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Remote Sensing
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Earth Observation Technologies for Monitoring of Water Environments
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Earth Observation Technologies for Monitoring of Water Environments

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

Deadline for manuscript submissions: closed (28 February 2022) | Viewed by 23523

Special Issue Editors

Dr. Giuseppe Ruello

E-Mail Website
Guest Editor
Department of Electrical Engineering and Information Technology, University of Naples Federico II, Naples, Italy
Interests: remote sensing; synthetic aperture radar (SAR); earth observation; satellite image processing and analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Donato Amitrano

E-Mail Website
Guest Editor
Surrey Space Centre, University of Surrey, Guildford GU2 7XH, UK
Interests: remote sensing; SAR; Earth observation; electromagnetic scattering, computer vision; machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water covers a large part of our planet and is essential for human life. Water woes, strictly linked with food security, climate change, and the most important challenges for humans, affect millions of people all over the world. Earth observation is an essential resource to provide crucial information for an integrated water resource management.

With this Special Issue, we intend to bring out the most recent Earth Observation technologies for monitoring oceans, wetlands, rivers, lakes, and coastal environments.

In particular, we solicit contributions describing:

  • Innovative methodologies for detecting water bodies with synthetic aperture radar and/or multispectral sensors;
  • Models and simulations of the electromagnetic scattering from the water surface, also in the presence of surface active materials;
  • Applications of Earth observation techniques for monitoring water scarcity, water quality, and water pollution;
  • Applications of remote sensing to water resource management;
  • Remote sensing data assimilation within hydrological models.

Review papers are also welcome.

Dr. Giuseppe Ruello
Dr. Donato Amitrano
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 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

  • Earth observation
  • Synthetic aperture radar
  • Multispectral
  • Marine environment
  • Wetlands
  • Internal water monitoring
  • Water quality
  • Water scarcity
  • Water management
  • Oil slick detection
  • Flooding

