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Applications of Remote Sensing in Hydrology and Water Resource Management
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Applications of Remote Sensing in Hydrology and Water Resource 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: 25 November 2025 | Viewed by 4011

Special Issue Editor

Dr. Rana Waqar Aslam

E-Mail Website
Guest Editor
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Interests: wetlands; remote sensing; groundwater quality; GIS; LULC; ecology; ecosystem; machine learning; water resource management; hydrology; SAR

Special Issue Information

Dear Colleagues,

Sustainable water resource management constitutes a paramount scientific challenge of the current era, exacerbated by intensifying climate variability and anthropogenic pressures. Advanced remote sensing technologies have emerged as indispensable tools for quantifying hydrological processes across unprecedented spatiotemporal scales, facilitating transformative insights into complex water cycle dynamics. This Special Issue solicits cutting-edge research on novel applications of multi-platform remote sensing in hydrology and water resource management. We particularly encourage submissions featuring innovative methodologies in precipitation retrieval algorithms, high-resolution soil moisture detection, groundwater fluctuation analysis, real-time flood prediction systems, and wetland ecosystem monitoring. Priority will be given to manuscripts demonstrating advanced multi-sensor fusion techniques, sophisticated machine learning approaches for data interpretation, or novel frameworks integrating remote sensing observations with in situ networks and physically based hydrological models. Additionally, we welcome pioneering research addressing challenges in data assimilation protocols, uncertainty quantification frameworks, and the operational implementation of state-of-the-art remote sensing products in water management decision support systems. This Special Issue aims to catalyze synergistic collaborations between remote sensing specialists and hydrologists, accelerating the transition from theoretical advancements to practical applications for resilient water resource management paradigms.

Dr. Rana Waqar Aslam
Guest Editor

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

  • hydrological remote sensing
  • water resource monitoring
  • satellite hydrology
  • soil moisture detection
  • precipitation estimation
  • flood mapping
  • drought assessment
  • multi-sensor integration
  • machine learning in hydrology
  • wetlands/lakes dynamics

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Published Papers (3 papers)

