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Challenges and Perspectives of Remote Sensing Techniques for Water Resources Assessments and Solutions

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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: closed (30 June 2024) | Viewed by 3682

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Special Issue Editors

Dr. Andrea Scozzari

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Institute of Information Science and Technologies (CNR-ISTI), National Research Council, Via Moruzzi 1, 56124 Pisa, Italy
Interests: environmental remote sensing; radiometry; instrumentation and measurements for water systems; radar altimetry
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Dr. Mariano Bresciani

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Institute for Electromagnetic Sensing of the Environment (CNR-IREA), National Research Council of Italy, Via Corti 12, 20133 Milan, Italy
Interests: optical remote sensing; water quality and monitoring; cyanobacteria; macrophyte; shallow and deep lakes; calibration/validation
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Prof. Dr. Abdelazim Negm

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Water and Water Structures Engineering Department, Faculty of Engineering, Zagazig University, Zagazig 44519, Egypt
Interests: environmental science; remote sensing application in water; engineering; sustainability studies
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The observation of water systems is growing in importance, especially in reference to climate change and increasing anthropic pressure. Today, water management decisions largely depend on instrumental observations, where remotely sensed information currently plays a major role. Given the inherent multidisciplinary characteristics of the water sector, we welcome contributions from both users of remote sensing products and developers of measurement methods, processing techniques and related field experiments. We welcome contributions from all those who are interested in the monitoring and assessment of water resources, and those offering novel solutions to the current problems and challenges. Additionally, novel and innovative applications of remote sensing and GIS in the field of water resources engineering will be highly appreciated.

This Special Issue aims to publish peer-reviewed original research on quantitative and qualitative observations of inland, coastal, and groundwater bodies, including the land–sea interface and coastal processes. Manuscripts should present investigations finalized to the assessment of the resource, with a particular focus on all the aspects that support resource monitoring and decision making for the management of resources. Studies are welcome which are based on both active and passive remote sensing techniques. All the techniques applicable to the water context, such as active and passive radiometry, optical imaging techniques, radar (SAR, SAR-IN and altimetry) and gravimetry, are within the scope of this Special Issue. Discussions of the integration of multisource remote sensing techniques and the usage of complementary technologies (e.g. in situ observations) will also be appreciated.

Scholars are invited to submit research articles, review articles as well as short communications. Articles may address, but are not limited to, the following topics:

Remote sensing of rivers, lakes and reservoirs
Remote sensing in the coastal zone
New generation of hyperspectral sensors
Detection of shoreline changes
Remote sensing of hydraulic infrastructures
Agricultural water and land use
Groundwater assessment and aquifer storage estimation
Monitoring natural resources over time
Assessment of water bodies sustainability and their bathymetry
Remote sensing in coastal structures management

Dr. Andrea Scozzari
Dr. Mariano Bresciani
Prof. Dr. Abdelazim Negm
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

water resources management
lakes and reservoirs
rivers and river deltas
groundwater bodies
remote sensing concepts and advanced sensors for the aquatic environment
shoreline changes
wave energy
sea level rise assessment
coastal zone management

Published Papers (3 papers)

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Research

28 pages, 8365 KiB

Open AccessArticle

Water Dynamics and Morphometric Parameters of Lake Sevan (Armenia) in the Summer–Autumn Period According to Satellite Data

by Anna I. Ginzburg, Andrey G. Kostianoy, Nickolay A. Sheremet and Olga Yu. Lavrova

Remote Sens. 2024, 16(13), 2285; https://doi.org/10.3390/rs16132285 - 22 Jun 2024

Viewed by 306

Abstract

Here, we explore the dynamics of the waters of eutrophicated Lake Sevan in the modern period, using MSI Sentinel-2 satellite images of different months in different years (2017–2022) and SAR Sentinel-1 images of similar dates. The main objective of the study is to investigate the spatiotemporal variability of the horizontal circulation of this lake and to establish whether the scheme of cyclonic water circulation in the deep-water part of Large Sevan, given in a number of publications, which does not imply water exchange between its littoral and deep-water zones, corresponds to the real picture of currents in the surface layer of the lake in the summer–autumn period (period of pronounced water stratification and intense phytoplankton bloom). The analysis performed convincingly showed that there is no constant cyclonic gyre on the scale of the deep-water part of Large Sevan (≈20 km) during the period under consideration. In most cases, non-stationary eddy dynamics are observed in Large Sevan, including mesoscale and submesoscale eddies, eddy dipoles (mushroom-shaped flows), and their packings. Often the entire deep-water part of Large Sevan is occupied by a two-cell (dipole) or even three-cell (cyclonic eddy with two anticyclones of similar size) water circulation. The time scale of the observed variability is several days. Such variable water circulation in different months (i.e., with different density stratification of water) of different years in a basin with a fairly homogeneous bottom and a slight indentation of the shoreline raises the assumption that the main reason for the non-stationary dynamics in Large Sevan is the variability of the wind effect on its surface layer. The cyclonic gyre in Small Sevan (8–9 km) is a permanent element of the circulation and maintains its position north of the strait between Small and Large Sevan. This gyre and attached anticyclonic eddies in the southern part of its periphery, as well as cyclonic submesoscale eddies in the northern part of Large Sevan, close to the strait, affect the water exchange between Small and Large Sevan in both directions. An additional objective of the study is a validation of the morphometric parameters of Lake Sevan (level, surface area, and water volume), contained in the near-real time HYDROWEB database, LEGOS, France (June 1995–January 2024), based on their comparison with the corresponding values of these parameters from gauging stations in Armenia. It is shown that, with a qualitative correspondence of the nature of lake level changes according to altimetric and instrumental measurements, its values in the HYDROWEB database exceed the data of gauging stations in most cases by 1–1.5 m in 1995–2012 and 0.5–0.6 m in 2013–2022, while the corresponding surface area and volume values according to HYDROWEB data turn out to be underestimated. Full article

