Special Issue "Estimating Inland Water Quality from Remote Sensing Data"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: 30 June 2021.

Special Issue Editor

Dr. Charles Verpoorter
E-Mail Website
Guest Editor
Laboratory of Oceanology and Geosciences Université du Littoral Côte d'Opale, France
Interests: high and moderate spatial-resolution remote sensing; hyperspectral; color of water; inland water mapping; water quality indicators

Special Issue Information

Dear Colleagues,

We would like to invite you to submit original and innovate manuscripts to a Special Issue that specifically addresses “Estimating Inland Water Quality from Remote Sensing Data” in the journal of Remote Sensing.

Water quality is an extremely important environmental factor for ecosystem, human beings and their economic activities, as well as their health. Freshwater quality data and products are widely use to support water ressources management and timely decision making. However, these data are scarce at the global, regional and national levels, due to the lack of monitoring networks and capacity.

In recent decades, high and moderate resolution sensors on board satellite platforms (e.g., S2, L8, S3, MODIS) have allowed for remote sampling and monitoring of the inland water quality parameters at synoptic temporal and spatial scales, offering a cost-effective approach to studying changes in water quality trends, allowing, for instance, one to characterize the impact of sediment fluxes within a drainage basin, phytoplankton biomass, and lake trophic function, as well as the brownification of lakes. However, compared to groundbase platform, which allows one to measure physiochemical parameters (potential hydrogen ions pH, temperature, electric conductivity EC, salinity, total dissolved solids TDS, total suspended solid TSS, turbidity and total alkalinity), organic parameters (biochemical oxygen demand BOD, total organic carbon TOC, dissolved organic carbon DOC, total inorganic carbon TIC) and microbiological parameters (total colloform TC, cholorophyll chl-a), the range of water quality parameters that can be retrieved from satellite remotely sensed data is restricted to only a few optical properties of a water body, due to a limited number of multispectral bands, such as TSS, turbidity, algal pigments, and dissolved organic substances, supplemented with additional surface water temperature data. The coupling approach allowing for the integration of several remote sensing sensors (satellites, airbornes and drones), modelling products (hydrodynamic and biological models), as well as in situ measurements, remains a promising strategy for inland water monitoring at high temporal and spatial resolution. The new generation of geostationary sensors allows one to integrate water quality information at near real-time, by yielding new insight into temporality and monitoring water quality, and specifically cyanobacteria blooms, phenological changes, etc. Similarly, the use of drones equipped both with multispectral to hyperspectral, or even thermal instruments use, has proven to be useful for mapping small water bodies that are not resolvable at satellite spatial resolution, or harder to access, in conjunction with traditional sampling methods for developing different water quality indicators, with which strong correlations can be found.

In short, this Special Issue aims to collect recent developments, methodologies, and innovate applications of remote sensing for generating inland water quality indicators, and derived products, from different platforms (i.e., satellite, airborne and UAV-based remote sensing) and in situ measurements. Both applied and theoretical research contributions on inland water dealing with new algorithms and methodology developments are cordially solicited.

Submissions are encouraged to cover a broad range of topics, which may include:

  • Novate remote sensing techniques to assess inland water quality,
  • Large areas (global and regional) water quality parameters mapping in inland water,
  • Temporal variability (changes/trends/shifts) of water quality in inland waters,
  • Integration of multisource remote sensing for assessing water quality indicators,
  • Integration of hyperspectral imagery with ground-based datasets.
Dr. Charles Verpoorter
Guest Editor

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 2400 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.


  • Advanced optical algorithm
  • Inland water quality indicators
  • Inland water quality mapping
  • Hyperspectral imagery
  • Ground-based datasets
  • Optical remote sensing imagery

Published Papers (1 paper)

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Open AccessArticle
Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis
Remote Sens. 2020, 12(3), 402; https://doi.org/10.3390/rs12030402 - 26 Jan 2020
Viewed by 1077
The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and [...] Read more.
The inland aquaculture environment is an artificial ecosystem, where the water quality is a key factor which is closely related to the economic benefits of inland aquaculture and the quality of aquatic products. Compared with marine aquaculture, inland aquaculture is normally smaller and susceptible to pollution, with poor self-purification capacity. Considering its low cost and large-scale monitoring ability, many researches have developed spectrum sensor on-board satellite platforms to allow remote monitoring of inland water surface. However, there remain many problems, such as low image resolution, poor flexible data acquisition, and anti-interference. Apart from that, the conventional forecasting model is of weak generalization ability and low accuracy. In our study, we combine unmanned aerial vehicles system (UAVs) with the wireless sensor network (WSN) to design a new ground water quality parameter and drone spectrum information acquisition approach, and to propose a novel dynamic network surgery-deep neural networks (DNS-DNNs) model based on multi-source feature fusion to forecast the distribution of dissolved oxygen (DO) and turbidity (TUB) in inland aquaculture areas. The result of using fused features, including characteristic spectrum, Gray-level co-occurrence matrix (GLCM) texture feature, and convolutional neural network (CNN) texture feature to build a model is that the characteristic spectrum+ CNN texture fusion features were the best input items for DNS-DNNs when forecasting DO, with the determination coefficient R 2 of the vertical set arriving at 0.8741, while the characteristic spectrum+ GLCM texture+ CNN texture fusion features were the best for TUB, with the R 2 reaching 0.8531. Compared with a variety of conventional models, our model had a better performance in the inversion of DO and TUB, and there was a strong correlation between predicted and real values: R 2 reached 0.8042 and 0.8346, whereas the root mean square error (RMSE) were only 0.1907 and 0.1794, separately. Our study provides a new insight about using remote sensing to rapidly monitor water quality in inland aquaculture regions. Full article
(This article belongs to the Special Issue Estimating Inland Water Quality from Remote Sensing Data)
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