Special Issue "Novel Lake Water Quality Monitoring Strategies"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Aquatic Systems—Quality and Contamination".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editors

Dr. Sampsa Koponen
Website
Guest Editor
Finnish Environment Institute, Helsinki, Finland
Interests: Earth Observation; water quality; validation measurements; algorithm development; environmental monitoring
Dr. Saku Anttila
Website
Guest Editor
Finnish Environment Institute, Helsinki, Finland
Interests: Earth Observation; water quality; environmental monitoring; representative sampling

Special Issue Information

Dear Colleagues,

The Sentinel satellites of the European Union’s Copernicus programme provide new opportunities for monitoring aquatic environment. The current constellation includes two types of satellites with instrument suitable for lake monitoring:

  • The MultiSpectral Instrument (MSI) on board two Sentinel-2 satellites provide global coverage of in-land and coastal water bodies every 5 days with high resolution (10–60 m) data.
  • The Ocean and Land Colour Instrument (OLCI) on board two Sentinel-3 satellites will provide global coverage every two days with 300 m resolution data once the commissioning of Sentinel 3B is finished in late 2018.

Combined, these two missions provide tremendous amounts of freely available data about the environment every day. The Copernicus programme will continue to launch more satellites and guarantees the availability of high quality data far into the future. These long-term data streams allow new monitoring strategies to be developed.

There is a lot of heritage in the use of Earth Observation for aquatic monitoring. However, there is still need for better processing methods before the full potential of these new instruments can be reached. Furthermore, comparison with in situ data is needed in order to convince the user communities.

The development of earth observation applications are also strongly linked to the redesigning of water quality monitoring programs. EO’s role as one information source can vary depending on the monitored area and on the availability and properties of other data sources. There is a clear need for designing monitoring programs to balance the monitoring efforts and costs with sufficient and representative data collection.

Against this background we invite original research papers on the use of Earth Observation data (not limited to Sentinels) for monitoring inland waters.

Dr. Sampsa Koponen
Dr. Saku Anttila
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. Water 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 2000 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

  • Earth Observation
  • water quality
  • inland water bodies
  • environmental monitoring and reporting
  • validation, service production

Published Papers (3 papers)