(This article belongs to the Special Issue Challenges and Perspectives of Remote Sensing Techniques for Water Resources Assessments and Solutions)

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21 pages, 3106 KiB

Open AccessArticle

The Long-Term Detection of Suspended Particulate Matter Concentration and Water Colour in Gravel and Sand Pit Lakes through Landsat and Sentinel-2 Imagery

by Nicola Ghirardi, Monica Pinardi, Daniele Nizzoli, Pierluigi Viaroli and Mariano Bresciani

Remote Sens. 2023, 15(23), 5564; https://doi.org/10.3390/rs15235564 - 29 Nov 2023

Cited by 1 | Viewed by 1081

Abstract

Over the past half century, the demand for sand and gravel has led to extensive quarrying activities, creating many pit lakes (PLs) which now dot floodplains and urbanized regions globally. Despite the potential importance of these environments, systematic data on their location, morphology and water quality remain limited. In this study, we present an extensive assessment of the physical and optical properties in a large sample of PLs located in the Po River basin (Italy) from 1990 to 2021, utilizing a combined approach of remote sensing (Landsat constellation and Sentinel-2) and traditional limnological techniques. Specifically, we focused on the concentration of Suspended Particulate Matter (SPM) and the dominant wavelength (λ_dom, i.e., water colour). This study aims to contribute to the analysis of PLs at a basin scale as an opportunity for environmental rehabilitation and river floodplain management. ACOLITE v.2022, a neural network particularly suitable for the analysis of turbid waters and small inland water bodies, was used to atmospherically correct satellite images and to obtain SPM concentration maps and the λ_dom. The results show a very strong correlation between SPM concentrations obtained in situ and those obtained from satellite images, both for data derived from Landsat (R² = 0.85) and Sentinel-2 images (R² = 0.82). A strong correlation also emerged from the comparison of spectral signatures obtained in situ via WISP-3 and those derived from ACOLITE, especially in the visible spectrum (443–705 nm, SA = 10.8°). In general, it appeared that PLs with the highest mean SPM concentrations and the highest mean λ_dom are located along the main Po River, and more generally near rivers. The results also show that active PLs exhibit a poor water quality status, especially those of small sizes (<5 ha) and directly connected to a river. Seasonal comparison shows the same trend for both SPM concentration and λ_dom: higher values in winter gradually decreasing until spring–summer, then increasing again. Finally, it emerged that the end of quarrying activity led to a reduction in SPM concentration from a minimum of 43% to a maximum of 72%. In this context, the combined use of Landsat and Sentinel-2 imagery allowed for the evaluation of the temporal evolution of the physical and optical properties of the PLs in a vast area such as the Po River basin (74,000 km²). In particular, the Sentinel-2 images consistently proved to be a reliable resource for capturing episodic and recurring quarrying events and portraying the ever-changing dynamics of these ecosystems. Full article

(This article belongs to the Special Issue Challenges and Perspectives of Remote Sensing Techniques for Water Resources Assessments and Solutions)

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23 pages, 13789 KiB

Open AccessArticle

Comparative Analysis of Deep Learning and Swarm-Optimized Random Forest for Groundwater Spring Potential Identification in Tropical Regions

by Viet-Ha Nhu, Pham Viet Hoa, Laura Melgar-García and Dieu Tien Bui

Remote Sens. 2023, 15(19), 4761; https://doi.org/10.3390/rs15194761 - 28 Sep 2023

Cited by 2 | Viewed by 1747

Abstract

Identifying areas with high groundwater spring potential is crucial as it enables better decision-making concerning water supply, sustainable development, and the protection of sensitive ecosystems; therefore, it is necessary to predict the groundwater spring potential with highly accurate models. This study aims to assess and compare the effectiveness of deep neural networks (DeepNNs) and swarm-optimized random forests (SwarmRFs) in predicting groundwater spring potential. This study focuses on a case study conducted in the Gia Lai province, located in the Central Highland of Vietnam. To accomplish this objective, a comprehensive groundwater database was compiled, comprising 938 groundwater spring locations and 12 influential variables, namely land use and land cover (LULC), geology, distance to fault, distance to river, rainfall, normalized difference vegetation index (NDVI), normalized difference moisture index (NDMI), normalized difference water index (NDWI), slope, aspect, elevation, and curvature. The DeepNN model was trained and fine-tuned using the Adaptive Moment Estimation (ADAM) optimizer, while the SwarmRF model employed the Harris Hawks Optimizer (HHO) to search for optimal parameters. The results indicate that both the DeepNN model (accuracy = 77.9%, F-score = 0.783, kappa = 0.559, and AUC = 0.820) and the SwarmRF model (accuracy = 80.2%, F-score = 0.798, kappa = 0.605, and AUC = 0.854) exhibit robust predictive capabilities. The SwarmRF model displays a slight advantage over the DeepNN model in terms of performance. Among the 12 influential factors, geology emerges as the most significant determinant of groundwater spring potential. The groundwater spring potential maps generated through this research can offer valuable information for local authorities to facilitate effective water resource management and support sustainable development planning. Full article

(This article belongs to the Special Issue Challenges and Perspectives of Remote Sensing Techniques for Water Resources Assessments and Solutions)

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