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Use of Remote Sensing Technologies for Water Resources Management

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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 5335

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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

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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 (5 papers)

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Research

27 pages, 20003 KiB

Open AccessArticle

Spatiotemporal Patterns of Algal Blooms in Lake Bosten Driven by Climate and Human Activities: A Multi-Source Remote-Sensing Perspective for Sustainable Water-Resource Management

by Haowei Wang, Zhoukang Li, Yang Wang and Tingting Xia

Water 2025, 17(16), 2394; https://doi.org/10.3390/w17162394 - 13 Aug 2025

Abstract

Algal blooms pose a serious threat not only to the lake ecosystem of Lake Bosten but also by negatively impacting its rapidly developing fisheries and tourism industries. This study focuses on Lake Bosten as the research area and utilizes multi-source remote sensing imagery from Landsat TM/ETM+/OLI and Sentinel-2 MSI. The Adjusted Floating Algae Index (AFAI) was employed to extract algal blooms in Lake Bosten from 2004 to 2023, analyze their spatiotemporal evolution characteristics and driving factors, and construct a Long Short Term Memory (LSTM) network model to predict the spatial distribution of algal-bloom frequency. The stability of the model was assessed through temporal segmentation of historical data combined with temporal cross-validation. The results indicate that (1) during the study period, algal blooms in Lake Bosten were predominantly of low-risk level, with low-risk bloom coverage accounting for over 8% in both 2004 and 2005. The intensity of algal blooms in summer and autumn was significantly higher than in spring. The coverage of medium- and high-risk blooms reached 2.74% in the summer of 2004 and 3.03% in the autumn of 2005, while remaining below 1% in spring. (2) High-frequency algal bloom areas were mainly located in the western and northwestern parts of the lake, and the central region experienced significantly more frequent blooms during 2004–2013 compared to 2014–2023, particularly in spring and summer. (3) The LSTM model achieved an R² of 0.86, indicating relatively stable performance. The prediction results suggest a continued low frequency of algal blooms in the future, reflecting certain achievements in sustainable water-resource management. (4) The interactions among meteorological factors exhibited significant influence on bloom formation, with the q values of temperature and precipitation interactions both exceeding 0.5, making them the most prominent meteorological driving factors. Monitoring of sewage discharge and analysis of agricultural and industrial expansion revealed that human activities have a more direct impact on the water quality of Lake Bosten. In addition, changes in lake area and water environment were mainly influenced by anthropogenic factors, ultimately making human activities the primary driving force behind the spatiotemporal variations of algal blooms. This study improved the timeliness of algal-bloom monitoring through the integration of multi-source remote sensing and successfully predicted the future spatial distribution of bloom frequency, providing a scientific basis and decision-making support for the sustainable management of water resources in Lake Bosten. Full article

(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)

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36 pages, 3457 KiB

Open AccessArticle

Evaluating CHIRPS and ERA5 for Long-Term Runoff Modelling with SWAT in Alpine Headwaters

by Damir Bekić and Karlo Leskovar

Water 2025, 17(14), 2116; https://doi.org/10.3390/w17142116 - 16 Jul 2025

Viewed by 501

Abstract

Reliable gridded precipitation products (GPPs) are essential for effective hydrological simulations, particularly in mountainous regions with limited ground-based observations. This study evaluates the performance of two widely used GPPs, CHIRPS and ERA5, in estimating precipitation and supporting runoff generation using the Soil and Water Assessment Tool (SWAT) across three headwater catchments (Sill, Drava and Isel) in the Austrian Alps from 1991 to 2018. The region’s complex topography and climatic variability present a rigorous test for GPP application. The evaluation methods combined point-to-point comparisons with gauge observations and assessments of generated runoff and runoff trends at annual, seasonal and monthly scales. CHIRPS showed a lower precipitation error (RMAE = 25%) and generated more consistent runoff results (RMAE = 12%), particularly in smaller catchments, whereas ERA5 showed higher spatial consistency but higher overall precipitation bias (RMAE = 37%). Although both datasets successfully reproduced the seasonal runoff regime, CHIRPS outperformed ERA5 in trend detection and monthly runoff estimates. Both GPPs systematically overestimate annual and seasonal precipitation amounts, especially at lower elevations and during the cold season. The results highlight the critical influence of GPP spatial resolution and its alignment with catchment morphology on model performance. While both products are viable alternatives to observed precipitation, CHIRPS is recommended for hydrological modelling in smaller, topographically complex alpine catchments due to its higher spatial resolution. Despite its higher precipitation bias, ERA5’s superior correlation with observations suggests strong potential for improved model performance if bias correction techniques are applied. The findings emphasize the importance of selecting GPPs based on the scale and geomorphological and climatic conditions of the study area. Full article

(This article belongs to the Special Issue Use of Remote Sensing Technologies for Water Resources Management)

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23 pages, 6099 KiB

Open AccessArticle

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

Cited by 6 | Viewed by 1033

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 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

Open AccessArticle

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

Cited by 1 | Viewed by 1499

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 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

R^{2}

of around 0.76 and a standard deviation of around 0.03. Field data validation achieved an

R^{2}

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

Open AccessArticle

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 2 | Viewed by 1721

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 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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