratios; (7) model(b2), the inputs are k2 and seven band ratios; (8) model(b3), the inputs are k1, k2, and seven band ratios. We estimated models with root mean squared error (RMSE), model(a) > model(b3) > model(b1) > model(c2) > model(c) > model(b) > model(c1) > model(b2). RMSE of the eight models are 12.762, 11.274, 10.577, 8.904, 8.361, 6.396, 5.389, and 5.104, respectively. Model b2 and c1 are two best models in these experiments, which confirms both the seven single bands and band ratios with k2 are superior to other models. Results also corroborate that most lakes in Wuhan urban area are in mesotrophic and light eutrophic states. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Optical Classification of the Remote Sensing Reflectance and Its Application in Deriving the Specific Phytoplankton Absorption in Optically Complex Lakes
Remote Sens. 2019, 11(2), 184; https://doi.org/10.3390/rs11020184
Received: 13 December 2018 / Revised: 14 January 2019 / Accepted: 16 January 2019 / Published: 18 January 2019
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Abstract
Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing [...] Read more.
Optical water types (OWTs) were identified from remote sensing reflectance (Rrs(λ)) values in a field-measured dataset of several large lakes in the lower reaches of the Yangtze and Huai River (LYHR) Basin. Four OWTs were determined from normalized remote sensing reflectance spectra (NRrs(λ)) using the k-means clustering approach, and were identified in the Sentinel 3A OLCI (Ocean Land Color Instrument) image data over lakes in the LYHR Basin. The results showed that 1) Each OWT is associated with different bio-optical properties, such as the concentration of chlorophyll-a (Chla), suspended particulate matter (SPM), proportion of suspended particulate inorganic matter (SPIM), and absorption coefficient of each component. One optical water type showed an obvious characteristic with a high contribution of mineral particles, while one type was mostly determined by a high content of phytoplankton. The other types belonged to the optically mixed water types. 2) Class-specific Chla inversion algorithms performed better for all water types, except type 4, compared to the overall dataset. In addition, class-specific inversion algorithms for estimating the Chla-specific absorption coefficient of phytoplankton at 443 nm (a*ph(443)) were developed based on the relationship between a*ph(443) and Chla of each OWT. The spatial variations in the class-specific model-derived a*ph(443) values were illustrated for 2 March 2017, and 24 October 2017. 3) The dominant water type and the Shannon index (H) were used to characterize the optical variability or similarity of the lakes in the LYHR Basin using cloud-free OLCI images in 2017. A high optical variation was located in the western and southern parts of Lake Taihu, the southern part of Lake Hongze, Lake Chaohu, and several small lakes near the Yangtze River, while the northern part of Lake Hongze had a low optical diversity. This work demonstrates the potential and necessity of optical classification in estimating bio-optical parameters using class-specific inversion algorithms and monitoring of the optical variations in optically complex and dynamic lake waters. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
The Assessment of Landsat-8 OLI Atmospheric Correction Algorithms for Inland Waters
Remote Sens. 2019, 11(2), 169; https://doi.org/10.3390/rs11020169
Received: 29 November 2018 / Revised: 12 January 2019 / Accepted: 15 January 2019 / Published: 17 January 2019
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Abstract
The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. [...] Read more.
The OLI (Operational Land Imager) sensor on Landsat-8 has the potential to meet the requirements of remote sensing of water color. However, the optical properties of inland waters are more complex than those of oceanic waters, and inland atmospheric correction presents additional challenges. We examined the performance of atmospheric correction (AC) methods for remote sensing over three highly turbid or hypereutrophic inland waters in China: Lake Hongze, Lake Chaohu, and Lake Taihu. Four water-AC algorithms (SWIR (Short Wave Infrared), EXP (Exponential Extrapolation), DSF (Dark Spectrum Fitting), and MUMM (Management Unit Mathematics Models)) and three land-AC algorithms (FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes), 6SV (a version of Second Simulation of the Satellite Signal in the Solar Spectrum), and QUAC (Quick Atmospheric Correction)) were assessed using Landsat-8 OLI data and concurrent in situ data. The results showed that the EXP (and DSF) together with 6SV algorithms provided the best estimates of the remote sensing reflectance (Rrs) and band ratios in water-AC algorithms and land-AC algorithms, respectively. AC algorithms showed a discriminating accuracy for different water types (turbid waters, in-water algae waters, and floating bloom waters). For turbid waters, EXP gave the best Rrs in visible bands. For the in-water algae and floating bloom waters, however, all water-algorithms failed due to an inappropriate aerosol model and non-zero reflectance at 1609 nm. The results of the study show the improvements that can be achieved considering SWIR bands and using band ratios, and the need for further development of AC algorithms for complex aquatic and atmospheric conditions, typical of inland waters. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Satellite Retrieval of Surface Water Nutrients in the Coastal Regions of the East China Sea
Remote Sens. 2018, 10(12), 1896; https://doi.org/10.3390/rs10121896
Received: 7 October 2018 / Revised: 22 November 2018 / Accepted: 23 November 2018 / Published: 27 November 2018
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Abstract
Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal [...] Read more.