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Research

Open AccessArticle
Temporal and Spatial Variations of Chlorophyll a Concentration and Eutrophication Assessment (1987–2018) of Donghu Lake in Wuhan Using Landsat Images
Water 2020, 12(8), 2192; https://doi.org/10.3390/w12082192 - 04 Aug 2020
Abstract
Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by [...] Read more.
Chlorophyll a (Chl-a) concentration, which reflects the biomass and primary productivity of phytoplankton in water, is an important water quality parameter to assess the eutrophication status of water. The band combinations shown in the images of Donghu Lake (Wuhan City, China) captured by Landsat satellites from 1987 to 2018 were analyzed. The (B4 − B3)/(B4 + B3) [(GreenRed)/(Green + Red)] band combination was employed to construct linear, power, exponential, logarithmic and cubic polynomial models based on Chl-a values in Donghu Lake in April 2016. The correlation coefficient (R2), the relative error (RE) and the root mean square error (RMSE) of the cubic model were 0.859, 9.175% and 11.194 μg/L, respectively and those of the validation model were 0.831, 6.509% and 19.846μg/L, respectively. Remote sensing images from 1987 to 2018 were applied to the model and the spatial distribution of Chl-a concentrations in spring and autumn of these years was obtained. At the same time, the eutrophication status of Donghu Lake was monitored and evaluated based on the comprehensive trophic level index (TLI). The results showed that the TLI (∑) of Donghu Lake in April 2016 was 63.49 and the historical data on Chl-a concentration showed that Donghu Lake had been eutrophic. The distribution of Chl-a concentration in Donghu Lake was affected by factors such as construction of bridges and dams, commercial activities and enclosure culture in the lake. The overall distribution of Chl-a concentration in each sub-lake was higher than that in the main lake region and Chl-a concentration was highest in summer, followed by spring, autumn and winter. Based on the data of three long-term (2005–2018) monitoring points in Donghu Lake, the matching patterns between meteorological data and Chl-a concentration were analyzed. It revealed that the Chl-a concentration was relatively high in warmer years or rainy years. The long-term measured data also verified the accuracy of the cubic model for Chl-a concentration. The R2, RE and RMSE of the validation model were 0.641, 2.518% and 22.606 μg/L, respectively, which indicated that it was feasible to use Landsat images to retrieve long-term Chl-a concentrations. Based on longitudinal remote sensing data from 1987 to 2018, long-term and large-scale dynamic monitoring of Chl-a concentrations in Donghu Lake was carried out in this study, providing reference and guidance for lake water quality management in the future. Full article
(This article belongs to the Special Issue Novel Lake Water Quality Monitoring Strategies)
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Open AccessArticle
The Use of Multisource Optical Sensors to Study Phytoplankton Spatio-Temporal Variation in a Shallow Turbid Lake
Water 2020, 12(1), 284; https://doi.org/10.3390/w12010284 - 18 Jan 2020
Cited by 6
Abstract
Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing [...] Read more.
Lake water quality monitoring has the potential to be improved through integrating detailed spatial information from new generation remote sensing satellites with high frequency observations from in situ optical sensors (WISPstation). We applied this approach for Lake Trasimeno with the aim of increasing knowledge of phytoplankton dynamics at different temporal and spatial scales. High frequency chlorophyll-a data from the WISPstation was modeled using non-parametric multiplicative regression. The ‘day of year’ was the most important factor, reflecting the seasonal progression of a phytoplankton bloom from July to September. In addition, weather factors such as the east–west wind component were also significant in predicting phytoplankton seasonal and diurnal patterns. Sentinel 3-OLCI and Sentinel 2-MSI satellites delivered 42 images in 2018 that successfully mapped the spatial and seasonal change in chlorophyll-a. The potential influence of localized inflows in contributing to increased chlorophyll-a in mid-summer was visualized. The satellite data also allowed an estimation of quality status at a much finer scale than traditional manual methods. Good correspondence was found with manually collected field data but more significantly, the greatly increased spatial and temporal resolution provided by satellite and WISPstation sensors clearly offers an unprecedented resource in the research and management of aquatic resources. Full article
(This article belongs to the Special Issue Novel Lake Water Quality Monitoring Strategies)
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Open AccessArticle
Remote Sensing of Water Quality Parameters over Lake Balaton by Using Sentinel-3 OLCI
Water 2018, 10(10), 1428; https://doi.org/10.3390/w10101428 - 11 Oct 2018
Cited by 19
Abstract
The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on [...] Read more.
The Ocean and Land Color Instrument (OLCI) onboard Sentinel 3A satellite was launched in February 2016. Level 2 (L2) products have been available for the public since July 2017. OLCI provides the possibility to monitor aquatic environments on 300 m spatial resolution on 9 spectral bands, which allows to retrieve detailed information about the water quality of various type of waters. It has only been a short time since L2 data became accessible, therefore validation of these products from different aquatic environments are required. In this work we study the possibility to use S3 OLCI L2 products to monitor an optically highly complex shallow lake. We test S3 OLCI-derived Chlorophyll-a (Chl-a), Colored Dissolved Organic Matter (CDOM) and Total Suspended Matter (TSM) for complex waters against in situ measurements over Lake Balaton in 2017. In addition, we tested the machine learning Gaussian process regression model, trained locally as a potential candidate to retrieve water quality parameters. We applied the automatic model selection algorithm to select the combination and number of spectral bands for the given water quality parameter to train the Gaussian Process Regression model. Lake Balaton represents different types of aquatic environments (eutrophic, mesotrophic and oligotrophic), hence being able to establish a model to monitor water quality by using S3 OLCI products might allow the generalization of the methodology. Full article
(This article belongs to the Special Issue Novel Lake Water Quality Monitoring Strategies)
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