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Remote Sensing
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Remote Sensing Applications in Hydrology and Water Resources Management
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Remote Sensing Applications in Hydrology and Water Resources Management

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

Deadline for manuscript submissions: 10 July 2025 | Viewed by 2806

Special Issue Editors

Dr. Ning Liu

E-Mail Website
Guest Editor
Commonwealth Scientific and Industrial Research Organisation—Environment, Canberra, Australia
Interests: ecohydrological modeling; effects of climate change and land management on water availability; ecosystem service accounting
Dr. Yun Yang

E-Mail Website
Guest Editor
Department of Forestry, College of Forest Resources, Forest and Wildlife Research Center, Mississippi State University, Starkville, MS 39759, USA
Interests: impacts of climate change and human activities on the interaction between surface water and groundwater; field-scale evapotranspiration mapping using remotely sensed data with cloud computing; multi-sensor data fusion for improved spatiotemporal sampling; vegetation health monitoring for agriculture and natural resource management
Special Issues, Collections and Topics in MDPI journals
Dr. Ge Sun

E-Mail Website
Guest Editor
Eastern Forest Environmental Threat Assessment Center, Southern Research Station, US Department of Agriculture Forest Service, Research Triangle Park, NC 27709, USA
Interests: effects of climate change and land management on water quantity and quality, and water supply and demand at a regional scale; Application of computer simulation models, GIS, and remote sensing in regional hydrology; Evapotranspiration and ecosystem productivity modeling at regional to continental scales
Special Issues, Collections and Topics in MDPI journals
Dr. Gabriel Senay

E-Mail Website
Guest Editor
USGS EROS Center, North Central Climate Adaptation Science Center, Fort Collins, CO 80523, USA
Interests: remote sensing hydrology; evapotranspiration and soil moisture modeling; drought monitoring and food security; water use, quality, and availability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water, a fundamental resource, is at the nexus of climate change challenges and the needs of an expanding global populace. As we navigate these complex issues, the imperative to accurately gauge water availability, trace its sources, and predict its fluctuations due to environmental and anthropogenic factors grows. However, traditional ground-based methods, while valuable, often grapple with the constraints of localized study costs and the intricacies of expansive assessments. The rapid advancements in remote sensing technology, coupled with the increasing accessibility of satellite data, have revolutionized the field of hydrology and water resources management.

This Special Issue aims to bridge these research gaps by highlighting diverse case studies that employ advanced remote sensing applications. We welcome contributions that explore the use of remotely sensed data from various platforms such as UAVs and airborne and satellite sensors in estimating hydrological processes. We are particularly focused on studies that demonstrate the integration of these remote sensing insights into robust models suitable for local or regional water resource management. By presenting this collection, we hope to stimulate interdisciplinary dialogue, promote scientific advancement, and advocate for sustainable water management strategies using remote sensing in our changing world.

Topics of interest may include, but are not limited to, the following:

  • Applications in monitoring and managing surface and ground water using remote sensing.
  • Remote sensing for water resource management.
  • Water availability assessment and prediction using satellite data.
  • Estimation of evapotranspiration and runoff through remote sensing.
  • Integration of remote sensing data with hydrological models.
  • Impactful case studies on remote sensing applications in water resource management.

Dr. Ning Liu
Dr. Yun Yang
Dr. Ge Sun
Dr. Gabriel Senay
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 use
  • water supply
  • water availability
  • hydrologic modeling
  • remote sensing
  • water resource management

