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Remote Sensing
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Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes
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Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 4486

Special Issue Editors

Prof. Dr. Shiqiang Zhang

E-Mail Website
Guest Editor
1. Shanxi Key Laboratory of Earth Surface System and Environmental Carrying Capacity, Northwest University, Xi’an 710127, China
2. College of Urban and Environmental Science, Northwest University, Xi’an 710127, China
Interests: remote sensing and GIS application; hydrological models
Special Issues, Collections and Topics in MDPI journals
Dr. Yun Chen

E-Mail Website
Guest Editor
CSIRO Land and Water, Canberra, ACT 2601, Australia
Interests: hydro-geoinformatics; flood inundation modelling; drought modelling
Special Issues, Collections and Topics in MDPI journals
Dr. Qiudong Zhao

E-Mail Website
Guest Editor
Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, China
Interests: remote sensing application; hydrological models
Dr. Chang Huang

E-Mail Website
Guest Editor
School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Interests: hydrological remote sensing; flood inundation modelling; water resources management; climate change
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Remote sensing technologies have revolutionized the way we observe and understand the Earth's surface and its dynamic processes and satellite, airborne, and ground-based remote sensing platforms offer the ability to collect spatially extensive, temporally frequent, and multispectral data over large geographic areas. These datasets provide valuable information on a wide range of hydrological and geophysical variables, including precipitation, soil moisture, surface water dynamics, land cover/land use, topography, and geophysical properties. In the fields of hydrology and geophysics, the integration of multi-source remote sensing data has emerged as a powerful tool for advancing our understanding of complex environmental phenomena and informing sustainable resource management practices.

Research focused on the integration of multi-source remote sensing data in hydrology and geophysical processes is critical for advancing our understanding of Earth's dynamic systems and addressing pressing societal and environmental challenges. By leveraging the wealth of information provided by remote sensing technologies, researchers can contribute to improved water resource management, enhanced disaster resilience, and informed decision-making for sustainable development. This fits well with the scope of the journal.

We invite contributions covering a wide range of topics related to the integration of multi-source remote sensing data in hydrology and geophysical processes including, but not limited to:

  • The integration of satellite, airborne, and ground-based remote sensing data for hydrological modelling and analysis;
  • Applications of multi-source remote sensing data in surface water and groundwater monitoring, assessment, and management;
  • The utilization of remote sensing techniques for studying soil moisture dynamics, land surface processes, and land–atmosphere interactions;
  • Remote sensing-based approaches to monitoring and modelling hydrological extremes such as floods, droughts, and landslides;
  • The fusion of remote sensing data with in situ observations and numerical models to improve the understanding of geophysical processes;
  • Innovative methods for the retrieval and assimilation of multi-source remote sensing data for hydrological and geophysical models;
  • Remote sensing-based studies on the impacts of climate change and land use/land cover change on hydrological and geophysical processes;
  • Applications of advanced remote sensing technologies (e.g., SAR, LiDAR, hyperspectral imaging) in hydrology and geophysics;
  • The integration of remote sensing data with machine learning and data-driven approaches for the enhanced understanding and prediction of processes;
  • Case studies and applications showcasing the practical implications of multi-source remote sensing data in hydrological and geophysical research and management.

Prof. Dr. Shiqiang Zhang
Dr. Yun Chen
Dr. Qiudong Zhao
Dr. Chang Huang
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

  • hydrologic variables
  • data fusion
  • surface water
  • glaciers
  • snow cover
  • hydrological process
  • flood monitoring

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Published Papers (5 papers)