Due to the tremendous flux of terrestrial nutrients from the Changjiang River, the waters in the coastal regions of the East China Sea (ECS) are exposed to heavy eutrophication. Satellite remote sensing was proven to be an ideal way of monitoring the spatiotemporal variability of these nutrients. In this study, satellite retrieval models for nitrate and phosphate concentrations in the coastal regions of the ECS are proposed using the back-propagation neural network (BP-NN). Both the satellite-retrieved sea surface salinity (SSS) and remote-sensing reflectance (Rrs) were used as inputs in our model. Compared with models that only use Rrs or SSS, the newly proposed model performs much better in the study area, with determination coefficients (R2) of 0.98 and 0.83, and mean relative error (MRE) values of 18.2% and 17.2% for nitrate and phosphate concentrations, respectively. Based on the proposed model and satellite-retrieved Rrs and SSS datasets, monthly time-series maps of nitrate and phosphate concentrations in the coastal regions of the ECS for 2015–2017 were retrieved for the first time. The results show that the distribution of nutrients had a significant seasonal variation. Phosphate concentrations in the ECS were lower in spring and summer than those in autumn and winter, which was mainly due to phytoplankton uptake and utilization. However, nitrate still spread far out into the ocean in summer because the diluted Changjiang River water remained rich in nitrogen. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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Open AccessArticle
Improved MODIS-Aqua Chlorophyll-a Retrievals in the Turbid Semi-Enclosed Ariake Bay, Japan
Remote Sens. 2018, 10(9), 1335; https://doi.org/10.3390/rs10091335
Received: 15 July 2018 / Revised: 10 August 2018 / Accepted: 19 August 2018 / Published: 21 August 2018
Cited by 1 | PDF Full-text (3999 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly [...] Read more.
The accurate retrieval of chlorophyll-a concentration (Chl-a) from ocean color satellite data is extremely challenging in turbid, optically complex coastal waters. Ariake Bay in Japan is a turbid semi-enclosed bay of great socio-economic significance, but it suffers from serious water quality problems, particularly due to red tide events. Chl-a derived from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on satellite Aqua in Ariake Bay was investigated, and it was determined that the causes of the errors were from inaccurate atmospheric correction and inappropriate in-water algorithms. To improve the accuracy of MODIS remote sensing reflectance (Rrs) in the blue and green bands, a simple method was adopted using in situ Rrs data. This method assumes that the error in MODIS Rrs(547) is small, and MODIS Rrs(412) can be estimated from MODIS Rrs(547) using a linear relation between in situ Rrs(412) and Rrs(547). We also showed that the standard MODIS Chl-a algorithm, OC3M, underestimated Chl-a, which was mostly due to water column turbidity. A new empirical switching algorithm was generated based on the relationship between in situ Chl-a and the blue-to-green band ratio, max(Rrs(443), Rrs(448)/Rrs(547), which was the same as the OC3M algorithm. The criterion of Rrs(667) of 0.005 sr−1 was used to evaluate the extent of turbidity for the switching algorithm. The results showed that the switching algorithm performed better than OC3M, and the root mean square error (RMSE) of estimated Chl-a decreased from 0.414 to 0.326. The RMSE for MODIS Chl-a using the recalculated Rrs and the switching algorithm was 0.287, which was a significant improvement from the RMSE of 0.610, which was obtained using standard MODIS Chl-a. Finally, the accuracy of our method was tested with an independent dataset collected by the local Fisheries Research Institute, and the results revealed that the switching algorithm with the recalculated Rrs reduced the RMSE of MODIS Chl-a from 0.412 of the standard to 0.335. Full article
(This article belongs to the Special Issue Satellite Monitoring of Water Quality and Water Environment)
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