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

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 2977

Special Issue Editors


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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
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
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
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 (2 papers)

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Research

21 pages, 3106 KiB  
Article
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
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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 [...] Read more.
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 (λdom, 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 λdom. 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 λdom 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 λdom: 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
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23 pages, 13789 KiB  
Article
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 1 | Viewed by 1604
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 [...] Read more.
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
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