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37 pages, 8385 KiB  
Article
Reconstruction of Effective Cross-Sections from DEMs and Water Surface Elevation
by Isadora Rezende, Christophe Fatras, Hind Oubanas, Igor Gejadze, Pierre-Olivier Malaterre, Santiago Peña-Luque and Alessio Domeneghetti
Remote Sens. 2025, 17(6), 1020; https://doi.org/10.3390/rs17061020 - 14 Mar 2025
Viewed by 253
Abstract
Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define [...] Read more.
Knowledge of river bathymetry is crucial for accurately simulating river flows and floodplain inundation. However, field data are scarce, and the depth and shape of the river channels cannot be systematically observed via remote sensing. Therefore, an efficient methodology is necessary to define effective river bathymetry. This research reconstructs the bathymetry from existing global digital elevation models (DEMs) and water surface elevation observations with minimum human intervention. The methodology can be considered a 1D geometric inverse problem, and it can potentially be used in gauged or ungauged basins worldwide. Nine global DEMs and two sources of water surface elevation (in situ and remotely sensed) were analyzed across two study areas. Results highlighted the importance of preprocessing cross-sections to align with water surface elevations, significantly improving discharge estimates. Among the techniques tested, one that combines the slope-break concept with the principles of mass conservation consistently provided robust discharge estimates for the different DEMs, achieving good performance in both study areas. Copernicus and FABDEM emerged as the most reliable DEMs for accurately representing river geometry. Overall, the proposed methodology offers a scalable and efficient solution for cross-section reconstruction, supporting global hydraulic modeling in data-scarce regions. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes)
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21 pages, 10570 KiB  
Article
Spatial and Temporal Variations in Soil Organic Carbon in Northwestern China via Comparisons of Different Methods
by Jinlin Li, Ning Hu, Yuxin Qi, Wenzhi Zhao and Qiqi Dong
Remote Sens. 2025, 17(3), 420; https://doi.org/10.3390/rs17030420 - 26 Jan 2025
Viewed by 595
Abstract
Soil organic carbon (SOC) is a crucial component for investigating carbon cycling and global climate change. Accurate data exhibiting the temporal and spatial distributions of SOC are very important for determining the soil carbon sequestration potential and formulating climate strategies. An important scheme [...] Read more.
Soil organic carbon (SOC) is a crucial component for investigating carbon cycling and global climate change. Accurate data exhibiting the temporal and spatial distributions of SOC are very important for determining the soil carbon sequestration potential and formulating climate strategies. An important scheme of mapping SOC is to establish a link between environmental factors and SOC via different methods. The Shiyang River Basin is the third largest inland river basin in the Hexi Corridor, which has closed geographical conditions and a relatively independent carbon cycle system, making it an ideal area for carbon cycle research in arid areas. In this study, 65 SOC samples were collected and 21 environmental factors were assessed from 2011 to 2021 in the Shiyang River Basin. The linear regression (LR) method and two machine learning methods, i.e., support vector machine regression (SVR) and random forest (RF), are applied to estimate the spatial distribution of SOC. RF is slightly better than SVR because of its advantages in the comparison of classification. When latitude, slope, and the normalized vegetation index (NDVI) are used as predictor variables, the best SOC performance is shown. Compared with the Harmonized World Soil Database (HWSD), the optimal scheme improved the accuracy of the SOC significantly. Finally, the spatial distribution of SOC tended to increase, with a total increase of 135.94 g/kg across the whole basin. The northwestern part of the middle basin decreased by 2.82% because of industrial activities. The SOC in Minqin County increased by approximately 62.77% from 2011 to 2021. Thus, the variability of the spatial SOC increased. This study provides a theoretical basis for the spatial and temporal distributions of SOC in inland river basins. In addition, this study can also provide effective and scientific suggestions for carbon projects, offer a key scientific basis for understanding the carbon cycle, and support global climate change adaptation and mitigation strategies. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes)
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20 pages, 20184 KiB  
Article
Snow Cover Extraction from Landsat 8 OLI Based on Deep Learning with Cross-Scale Edge-Aware and Attention Mechanism
by Zehao Yu, Hanying Gong, Shiqiang Zhang and Wei Wang
Remote Sens. 2024, 16(18), 3430; https://doi.org/10.3390/rs16183430 - 15 Sep 2024
Viewed by 1492
Abstract
Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this [...] Read more.
