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Use of Remote Sensing Technologies for Water Resources Management
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Use of Remote Sensing Technologies for Water Resources Management

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "New Sensors, New Technologies and Machine Learning in Water Sciences".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 3312

Special Issue Editors

Dr. Anuj Tiwari

E-Mail Website
Guest Editor
Discovery Partners Institute (DPI), University of Illinois System, Chicago, IL, USA
Interests: satellite hydrology; UAV remote sensing; predictive modeling; feature engineering; public health; water monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Anukesh Krishnan Kutty Ambika

E-Mail Website
Guest Editor Assistant
Oak Ridge National Laboratory, Oak Ridge, TN, USA
Interests: climate modeling; hydroclimatic extremes; hydrology remote sensing; machine learning

Special Issue Information

Dear Colleagues,

This Special Issue titled “Use of Remote Sensing Technologies for Water Resources Management” incorporates a wide array of remote sensing technologies, including satellite, UAV, LiDAR, ground-based sensors, and radar imaging, to capture comprehensive data about water systems. The focus is on integrating traditional and non-traditional remote sensing tools for monitoring hydrological systems and water resources. In terms of methodology, this issue emphasizes the application of cutting-edge techniques such as machine learning and deep learning for data processing and analysis and the effective use of traditional analytics and statistical methods. This combined approach aims to support innovative decision making in water resource management, ensuring that advanced and conventional methodologies contribute to solving key water challenges. Applications will span diverse domains, including water quality assessment, flood and drought prediction, agricultural water use optimization, and urban water management. In addition, this Special Issue will highlight how remote sensing is addressing key public health challenges such as hazardous algal blooms (HABs) and waterborne disease monitoring. Remote sensing technologies are crucial in early detection and mitigation of harmful environmental conditions that impact public health, including monitoring pollution and managing waterborne pathogens. This issue aims to advance the resilience of water systems and public health infrastructure by fostering innovative methodologies.

Dr. Anuj Tiwari
Guest Editor

Dr. Anukesh Krishnan Kutty Ambika
Guest Editor Assistant

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. Water 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 2600 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

  • satellite remote sensing
  • unmanned aerial vehicle (UAV)
  • LiDAR hydrology
  • ground-based remote sensing
  • geospatial intelligence
  • hydrological modeling
  • flood and drought prediction
  • hazardous algal blooms (HABs)
  • waterborne disease monitoring

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Published Papers (3 papers)