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

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31 pages, 9667 KiB  
Article
The Use of Sentinel-3/OLCI for Monitoring the Water Quality and Optical Water Types in the Largest Portuguese Reservoir
by Gonçalo Rodrigues, Miguel Potes, Alexandra Marchã Penha, Maria João Costa and Maria Manuela Morais
Remote Sens. 2022, 14(9), 2172; https://doi.org/10.3390/rs14092172 - 30 Apr 2022
Cited by 15 | Viewed by 4627
Abstract
The Alqueva reservoir is essential for water supply in the Alentejo region (south of Portugal). Satellite data are essential to overcome the temporal and spatial limitations of in situ measurements, ensuring continuous and global water quality monitoring. Data between 2017 and 2020, obtained [...] Read more.
The Alqueva reservoir is essential for water supply in the Alentejo region (south of Portugal). Satellite data are essential to overcome the temporal and spatial limitations of in situ measurements, ensuring continuous and global water quality monitoring. Data between 2017 and 2020, obtained from OLCI (Ocean and Land Color Instrument) aboard Sentinel-3, were explored. Two different methods were used to assess the water quality in the reservoir: K-means to group reflectance spectra into different optical water types (OWT), and empirical algorithms to estimate water quality parameters. Spatial (in five different areas in the reservoir) and temporal (monthly) variations of OWT and water quality parameters were analyzed, namely, Secchi depth, water turbidity, chlorophyll a, and phycocyanin concentrations. One cluster has been identified representing the typical spectra of the presence of microalgae in the reservoir, mainly between July and October and more intense in the northern region of the Alqueva reservoir. An OWT type representing the area of the reservoir with the highest transparency and lowest chlorophyll a concentration was defined. The methodology proposed is suitable to continuously monitor the water quality of Alqueva reservoir, constituting a useful contribution to a potential early warning system for identification of critical areas corresponding to cyanobacterial algae blooms. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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22 pages, 6111 KiB  
Article
Small Water Body Detection and Water Quality Variations with Changing Human Activity Intensity in Wuhan
by Lingjun Wang, Wanjuan Bie, Haocheng Li, Tanghong Liao, Xingxing Ding, Guofeng Wu and Teng Fei
Remote Sens. 2022, 14(1), 200; https://doi.org/10.3390/rs14010200 - 2 Jan 2022
Cited by 8 | Viewed by 3871
Abstract
Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is [...] Read more.
Small water bodies ranging in size from 1 to 50,000 m2, are numerous, widely distributed, and have various functions in water storage, agriculture, and fisheries. Small water bodies used for agriculture and fisheries are economically significant in China, hence it is important to properly identify and analyze them. In remote sensing technology, water body identification based on band analysis, image classification, and water indices are often designed for large, homogenous water bodies. Traditional water indices are often less accurate for small water bodies, which often contain submerged or floating plants or easily confused with hill shade. Water quality inversion commonly depends on establishing the relationship between the concentration of water constituents and the observed spectral reflectance. However, individual variation in water quality in small water bodies is enormous and often far beyond the range of existing water quality inversion models. In this study, we propose a method for small water body identification and water quality estimation and test its applicability in Wuhan. The kappa coefficient of small water body identification is over 0.95, and the coefficient of determination of the water quality inversion model is over 0.9. Our results show that the method proposed in this study can be employed to accurately monitor the dynamics of small water bodies. Due to the outbreak of the COVID-19 pandemic, the intensity of human activities decreased. As a response, significant changes in the water quality of small water bodies were observed. The results also suggest that the water quality of small water bodies under different production modes (intensive/casual) respond differently in spatial and temporal dimensions to the decrease in human activities. These results illustrate that effective remote sensing monitoring of small water bodies can provide valuable information on water quality. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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24 pages, 11717 KiB  
Article
Assessing a Prototype Database for Comprehensive Global Aquatic Land Cover Mapping
by Panpan Xu, Nandin-Erdene Tsendbazar, Martin Herold and Jan G. P. W. Clevers
Remote Sens. 2021, 13(19), 4012; https://doi.org/10.3390/rs13194012 - 7 Oct 2021
Cited by 2 | Viewed by 2409
Abstract
The monitoring of Global Aquatic Land Cover (GALC) plays an essential role in protecting and restoring water-related ecosystems. Although many GALC datasets have been created before, a uniform and comprehensive GALC dataset is lacking to meet multiple user needs. This study aims to [...] Read more.
The monitoring of Global Aquatic Land Cover (GALC) plays an essential role in protecting and restoring water-related ecosystems. Although many GALC datasets have been created before, a uniform and comprehensive GALC dataset is lacking to meet multiple user needs. This study aims to assess the effectiveness of using existing global datasets to develop a comprehensive and user-oriented GALC database and identify the gaps of current datasets in GALC mapping. Eight global datasets were reframed to construct a three-level (i.e., from general to detailed) prototype database for 2015, conforming with the United Nations Land Cover Classification System (LCCS)-based GALC characterization framework. An independent validation was done, and the overall results show some limitations of current datasets in comprehensive GALC mapping. The Level-1 map had considerable commission errors in delineating the general GALC distribution. The Level-2 maps were good at characterizing permanently flooded areas and natural aquatic types, while accuracies were poor in the mapping of temporarily flooded and waterlogged areas as well as artificial aquatic types; vegetated aquatic areas were also underestimated. The Level-3 maps were not sufficient in characterizing the detailed life form types (e.g., trees, shrubs) for aquatic land cover. However, the prototype GALC database is flexible to derive user-specific maps and has important values to aquatic ecosystem management. With the evolving earth observation opportunities, limitations in the current GALC characterization can be addressed in the future. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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28 pages, 14436 KiB  
Article
Continuous Monitoring of the Flooding Dynamics in the Albufera Wetland (Spain) by Landsat-8 and Sentinel-2 Datasets
by Carmela Cavallo, Maria Nicolina Papa, Massimiliano Gargiulo, Guillermo Palau-Salvador, Paolo Vezza and Giuseppe Ruello
Remote Sens. 2021, 13(17), 3525; https://doi.org/10.3390/rs13173525 - 5 Sep 2021
Cited by 32 | Viewed by 3871
Abstract
Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. [...] Read more.
Satellite data are very useful for the continuous monitoring of ever-changing environments, such as wetlands. In this study, we investigated the use of multispectral imagery to monitor the winter evolution of land cover in the Albufera wetland (Spain), using Landsat-8 and Sentinel-2 datasets. With multispectral data, the frequency of observation is limited by the possible presence of clouds. To overcome this problem, the data acquired by the two missions, Landsat-8 and Sentinel-2, were jointly used, thus roughly halving the revisit time. The varied types of land cover were grouped into four classes: (1) open water, (2) mosaic of water, mud and vegetation, (3) bare soil and (4) vegetated soil. The automatic classification of the four classes was obtained through a rule-based method that combined the NDWI, MNDWI and NDVI indices. Point information, provided by geo-located ground pictures, was spatially extended with the help of a very high-resolution image (GeoEye-1). In this way, surfaces with known land cover were obtained and used for the validation of the classification method. The overall accuracy was found to be 0.96 and 0.98 for Landsat-8 and Sentinel-2, respectively. The consistency evaluation between Landsat-8 and Sentinel-2 was performed in six days, in which acquisitions by both missions were available. The observed dynamics of the land cover were highly variable in space. For example, the presence of the open water condition lasted for around 60–80 days in the areas closest to the Albufera lake and progressively decreased towards the boundaries of the park. The study demonstrates the feasibility of using moderate-resolution multispectral images to monitor land cover changes in wetland environments. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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Review

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24 pages, 2425 KiB  
Review
Water-Body Segmentation for SAR Images: Past, Current, and Future
by Zhishun Guo, Lin Wu, Yabo Huang, Zhengwei Guo, Jianhui Zhao and Ning Li
Remote Sens. 2022, 14(7), 1752; https://doi.org/10.3390/rs14071752 - 6 Apr 2022
Cited by 41 | Viewed by 7617
Abstract
Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 [...] Read more.
Synthetic Aperture Radar (SAR), as a microwave sensor that can sense a target all day or night under all-weather conditions, is of great significance for detecting water resources, such as coastlines, lakes and rivers. This paper reviews literature published in the past 30 years in the field of water body extraction in SAR images, and makes some proposals that the community working with SAR image waterbody extraction should consider. Firstly, this review focuses on the main ideas and characteristics of traditional water body extraction on SAR images, mainly focusing on traditional Machine Learning (ML) methods. Secondly, how Deep Learning (DL) methods are applied and optimized in the task of water-body segmentation for SAR images is summarized from the two levels of pixel and image. We also pay more attention to the most popular networks, such as U-Net and its modified models, and novel networks, such as the Cascaded Fully-Convolutional Network (CFCN) and River-Net. In the end, an in-depth discussion is presented, along with conclusions and future trends, on the limitations and challenges of DL for water-body segmentation. Full article
(This article belongs to the Special Issue Earth Observation Technologies for Monitoring of Water Environments)
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