Snow cover distribution is of great significance for climate change and water resource management. Current deep learning-based methods for extracting snow cover from remote sensing images face challenges such as insufficient local detail awareness and inadequate utilization of global semantic information. In this study, a snow cover extraction algorithm integrating cross-scale edge perception and an attention mechanism on the U-net model architecture is proposed. The cross-scale edge perception module replaces the original jump connection of U-net, enhances the low-level image features by introducing edge detection on the shallow feature scale, and enhances the detail perception via branch separation and fusion features on the deep feature scale. Meanwhile, parallel channel and spatial attention mechanisms are introduced in the model encoding stage to adaptively enhance the model’s attention to key features and improve the efficiency of utilizing global semantic information. The method was evaluated on the publicly available CSWV_S6 optical remote sensing dataset, and the accuracy of 98.14% indicates that the method has significant advantages over existing methods. Snow extraction from Landsat 8 OLI images of the upper reaches of the Irtysh River was achieved with satisfactory accuracy rates of 95.57% (using two, three, and four bands) and 96.65% (using two, three, four, and six bands), indicating its strong potential for automated snow cover extraction over larger areas. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes)
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20 pages, 10187 KiB  
Article
Finding Oasis Cold Island Footprints Based on a Logistic Model—A Case Study in the Ejina Oasis
by Wentong Wu and Rensheng Chen
Remote Sens. 2024, 16(16), 2895; https://doi.org/10.3390/rs16162895 - 8 Aug 2024
Viewed by 1238
Abstract
Oases play a crucial role in arid regions within the human–environmental system, holding significant ecological and biological importance. The Oasis Cold Island Effect (OCIE) represents a distinct climatic feature of oases and serves as a vital metric for assessing oasis ecosystems. Previous studies [...] Read more.
Oases play a crucial role in arid regions within the human–environmental system, holding significant ecological and biological importance. The Oasis Cold Island Effect (OCIE) represents a distinct climatic feature of oases and serves as a vital metric for assessing oasis ecosystems. Previous studies have overlooked the spatial extent of the Oasis Cold Island Effect (OCIE), specifically the boundary delineating areas influenced and unaffected by oases. This boundary is defined as the Oasis Cold Island Footprint (OCI FP). Utilizing Logistic modeling and MODIS data products, OCI FPs were calculated for the Ejina Oasis from 2000 to 2019. The assessment results underscore the accuracy and feasibility of the methodology, indicating its potential applicability to other oases. Spatial and temporal distributions of OCI FPs and the intensity of the Oasis Cold Island Effect Intensity (OCIEI) in the Ejina Oasis were analyzed, yielding the following findings: (1) OCI FP area and complexity were smallest in summer and largest in autumn. (2) Over the period 2000–2019, OCI FPs exhibited a pattern of increase, decrease, and subsequent increase. (3) OCIEI peaks in summer and reaches its lowest point in winter. Lastly, the study addresses current limitations and outlines future research objectives. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes)
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16 pages, 7343 KiB  
Technical Note
Two-Stage Evapotranspiration Partitioning Under the Generalized Proportionality Hypothesis Based on the Interannual Relationship Between Precipitation and Runoff
by Changwu Cheng, Wenzhao Liu, Rui Chen, Zhaotao Mu and Xiaoyang Han
Remote Sens. 2025, 17(7), 1203; https://doi.org/10.3390/rs17071203 - 28 Mar 2025
Viewed by 176
Abstract
The generalized proportionality hypothesis (GPH) highlights the competitive relationships among hydrological components as precipitation (P) transforms into runoff (Q) and evapotranspiration (E), providing a novel perspective on E partitioning that differs from the traditional physical source-based approach. To achieve sequential partitioning of E [...] Read more.
The generalized proportionality hypothesis (GPH) highlights the competitive relationships among hydrological components as precipitation (P) transforms into runoff (Q) and evapotranspiration (E), providing a novel perspective on E partitioning that differs from the traditional physical source-based approach. To achieve sequential partitioning of E into initial (Ei) and continuing (Ec) evapotranspiration under the GPH, a P-Q relationship-based Ei estimation method was proposed for the Model Parameter Estimation Experiment (MOPEX) catchments. On this basis, we analyzed the relationship between the GPH-based E components and the physical source-based ones separated by the Penman-Monteith-Mu algorithm. Additionally, we explored the differences between the calculated and inverse Budyko-WT model parameter (Ei/E) and discussed the implications for the Budyko framework. The results showed the following: (1) A significant linear P-Q relationship (p < 0.05) prevailed in the MOPEX catchments, providing a robust data foundation for Ei estimation. Across the MOPEX catchments, Ei and Ec contributed 73% and 27% of total E, respectively. (2) The combined proportion of evaporation from canopy interception and wet soil averaged about 25%, and it was much lower than that of Ei, indicating that it was difficult to establish a connection between Ei and the physical source-based E components. (3) The potential evapotranspiration (EP) satisfying the Budyko-WT model was strictly constrained by the GPH, while the inappropriate EP estimation method largely explained the discrepancy between the calculated and inverse Ei/E. This study deepens the knowledge of the sequential partitioning of E components, uncovers the discrepancies between different E partitioning frameworks, and provides new insights into the characterization of key variables in Budyko models. Full article
(This article belongs to the Special Issue Multi-Source Remote Sensing Data in Hydrology and Geophysical Processes)
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