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Research

23 pages, 6099 KiB  
Article
Evaluation of ICESat-2 Laser Altimetry for Inland Water Level Monitoring: A Case Study of Canadian Lakes
by Yunus Kaya
Water 2025, 17(7), 1098; https://doi.org/10.3390/w17071098 - 6 Apr 2025
Viewed by 497
Abstract
This study evaluates the performance of the ICESat-2 ATL13 altimetry product for estimating water levels in 182 Canadian lakes by integrating satellite-derived observations with in situ gauge measurements and applying spatial filtering using the HydroLAKES dataset. The analysis compares ATL13-derived lake surface elevations [...] Read more.
This study evaluates the performance of the ICESat-2 ATL13 altimetry product for estimating water levels in 182 Canadian lakes by integrating satellite-derived observations with in situ gauge measurements and applying spatial filtering using the HydroLAKES dataset. The analysis compares ATL13-derived lake surface elevations with hydrometric data from national monitoring stations, providing a robust framework for assessing measurement accuracy. Statistical metrics—including root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)—are employed to quantify discrepancies between the datasets. Importantly, the application of HydroLAKES-based filtering reduces the mean RMSE from 1.53 m to 1.40 m, and the further exclusion of high-error lakes lowers it to 0.96 m. Larger and deeper lakes exhibit lower error margins, while smaller lakes with complex shorelines show greater variability. Regression analysis confirms the excellent agreement between satellite and gauge measurements (R2 = 0.9999; Pearson’s r = 0.9999, n = 182 lakes, p < 0.0001). Temporal trends reveal declining water levels in 134 lakes and increasing levels in 48 lakes from 2018 to 2024, potentially reflecting climatic variability and human influence. These findings highlight the potential utility of ICESat-2 ATL13 altimetry for large-scale inland water monitoring when combined with spatial filtering techniques such as HydroLAKES. Full article
(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)
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20 pages, 3096 KiB  
Article
Water Clarity Assessment Through Satellite Imagery and Machine Learning
by Joaquín Salas, Rodrigo Sepúlveda and Pablo Vera
Water 2025, 17(2), 253; https://doi.org/10.3390/w17020253 - 17 Jan 2025
Viewed by 1193
Abstract
Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need for sustainable water management. This study aims to assess water clarity by predicting the Secchi disk depth (SDD) using satellite images and ML techniques. The primary methods [...] Read more.
Leveraging satellite monitoring and machine learning (ML) techniques for water clarity assessment addresses the critical need for sustainable water management. This study aims to assess water clarity by predicting the Secchi disk depth (SDD) using satellite images and ML techniques. The primary methods involve data preparation and SSD inference. During data preparation, AquaSat samples, originally from the L1TP collection, were updated with the Landsat 8 satellite’s latest postprocessing, L2SP, which includes atmospheric corrections, resulting in 33,261 multispectral observations and corresponding SSD measurements. For inferring the SSD, regressors such as SVR, NN, and XGB, along with an ensemble of them, were trained. The ensemble demonstrated performance with an average determination coefficient of R2 of around 0.76 and a standard deviation of around 0.03. Field data validation achieved an R2 of 0.80. Furthermore, we show that the regressors trained with L1TP imagery for predicting SSD result in a favorable performance with respect to their counterparts trained on the L2SP collection. This document contributes to the transition from semi-analytical to data-driven methods in water clarity research, using an ML ensemble to assess the clarity of water bodies through satellite imagery. Full article
(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)
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21 pages, 8795 KiB  
Article
Morphometric Characterization and Dual Analysis for Flash Flood Hazard Assessment of Wadi Al-Lith Watershed, Saudi Arabia
by Bashar Bashir and Abdullah Alsalman
Water 2024, 16(22), 3333; https://doi.org/10.3390/w16223333 - 20 Nov 2024
Cited by 1 | Viewed by 1205
Abstract
Flash floods are one of the most hazardous natural events globally, characterized by their rapid onset and unpredictability, often overwhelming emergency preparedness and response systems. In the arid environment of Saudi Arabia, Wadi Al-Lith watershed is particularly prone to flash floods, exacerbated by [...] Read more.
Flash floods are one of the most hazardous natural events globally, characterized by their rapid onset and unpredictability, often overwhelming emergency preparedness and response systems. In the arid environment of Saudi Arabia, Wadi Al-Lith watershed is particularly prone to flash floods, exacerbated by sudden storms and the region’s distinct topographical features. This study focuses on the morphometric characterization and comparative analysis of flash flood risk within the Wadi Al-Lith basin. To assess flood susceptibility, two widely adopted methodologies were employed: the morphometric ranking approach and El-Shamy’s method. A 12.5-m resolution ALOS PALSAR digital elevation model (DEM) was used to delineate the watershed and generate a detailed drainage network via Arc-Hydro tools in the ArcGIS 10.4 software. Fifteen morphometric parameters were analyzed to determine their influence on flood potential and hazard prioritization. The findings of this study provide crucial insights for regional flood risk management, offering an improved understanding of flash flood dynamics and assisting in developing effective mitigation strategies for Wadi Al-Lith and similar environments. The findings reveal that Wadi Al-Lith comprises multiple sub-catchments with varying degrees of vulnerability to flash flooding. According to the morphometric hazard analysis (MHA), certain sub-catchments, including sc-2, sc-4, sc-5, sc-6, sc-10, sc-12, sc-13, and sc-15, emerge as highly susceptible to flood hazards, while others (sc-1 and sc-9) fall into moderate risk categories. In contrast, the application of El-Shamy’s method provides a different ranking of flood risks across the watershed’s sub-catchments, offering a comparative view of flood susceptibility. The insights gained from this dual-analysis approach are expected to support the development of targeted flood prevention and mitigation strategies, which are essential for minimizing the future impacts of flash flooding in the Wadi Al-Lith watershed and ensuring better preparedness for local communities. Full article
(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)
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