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 7576 KiB  
Article
Interpreting Global Terrestrial Water Storage Dynamics and Drivers with Explainable Deep Learning
by Haijun Huang, Xitian Cai, Lu Li, Xiaolu Wu, Zichun Zhao and Xuezhi Tan
Remote Sens. 2025, 17(13), 2118; https://doi.org/10.3390/rs17132118 - 20 Jun 2025
Viewed by 280
Abstract
Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has [...] Read more.
Sustained reductions in terrestrial water storage (TWS) have been observed globally using Gravity Recovery and Climate Experiment (GRACE) satellite data since 2002. However, the underlying mechanisms remain incompletely understood due to limited record lengths and data discontinuity. Recently, explainable artificial intelligence (XAI) has provided robust tools for unveiling dynamics in complex Earth systems. In this study, we employed a deep learning technique (Long Short-Term Memory network, LSTM) to reconstruct global TWS dynamics, filling gaps in the GRACE record. We then utilized the Local Interpretable Model-agnostic Explanations (LIME) method to uncover the underlying mechanisms driving observed TWS reductions. Our results reveal a consistent decline in the global mean TWS over the past 22 years (2002–2024), primarily influenced by precipitation (17.7%), temperature (16.0%), and evapotranspiration (10.8%). Seasonally, the global average of TWS peaks in April and reaches a minimum in October, mirroring the pattern of snow water equivalent with approximately a one-month lag. Furthermore, TWS variations exhibit significant differences across latitudes and are driven by distinct factors. The largest declines in TWS occur predominantly in high latitudes, driven by rising temperatures and significant snow/ice variability. Mid-latitude regions have experienced considerable TWS losses, influenced by a combination of precipitation, temperature, air pressure, and runoff. In contrast, most low-latitude regions show an increase in TWS, which the model attributes mainly to increased precipitation. Notably, TWS losses are concentrated in coastal areas, snow- and ice-covered regions, and areas experiencing rapid temperature increases, highlighting climate change impacts. This study offers a comprehensive framework for exploring TWS variations using XAI and provides valuable insights into the mechanisms driving TWS changes on a global scale. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Water Resources Management)
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23 pages, 11792 KiB  
Article
Quantifying Long Term (2000–2020) Water Balances Across Nepal by Integrating Remote Sensing and an Ecohydrological Model
by Kailun Jin, Ning Liu, Run Tang, Ge Sun and Lu Hao
Remote Sens. 2025, 17(11), 1819; https://doi.org/10.3390/rs17111819 - 23 May 2025
Viewed by 651
Abstract
Nepal is known for its complex terrain, climate, and vegetation dynamics, resulting in tremendous hydrologic variability and complexity. Accurately quantifying the water balances at the national level in Nepal is extremely challenging and is currently not available. This study constructed long-term (2000–2022) water [...] Read more.
Nepal is known for its complex terrain, climate, and vegetation dynamics, resulting in tremendous hydrologic variability and complexity. Accurately quantifying the water balances at the national level in Nepal is extremely challenging and is currently not available. This study constructed long-term (2000–2022) water balances for 358 watersheds across Nepal by integrating watershed hydrometeorological monitoring data, remote sensing products including Leaf Area Index and land use and land cover data, with an existing ecohydrological model, Water Supply Stress Index (WaSSI). The WaSSI model’s performance is assessed at both watershed and national levels using observed water yield (Q) and evapotranspiration (ET) products derived from remote sensing (ETMonitor, PEW, SSEBop) and eddy flux network (i.e., FLUXCOM). We show that the WaSSI model captured the seasonal dynamics of ET and Q, providing new insights about climatic controls on ET and Q across Nepal. At the national scale, the simulated long-term (2000–2020) mean annual Q and ET was about half of the precipitation (1567 mm), but both Q and ET varied tremendously in space and time as influenced by a monsoon climate and mountainous terrain. We found that watersheds in the central Gandaki River basin had the highest Q (up to 1600 mm yr−1) and ET (up to 1000 mm yr−1). This study offers a validated ecohydrological modeling tool for the Himalaya region and a national benchmark dataset of the water balances for Nepal. These products are useful for quantitative assessment of ecosystem services and science-based watershed management at the national scale. Future studies are needed to improve the WaSSI model and remote sensing ET products by conducting ecohydrological research on key hydrologic processes (i.e., forest ET, streamflow generations of small watersheds) across physiographic gradients to better answer emerging questions about the impacts of environmental change in Nepal. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Water Resources Management)
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20 pages, 23461 KiB  
Article
Direct and Indirect Effects of Large-Scale Forest Restoration on Water Yield in China’s Large River Basins
by Yaoqi Zhang and Lu Hao
Remote Sens. 2025, 17(9), 1581; https://doi.org/10.3390/rs17091581 - 29 Apr 2025
Cited by 1 | Viewed by 447
Abstract
Emerging evidence indicates that large-scale forest restoration exhibits dual hydrological effects: direct reduction of local water availability through elevated evapotranspiration (ET) and indirect augmentation of water resources via enhanced atmospheric moisture recycling. However, the quantitative assessment of these counteracting effects remains challenging due [...] Read more.
Emerging evidence indicates that large-scale forest restoration exhibits dual hydrological effects: direct reduction of local water availability through elevated evapotranspiration (ET) and indirect augmentation of water resources via enhanced atmospheric moisture recycling. However, the quantitative assessment of these counteracting effects remains challenging due to the limited observational constraints on moisture transport. Here, we integrate the Budyko model with the Lagrangian-based UTrack moisture-tracking dataset to disentangle the direct (via ET) and indirect (via precipitation) large-scale hydrological impacts of China’s four-decade forest restoration campaign across eight major river basins. Multisource validation datasets, including gauged runoff records, hydrological reanalysis products, and satellite-derived forest cover maps, were systematically incorporated to verify the Budyko model at the nested spatial scales. Our scenario analyses reveal that during 1980–2015, extensive afforestation individually reduced China’s terrestrial water yield by −28 ± 25 mm yr−1 through dominant ET increases. Crucially, atmospheric moisture recycling mechanisms attenuated this water loss by 12 ± 5 mm yr−1 nationally, with marked spatial heterogeneity across the basins. In some moisture-limited watersheds in the Yellow River Basin, the negative ET effect was compensated for to a certain extent by precipitation recycling, demonstrating net positive hydrological outcomes. We conclude that China’s forest expansion imposes local water stress (direct effect) by elevating ET, while the concomitant strengthening of continental-scale moisture recycling generates compensatory water gains (indirect effect). These findings advance the mechanistic understanding of the vegetation-climate-water nexus, providing quantitative references for optimizing forestation strategies under atmospheric water connectivity constraints. Full article
(This article belongs to the Special Issue Remote Sensing Applications in Hydrology and Water Resources Management)
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