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Research

20 pages, 3577 KB  
Article
Hyperspectral Remote Sensing and Artificial Intelligence for High-Resolution Soil Moisture Prediction
by Ki-Sung Kim, Junwon Lee, Jeongjun Park, Gigwon Hong and Kicheol Lee
Water 2025, 17(21), 3069; https://doi.org/10.3390/w17213069 (registering DOI) - 27 Oct 2025
Abstract
Reliable field estimation of soil moisture supports hydrology and water resources management. This study develops a drone-based hyperspectral approach in which visible and near-infrared reflectance is paired one-to-one with gravimetric water content measured by oven drying, yielding 1000 matched samples. After standardization, outlier [...] Read more.
Reliable field estimation of soil moisture supports hydrology and water resources management. This study develops a drone-based hyperspectral approach in which visible and near-infrared reflectance is paired one-to-one with gravimetric water content measured by oven drying, yielding 1000 matched samples. After standardization, outlier control, ranked wavelength selection, and light feature engineering, several predictors were evaluated. Conventional machine learning methods, including simple and multiple regression and tree-based ensembles, were limited by band collinearity and piecewise approximations and therefore failed to meet the accuracy target. Gradient boosting reached the target but used different trade-offs in variable sensitivity. An artificial neural network with three hidden layers, rectified linear unit activations, and dropout was trained using a feature count sweep and early stopping. With ten predictors, the model achieved a coefficient of determination of 0.9557, demonstrating accurate mapping from hyperspectral reflectance to gravimetric water content and providing a reproducible framework suitable for larger, multi date acquisitions and operational decision support. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Hydrology and Water Resource Management)
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18 pages, 2291 KB  
Article
Forecasting Tibetan Plateau Lake Level Responses to Climate Change: An Explainable Deep Learning Approach Using Altimetry and Climate Models
by Atefeh Gholami and Wen Zhang
Water 2025, 17(16), 2434; https://doi.org/10.3390/w17162434 - 17 Aug 2025
Viewed by 916
Abstract
The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled [...] Read more.
The Tibetan Plateau’s lakes, serving as critical water towers for over two billion people, exhibit divergent responses to climate change that remain poorly quantified. This study develops a deep learning framework integrating Synthetic Aperture Radar (SAR) altimetry from Sentinel-3A with bias-corrected CMIP6 (Coupled Model Intercomparison Project Phase 6) climate projections under Shared Socioeconomic Pathways (SSP) scenarios (SSP2-4.5 and SSP5-8.5, adjusted via quantile mapping) to predict lake-level changes across eight Tibetan Plateau (TP) lakes. Using a Feed-Forward Neural Network (FFNN) optimized via Bayesian optimization using the Optuna framework, we achieve robust water level projections (mean validation R2 = 0.861) and attribute drivers through Shapley Additive exPlanations (SHAP) analysis. Results reveal a stark north–south divergence: glacier-fed northern lakes like Migriggyangzham will rise by 13.18 ± 0.56 m under SSP5-8.5 due to meltwater inputs (temperature SHAP value = 0.41), consistent with the early (melt-dominated) phase of the IPCC’s ‘peak water’ framework. In comparison, evaporation-dominated southern lakes such as Langacuo face irreversible desiccation (−4.96 ± 0.68 m by 2100) as evaporative demand surpasses precipitation gains. Transitional western lakes exhibit “peak water” inflection points (e.g., Lumajang Dong’s 2060 maximum) signaling cryospheric buffer loss. These projections, validated through rigorous quantile mapping and rolling-window cross-validation, provide the first process-aware assessment of TP Lake vulnerabilities, informing adaptation strategies under the Sustainable Development Goals (SDGs) for water security (SDG 6) and climate action (SDG 13). The methodological framework establishes a transferable paradigm for monitoring high-altitude freshwater systems globally. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Hydrology and Water Resource Management)
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23 pages, 4107 KB  
Article
Assessing Recharge Zones for Groundwater Potential in Dera Ismail Khan (Pakistan): A GIS-Based Analytical Hierarchy Process Approach
by Anwaar Tabassum, Asif Sajjad, Ghayas Haider Sajid, Mahtab Ahmad, Mazhar Iqbal and Aqib Hassan Ali Khan
Water 2025, 17(11), 1586; https://doi.org/10.3390/w17111586 - 23 May 2025
Cited by 3 | Viewed by 2465
Abstract
Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable [...] Read more.
Groundwater constitutes the primary source of liquid freshwater on Earth and is essential for ecosystems, agriculture, and human consumption. However, rising demand, urbanization, and climate change have intensified groundwater depletion, particularly in semi-arid regions. Therefore, assessing groundwater recharge zones is essential for sustainable water resource management in vulnerable areas such as Dera Ismail Khan, Pakistan. This study aims to delineate groundwater potential zones (GWPZs), using an integrated approach combining the Geographic Information System (GIS), remote sensing (RS), and the analytical hierarchy process (AHP). Twelve factors were identified in a study conducted using GIS-based AHP to determine the groundwater recharge zones in the region. These include land use/land cover (LULC), rainfall, drainage density, soil type, slope, road density, water table depth, and remote sensing indices such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), Moisture Stress Index (MSI), Worldview Water Index (WVWI), and Land Surface Temperature (LST). The results show that 17.52% and 2.03% of the area have “good” and “very good” potential for groundwater recharge, respectively, while 48.63% of the area has “moderate” potential. Furthermore, gentle slopes (0–2.471°), high drainage density, shallow water depths (20–94 m), and densely vegetated areas (with a high NDVI) are considered important influencing factors for groundwater recharge. Conversely, areas with steep slopes, high temperatures, and dense built-up areas showed “poor” potential for recharge. This approach demonstrates the effectiveness of integrating advanced remote sensing indices with the AHP model in a semi-arid context, validated through high-accuracy field data (Kappa = 0.93). This methodology offers a cost-effective decision support tool for sustainable groundwater planning in similar environments. Full article
(This article belongs to the Special Issue Applications of Remote Sensing in Hydrology and Water Resource Management)
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