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The Use of Remote Sensing Data in Water Resources Management: Current Challenges and Future Opportunities

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

Deadline for manuscript submissions: 15 July 2025 | Viewed by 4587

Special Issue Editors

Dr. Mohamed Ahmed

E-Mail Website
Guest Editor
Department of Geology, Suez Canal University, Ismailia 41522, Egypt
Interests: geophysics; groundwater; land deformation; remote sensing; machine learning; numerical modeling; GIS
Dr. Esayas Gebremichael

E-Mail Website
Guest Editor
Department of Geological Sciences, Texas Christian University, Fort Worth, TX, USA
Interests: natural hazards; coastal processes and climate change; InSAR; hydrology; remote sensing

Special Issue Information

Dear Colleagues,

Water resources (e.g., surface water and groundwater) are vulnerable to both natural and man-made variabilities. Monitoring the responses of water resources to these variabilities is more challenging than ever before. The field data that are required for monitoring programs are spatially limited, expensive, and time consuming. Remote sensing data (visible, thermal, and radar) complement and/or can provide an alternative to field data given their global coverage, public availability (for most part), and spatial and temporal consistency. However, there are challenges and limitations in the use of remote sensing data in monitoring water resources on both local and global scales.

This Special Issue covers data analysis techniques, applications, and limitations of remote sensing data in monitoring water resources and their response to natural and anthropogenic forces. We encourage submissions of high-impact research in the form of new research articles, methodology articles, and review articles that addresses current challenges and future opportunities in the use of remote sensing data in monitoring water resources across local and global scales.

Dr. Mohamed Ahmed
Dr. Esayas Gebremichael
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

  • surface water
  • groundwater
  • visible data
  • thermal data
  • radar data monitoring
  • natural variability
  • climate change
  • anthropogenic variability

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Published Papers (2 papers)

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Research

24 pages, 21233 KiB  
Article
Remote Sensing Tool for Reservoir Volume Estimation
by João Pimenta, João Nuno Fernandes and Alberto Azevedo
Remote Sens. 2025, 17(4), 619; https://doi.org/10.3390/rs17040619 - 11 Feb 2025
Cited by 1 | Viewed by 1140
Abstract
Efficient reservoir management is essential for ensuring water security and flood control, as well as hydroelectric power generation. Accurate volume measurements are key to optimizing these functions, but traditional methods—such as in situ measurements and physical surveys—are often time-consuming, costly, and unfeasible in [...] Read more.
Efficient reservoir management is essential for ensuring water security and flood control, as well as hydroelectric power generation. Accurate volume measurements are key to optimizing these functions, but traditional methods—such as in situ measurements and physical surveys—are often time-consuming, costly, and unfeasible in many regions due to financial or geographical limitations. This study introduces a novel globally accessible remote sensing tool designed to overcome these challenges by providing a more effective approach to reservoir volume estimation. The tool leverages high-resolution satellite imagery from Sentinel-2 and integrates it with official storage capacity data and the GLOBAthy dataset to calculate surface area and reservoir volume across varying water levels over user-defined timeframes. Users can select reservoirs, date ranges, and cloud cover thresholds via an intuitive interface, which then generates time-series data of reservoir volumes. The tool employs machine learning algorithms to improve the precision of water surface delineation and volume calculations, accounting for complex environmental factors like cloud cover and built structures such as bridges. This remote sensing tool was tested on reservoirs of varying sizes and topographies in Portugal and California, USA, demonstrating a high accuracy with a Mean Absolute Percentage Error (MAPE) of 5.35% and a correlation coefficient (R2) of 0.90 when compared to official records. By offering a cost-effective, scalable, totally remote, and timely solution, the tool enables improved reservoir monitoring, particularly in remote or otherwise inaccessible areas. Ultimately, this research contributes to global water resources management, enhancing the sustainability and resilience of reservoir operations around the world. Full article
(This article belongs to the Special Issue The Use of Remote Sensing Data in Water Resources Management: Current Challenges and Future Opportunities)
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26 pages, 5003 KiB  
Article
Monitoring Water Quality Indicators over Matagorda Bay, Texas, Using Landsat-8
by Meghan Bygate and Mohamed Ahmed
Remote Sens. 2024, 16(7), 1120; https://doi.org/10.3390/rs16071120 - 22 Mar 2024
Cited by 6 | Viewed by 2478
Abstract
Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and [...] Read more.
Remote sensing datasets offer a unique opportunity to observe spatial and temporal trends in water quality indicators (WQIs), such as chlorophyll-a, salinity, and turbidity, across various aquatic ecosystems. In this study, we used available in situ WQI measurements (chlorophyll-a: 17, salinity: 478, and turbidity: 173) along with Landsat-8 surface reflectance data to examine the capability of empirical and machine learning (ML) models in retrieving these indicators over Matagorda Bay, Texas, between 2014 and 2023. We employed 36 empirical models to retrieve chlorophyll-a (12 models), salinity (2 models), and turbidity (22 models) and 4 ML families—deep neural network (DNN), distributed random forest, gradient boosting machine, and generalized linear model—to retrieve salinity and turbidity. We used the Nash–Sutcliffe efficiency coefficient (NSE), correlation coefficient (r), and normalized root mean square error (NRMSE) to assess the performance of empirical and ML models. The results indicate that (1) the empirical models displayed minimal effectiveness when applied over Matagorda Bay without calibration; (2) once calibrated over Matagorda Bay, the performance of the empirical models experienced significant improvements (chlorophyll-a—NRMSE: 0.91 ± 0.03, r: 0.94 ± 0.04, NSE: 0.89 ± 0.06; salinity—NRMSE: 0.24 ± 0, r: 0.24 ± 0, NSE: 0.06 ± 0; turbidity—NRMSE: 0.15 ± 0.10, r: 0.13 ± 0.09, NSE: 0.03 ± 0.03); (3) ML models outperformed calibrated empirical models when used to retrieve turbidity and salinity, and (4) the DNN family outperformed all other ML families when used to retrieve salinity (NRMSE: 0.87 ± 0.09, r: 0.49 ± 0.09, NSE: 0.23 ± 0.12) and turbidity (NRMSE: 0.63± 0.11, r: 0.79 ± 0.11, NSE: 0.60 ± 0.20). The developed approach provides a reference context, a structured framework, and valuable insights for using empirical and ML models and Landsat-8 data to retrieve WQIs over aquatic ecosystems. The modeled WQI data could be used to expand the footprint of in situ observations and improve current efforts to conserve, enhance, and restore important habitats in aquatic ecosystems. Full article
(This article belongs to the Special Issue The Use of Remote Sensing Data in Water Resources Management: Current Challenges and Future Opportunities)
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