Submit to Remote Sensing Review for Remote Sensing Propose a Special Issue

Journal Browser

Estimating Inland Water Quality from Remote Sensing Data

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Published Papers

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 16282

Share This Special Issue

Special Issue Editor

Dr. Charles Verpoorter

E-Mail Website
Guest Editor

Laboratory of Oceanology and Geosciences Université du Littoral Côte d'Opale, Wimereux, 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 submissions that pass pre-check are 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 2700 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

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

Published Papers (4 papers)

Download All Papers

Research

16 pages, 3736 KiB

Open AccessArticle

Proof of Concept Study of an Electrochemical Sensor for Inland Water Monitoring with a Network Approach

by Anna Sabatini, Alessandro Zompanti, Simone Grasso, Luca Vollero, Giorgio Pennazza and Marco Santonico

Remote Sens. 2021, 13(20), 4026; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13204026 - 09 Oct 2021

Cited by 3 | Viewed by 1496

Abstract

The technologies most suitable for monitoring the ecosystem of inland waters are image spectrometry and electrochemical sensors. The reason is that these instruments are able to ensure accuracy in the surveillance of very large areas through reliable and frequent measurements performed remotely. Electrochemical systems provide low-cost, miniaturized, reliable sensors that can be organized, when equipped with commercial on the shelf (COTS) low-power radio components implementing LoRaWAN, Sigfox or NB-IoT communications, in a dense network of sensors achieving the aforementioned requirements. In this work, a low-cost, low-size and low-noise electrochemical sensor endowed with protocols for network configuration, management and monitoring is presented. The electronic interface of the sensor allows high reproducible responses. As proof of concept for its utilization in inland water monitoring, the device has been tested for water composition analysis, bacteria identification and frequent pollutant detection: atrazine, dichloromethane and tetrachloroethene. The results are promising, and future investigations will be oriented to unlock the true potential of a general-purpose approach exploiting the continuous fusion of distributed data in each of the three considered application scenarios. A new device, with reduced power consumption and size, has been also developed and tested; this new device should be a node of a large network for inland water monitoring. Full article

(This article belongs to the Special Issue Estimating Inland Water Quality from Remote Sensing Data)

► Show Figures

Figure 1

24 pages, 4447 KiB

Open AccessArticle

Remote Sensing of Turbidity in the Tennessee River Using Landsat 8 Satellite

by A. K. M. Azad Hossain, Caleb Mathias and Richard Blanton

Remote Sens. 2021, 13(18), 3785; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13183785 - 21 Sep 2021

Cited by 23 | Viewed by 5151

Abstract

The Tennessee River in the United States is one of the most ecologically distinct rivers in the world and serves as a great resource for local residents. However, it is also one of the most polluted rivers in the world, and a leading cause of this pollution is storm water runoff. Satellite remote sensing technology, which has been used successfully to study surface water quality parameters for many years, could be very useful to study and monitor the quality of water in the Tennessee River. This study developed a numerical turbidity estimation model for the Tennessee River and its tributaries in Southeast Tennessee using Landsat 8 satellite imagery coupled with near real-time in situ measurements. The obtained results suggest that a nonlinear regression-based numerical model can be developed using Band 4 (red) surface reflectance values of the Landsat 8 OLI sensor to estimate turbidity in these water bodies with the potential of high accuracy. The accuracy assessment of the estimated turbidity achieved a coefficient of determination (R²) value and root mean square error (RMSE) as high as 0.97 and 1.41 NTU, respectively. The model was also tested on imagery acquired on a different date to assess its potential for routine remote estimation of turbidity and produced encouraging results with R² value of 0.94 and relatively high RMSE. Full article

(This article belongs to the Special Issue Estimating Inland Water Quality from Remote Sensing Data)

► Show Figures

Figure 1

22 pages, 7989 KiB

Open AccessArticle

Patterns, Trends and Drivers of Water Transparency in Sri Lanka Using Landsat 8 Observations and Google Earth Engine

by Deepakrishna Somasundaram, Fangfang Zhang, Sisira Ediriweera, Shenglei Wang, Ziyao Yin, Junsheng Li and Bing Zhang

Remote Sens. 2021, 13(11), 2193; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13112193 - 04 Jun 2021

Cited by 8 | Viewed by 3135

Abstract

Addressing inland water transparency and driver effects to ensure the sustainability and provision of good quality water in Sri Lanka has been a timely prerequisite, especially under the Sustainable Development Goals 2030 agenda. Natural and anthropogenic changes lead to significant variations in water quality in the country. Therefore, an urgent need has emerged to understand the variability, spatiotemporal patterns, changing trends and impact of drivers on transparency, which are unclear to date. This study used all available Landsat 8 images from 2013 to 2020 and a quasi-analytical approach to assess the spatiotemporal Secchi disk depth (Z_SD) variability of 550 reservoirs and its relationship with natural (precipitation, wind and temperature) and anthropogenic (human activity and population density) drivers. Z_SD varied from 9.68 cm to 199.47 with an average of 64.71 cm and 93% of reservoirs had transparency below 100 cm. Overall, slightly increasing trends were shown in the annual mean Z_SD. Notable intra-annual variations were also indicating the highest and lowest Z_SD during the north-east monsoon and south-west monsoon, respectively. The highest Z_SD was found in wet zone reservoirs, while dry zone showed the least. All of the drivers were significantly affecting the water transparency in the entire island. The combined impact of natural factors on Z_SD changes was more significant (77.70%) than anthropogenic variables, whereas, specifically, human activity accounted for the highest variability across all climatic zones. The findings of this study provide the first comprehensive estimation of the Z_SD of entire reservoirs and driver contribution and also provides essential information for future sustainable water management and conservation strategies. Full article

(This article belongs to the Special Issue Estimating Inland Water Quality from Remote Sensing Data)

► Show Figures

Figure 1

19 pages, 5755 KiB

Open AccessArticle

Dynamic Inversion of Inland Aquaculture Water Quality Based on UAVs-WSN Spectral Analysis

by Linhui Wang, Xuejun Yue, Huihui Wang, Kangjie Ling, Yongxin Liu, Jian Wang, Jinbao Hong, Wen Pen and Houbing Song

Remote Sens. 2020, 12(3), 402; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12030402 - 26 Jan 2020

Cited by 29 | Viewed by 4580

Abstract

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)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Estimating Inland Water Quality from Remote Sensing Data

Share This Special Issue

Special Issue Editor

Special Issue Information

Keywords

Published Papers (4 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI