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Advances of Remote Sensing and GIS Technology in Surface Water Bodies

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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 2024 | Viewed by 14022

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Special Issue Editors

Dr. Andrew Ogilvie

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Guest Editor

UMR G-EAU, Institut de Recherche pour le Développement (IRD), CEDEX 5, 34196 Montpellier, France
Interests: spatial hydrology; water resources; data assimilation; long-term monitoring; wetlands; large rivers; UAV

Dr. Frédéric Frappart

E-Mail Website
Guest Editor

Interactions Sol Plante Atmosphère, UMR 1391 INRAE/Bordeaux Science Agro 71, Avenue Edouard Bourlaux, 33882 Villenave d'Ornon, France
Interests: remote sensing; water cycle; carbon cycle; wetlands
Special Issues, Collections and Topics in MDPI journals

Dr. Lisa Maria Rebelo

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Guest Editor

International Water Management Institute, 127 Sunil Mawatha,Pelawatte, Battaramulla, Colombo, Sri Lanka
Interests: remote sensing for basin scale hydrology, water availability and allocation; management of water resources at multiple scales; basin scale water accounting; wetland inventory, monitoring and assessment
Special Issues, Collections and Topics in MDPI journals

Dr. Raphael M. Tshimanga

E-Mail Website
Guest Editor

Congo Basin Water Resources Research Center, University of Kinshasa, Kinshasa B.P. 190, Congo
Interests: hydrological modelling; prediction in ungauged catchment; climate change vulnerability and adaptation in water sector; acoustic Doppler technology and large river investigation; catchment classification, water resources management

Special Issue Information

Dear Colleagues,

The rising spatial and temporal resolution of earth observations provide unprecedented opportunities for monitoring and understanding surface water bodies. Across large wetlands and multiple dispersed water bodies, remote sensing provides rising opportunities to monitor surface water variations, which can be difficult to capture by localised hydrological monitoring or modelling. Sentinel imagery notably provided a significant leap in the understanding of surface water bodies, and the arrival of SWOT planned in 2022 will provide enhanced possibilities.

Despite these opportunities and the numerous global datasets developed from earth observations, research has also shown the limitations of these works in specific contexts. Many methods are indeed calibrated and designed to classify open water bodies, leading to omission errors on smaller water bodies, floodplains and wetlands containing large areas with flooded vegetation. In mixed water environments such as floodplains, which concentrate meanders and shallow water basins and where temporary flood patterns require high image availability, novel approaches to build upon the multiple sources of imagery are necessary. Optimal approaches to characterise long-term surface water dynamics across multiple locations must also seek to combine or fuse the observations from multiple sensors, and exploit the opportunities provided by large scale computing geoprocessing capacities. Furthermore, these approaches must be combined and confronted with ground hydrological data to increase the understanding of complex hydrological and socio-hydrological systems.

This special issue welcomes original contributions providing novel insights to advance remote sensing of surface water bodies. Topics of interest include among others:

surface water classification methods and accuracy considerations;
combining and fusing earth observations from multiple sensors (radar/optical);
increased understanding of the hydrology of selected water bodies from earth observations;
combining and confronting earth observations with hydrological data and modelling.

Dr. Andrew Ogilvie
Dr. Frédéric Frappart
Dr. Lisa-Maria Rebelo
Dr. Raphael Tshimanga
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

remote sensing
hydrology
surface water bodies
lakes
wetlands
classification accuracy
long-term monitoring

Published Papers (7 papers)

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Research

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22 pages, 3980 KiB

Open AccessArticle

A Geographic Object-Based Image Approach Based on the Sentinel-2 Multispectral Instrument for Lake Aquatic Vegetation Mapping: A Complementary Tool to In Situ Monitoring

by Maria Tompoulidou, Elpida Karadimou, Antonis Apostolakis and Vasiliki Tsiaoussi

Remote Sens. 2024, 16(5), 916; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050916 - 05 Mar 2024

Viewed by 945

Abstract

Aquatic vegetation is an essential component of lake ecosystems, used as a biological indicator for in situ monitoring within the Water Framework Directive. We developed a hierarchical object-based image classification model with multi-seasonal Sentinel-2 imagery and suitable spectral indices in order to map the aquatic vegetation in a Mediterranean oligotrophic/mesotrophic deep lake; we then applied the model to another lake with similar abiotic and biotic characteristics. Field data from a survey of aquatic macrophytes, undertaken on the same dates as EO data, were used within the accuracy assessment. The aquatic vegetation was discerned into three classes: emergent, floating, and submerged aquatic vegetation. Geographic object-based image analysis (GEOBIA) proved to be effective in discriminating the three classes in both study areas. Results showed high effectiveness of the classification model in terms of overall accuracy, particularly for the emergent and floating classes. In the case of submerged aquatic vegetation, challenges in their classification prompted us to establish specific criteria for their accurate detection. Overall results showed that GEOBIA based on spectral indices was suitable for mapping aquatic vegetation in oligotrophic/mesotrophic deep lakes. EO data can contribute to large-scale coverage and high-frequency monitoring requirements, being a complementary tool to in situ monitoring. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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22 pages, 20796 KiB

Open AccessArticle

Monitoring Spatial–Temporal Variations in River Width in the Aral Sea Basin with Sentinel-2 Imagery

by Jingjing Zhou, Linghong Ke, Xin Ding, Ruizhe Wang and Fanxuan Zeng

Remote Sens. 2024, 16(5), 822; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16050822 - 27 Feb 2024

Viewed by 599

Abstract

Rivers in arid regions serve as crucial freshwater resources for local communities and play an essential role in global hydrological and biogeochemical cycles. The Aral Sea Basin (ASB) in Central Asia is characterized by an arid climate and river dynamics that are sensitive to climate change and human activities. Monitoring the spatiotemporal variations in river water extent in the ASB is essential to maintain an ecological balance and ensure water security. In this study, we extracted data regarding monthly river water bodies in the ASB from 2017 to 2022 by synthesizing monthly Sentinel-2 images. The water extents on the Sentinel images were automatically mapped using the Otsu method, and the river widths for all river channels were calculated using the RivWidth algorithm. We investigated the relationships between the river dynamics and the geomorphology, climatic change, human activities, and the annual and interannual variations in the river width in different reaches of the basin. The results show a seasonal variability in the river width, with most rivers reaching the largest width in the warm season and a few rivers in the middle and lower areas reaching the valley value in the warm season. Compared to their tributaries, the mainstem in the middle/lower regions showed less seasonal variability. According to interannual analysis, most of the rivers in the ASB significantly narrowed between 2017 and 2022, a phenomenon which is generally impacted by temperature and evapotranspiration variations. Comparisons show that our results provide improved information about the narrow river reaches and denser river networks compared to the previous global dataset, demonstrating the advantageous properties of high spatial resolution in Sentinel-2 imagery. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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22 pages, 14248 KiB

Open AccessArticle

Synergistic Integration of Time Series Optical and SAR Satellite Data for Mariculture Extraction

by Shuxuan Wang, Chong Huang, He Li and Qingsheng Liu

Remote Sens. 2023, 15(9), 2243; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092243 - 23 Apr 2023

Cited by 3 | Viewed by 1778

Abstract

Mariculture is an important part of aquaculture, and it is important to address global food security and nutrition issues. However, seawater environmental conditions are complex and variable, which causes large uncertainties in the remote sensing spectral features. At the same time, mariculture types are distinct because of the different types of aquaculture (cage aquaculture and raft aquaculture). These factors bring great challenges for mariculture extraction and mapping using remote sensing. In order to solve these problems, an optical remote sensing aquaculture index named the marine aquaculture index (MAI) is proposed. Based on this spectral index, using time series Sentinel-1 and Sentinel-2 satellite data, a random forest classification scheme is proposed for mapping mariculture by combining spectral, textural, geometric, and synthetic aperture radar (SAR) backscattering. The results revealed that (1) MAI can emphasize the difference between mariculture and seawater; (2) the overall accuracy of mariculture in the Bohai Rim is 94.10%, and the kappa coefficient is 0.91; and (3) the area of cage aquaculture and raft aquaculture in the Bohai Rim is 16.89 km² and 1206.71 km², respectively. This study details an effective method for carrying out mariculture monitoring and ensuring the sustainable development of aquaculture. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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30 pages, 24057 KiB

Open AccessArticle

RadWet: An Improved and Transferable Mapping of Open Water and Inundated Vegetation Using Sentinel-1

by Gregory Oakes, Andy Hardy and Pete Bunting

Remote Sens. 2023, 15(6), 1705; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061705 - 22 Mar 2023

Cited by 2 | Viewed by 2162

Abstract

Mapping the spatial and temporal dynamics of tropical herbaceous wetlands is vital for a wide range of applications. Inundated vegetation can account for over three-quarters of the total inundated area, yet widely used EO mapping approaches are limited to the detection of open water bodies. This paper presents a new wetland mapping approach, RadWet, that automatically defines open water and inundated vegetation training data using a novel mixture of radar, terrain, and optical imagery. Training data samples are then used to classify serial Sentinel-1 radar imagery using an ensemble machine learning classification routine, providing information on the spatial and temporal dynamics of inundation every 12 days at a resolution of 30 m. The approach was evaluated over the period 2017–2022, covering a range of conditions (dry season to wet season) for two sites: (1) the Barotseland Floodplain, Zambia (31,172 km²) and (2) the Upper Rupununi Wetlands in Guyana (11,745 km²). Good agreement was found at both sites using random stratified accuracy assessment data (n = 28,223) with a median overall accuracy of 89% in Barotseland and 80% in the Upper Rupununi, outperforming existing approaches. The results revealed fine-scale hydrological processes driving inundation patterns as well as temporal patterns in seasonal flood pulse timing and magnitude. Inundated vegetation dominated wet season wetland extent, accounting for a mean 80% of total inundation. RadWet offers a new way in which tropical wetlands can be routinely monitored and characterised. This can provide significant benefits for a range of application areas, including flood hazard management, wetland inventories, monitoring natural greenhouse gas emissions and disease vector control. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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17 pages, 3224 KiB

Open AccessArticle

Estimation of the Key Water Quality Parameters in the Surface Water, Middle of Northeast China, Based on Gaussian Process Regression

by Xingpeng Liu, Bazel Al-Shaibah, Chunli Zhao, Zhijun Tong, Hongfeng Bian, Feng Zhang, Jiquan Zhang and Xiangjun Pei

Remote Sens. 2022, 14(24), 6323; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14246323 - 13 Dec 2022

Cited by 2 | Viewed by 1564

Abstract

To estimate the key water quality parameters on a large scale, based on Pearson’s correlation analysis and band ratio, this study first obtains multiple sensitive band combinations (R ≥ 0.30, p < 0.01) for three key water quality parameters: dissolved oxygen (DO), total nitrogen (TN), and total phosphorus (TP). Then, principal component analysis is used to reduce the dimensions and analyze multiple optimal combinations, and the first three principal components (PCs) of the optimal combinations are selected to analyze the water quality parameters. Finally, the water quality parameter models of DO, TN, and TP are proposed and compared based on spectral analysis and field measured water quality data respectively using Gaussian process regression and PCs for each parameter. Through model verification and by comparing the performance of the three models, it is found that the TP model performed well (R = 0.9824, p < 0.01), and TP grade accuracy rate is up to 94.97%. Through the error analysis of TN and DO, it is found that 93.0% of error samples occurs when TP < 0.1 mg/L in the water quality. These results would provide a scientific basis for water quality monitoring and water environment management in the study area and could also be used as a reference for water quality monitoring in other basins. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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22 pages, 18732 KiB

Open AccessArticle

Triangle Water Index (TWI): An Advanced Approach for More Accurate Detection and Delineation of Water Surfaces in Sentinel-2 Data

by Lifeng Niu, Hermann Kaufmann, Guochang Xu, Guangzong Zhang, Chaonan Ji, Yufang He and Mengfei Sun

Remote Sens. 2022, 14(21), 5289; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14215289 - 22 Oct 2022

Cited by 5 | Viewed by 4026

Abstract

One of the most basic classification tasks in remote sensing is to distinguish between water bodies and other surface types. Although there are numerous techniques for extracting surface water from satellite imagery, there is still a need for research to more accurately identify water bodies with a view to efficient water maintenance in the future. Delineation accuracy is limited by varying amounts of suspended matter and different background land covers, especially those with low albedo. Therefore, the objective of this study was to develop an advanced index that improves the accuracy of extracting water bodies characterized by varying amounts of water constituents, especially in mountainous regions with highly rugged terrain, urban areas with cast shadows, and snow- and ice-covered areas. In this context, we propose a triangle water index (TWI) based on Sentinel-2 data. The principle of the TWI is that it first analyzes the reflectance values of water bodies in different wavelength bands to determine specific types. Then, triangles are constructed in a cartesian coordinate system according to the reflectance values of different water bodies in the respective wavelength bands. Finally, the TWI is achieved by using the triangle similarity theorem. We tested the accuracy and robustness of the TWI method using Sentinel-2 data of several water bodies in Mongolia, Canada, Sweden, the United States, and China and determined kappa coefficients and the overall precision. The performance of the classifier was compared with methods such as the normalized difference water index (NDWI), the modified normalized difference water index (MNDWI), the enhanced water index (EWI), the automated water extraction index (AWEI), and the land surface water index (LSWI). The classification accuracy of the TWI for all test sites is significantly higher than that of these indices that are commonly used classification methods. The overall precision of the TWI ranges between 95% and 97%. Moreover, the TWI is also effective in extracting flooded areas. Hence, the TWI can automatically extract different water bodies from Sentinel-2 data with high accuracy, which provides also a favorable analysis method for the study of droughts and flood disasters and for the general maintenance of water bodies in the future. Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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Other

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12 pages, 2681 KiB

Open AccessTechnical Note

Densifying and Optimizing the Water Level Series for Large Lakes from Multi-Orbit ICESat-2 Observations

by Tan Chen, Chunqiao Song, Pengfei Zhan and Chenyu Fan

Remote Sens. 2023, 15(3), 780; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15030780 - 30 Jan 2023

Cited by 1 | Viewed by 1627

Abstract

Satellite laser altimetry has been widely used for monitoring surface height changes in inland waters. However, constructing time series of water levels is partially limited in temporal resolution only based on the individual orbit of altimeter observations. To densify and optimize the time series of altimetry-based water levels is crucial to the scientific understanding of lake hydrologic dynamics. This paper focuses on synthesizing the multi-orbit on-lake observations from the Ice, Cloud, and land Elevation Satellite 2 (ICESat-2) to densify and refine the water level time series for large lakes. The approach of synthesizing water level time series has been validated through experiments applied to 18 large lakes worldwide, resulting in an average R of 0.93, RMSE of 0.14 m, MAE of 0.12 m, NSE of 0.67, and CV of 2.86, according to the hydrologic gauge stations. The evaluation results demonstrate that our approach can provide an effective solution for densifying the water level series of large lakes covered by multi-orbit ICESat-2 observations. Further, the approach can be extended to monitor the high-frequency variation of other lakes covered by the multiple ICESat-2 orbits. This approach provides the potential of generating higher-frequency estimates of water levels based on satellite altimetry, which could not only help to reveal the characteristics of the seasonal dynamics of lakes but also be used to investigate the abrupt water level changes due to hydrological extreme events (e.g., floods, droughts, etc.). Full article

(This article belongs to the Special Issue Advances of Remote Sensing and GIS Technology in Surface Water Bodies)

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