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Remote Sensing
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Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations
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Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations

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 August 2025 | Viewed by 868

Special Issue Editors

Dr. Jiashuang Jiao

E-Mail Website
Guest Editor
Chinese Antarctic Center of Surveying and Mapping, Wuhan University, Wuhan 430079, China
Interests: geodesy; global navigation satellite system; satellite gravity; mass change; crustal deformation; land water change; Tibetan Plateau; antarctica
Dr. Daocheng Yu

E-Mail Website
Guest Editor
School of Geomatics, Liaoning Technical University, Fuxin 123000, China
Interests: geodesy: marine gravity recovery from satellite altimetry
Prof. Dr. Cheinway Hwang

E-Mail Website
Guest Editor
Department of Civil Engineering, National Yang Ming Chiao Tung University, Taiwan 300093, China
Interests: geodesy; remote sensing; hydrology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With global climate change, extreme hydrological events such as droughts or heavy precipitation are occurring more frequently, which enhances the variability in the terrestrial water cycle process and affects the stability and sustainability of land water resources. Accurately monitoring and predicting changes in terrestrial water elements, such as the total land water storage, lake, reservoir, river, glacier, snow, soil moisture, and groundwater, not only helps to understand the response mechanism of land water dynamics to global climate change, but is also crucial for the formulation of global to regional water resource management and adaptive policies. In recent years, advances in geodetic techniques, such as global navigation satellite systems, satellite gravimetry, satellite altimetry, and synthetic aperture radars, together with the development of machine learning techniques, have brought new challenges for multisource geodetic data processing and new development opportunities for advancing hydrological monitoring and prediction.

The objective of this Special Issue is to present reviews and recent advances of general interest that make use of geodetic techniques in water resources research. Manuscripts on all aspects related to multisource geodetic techniques for higher precision, higher resolution, or higher reliability of hydrological monitoring and prediction are welcome. Paper topics may include, but are not limited to, the following:

  • Multisource geodetic data fusion for higher-precision land water storage monitoring;
  • Validation of geodetic technique-based land water storage monitoring;
  • Machine learning techniques for higher-resolution land water storage monitoring;
  • Joint use of geodetic and machine learning techniques for land water change predictions;
  • Monitoring and prediction of coastal water level changes using geodetic observations;
  • Impacts of extreme hydrological events on geodetic observations.

Dr. Jiashuang Jiao
Dr. Daocheng Yu
Prof. Dr. Cheinway Hwang
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

  • satellite geodesy
  • hydrological geodesy
  • land water storage
  • joint inversion
  • machine learning
  • hydrological monitoring and predication

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Published Papers (2 papers)

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Research

17 pages, 21498 KiB  
Article
Multi-Year Global Oscillations in GNSS Deformation and Surface Loading Contributions
by Songyun Wang, Clark R. Wilson, Jianli Chen, Yuning Fu, Weijia Kuang and Ki-Weon Seo
Remote Sens. 2025, 17(9), 1509; https://doi.org/10.3390/rs17091509 - 24 Apr 2025
Viewed by 199
Abstract
Recent studies have identified a near six-year oscillation (SYO) in Global Navigation Satellite Systems (GNSS) surface displacements, with a degree 2, order 2 spherical harmonic (SH) pattern and retrograde motion. The cause is uncertain, with proposals ranging from deep Earth to near-surface sources. [...] Read more.
Recent studies have identified a near six-year oscillation (SYO) in Global Navigation Satellite Systems (GNSS) surface displacements, with a degree 2, order 2 spherical harmonic (SH) pattern and retrograde motion. The cause is uncertain, with proposals ranging from deep Earth to near-surface sources. This study investigates the SYO and possible causes from surface loading. Considering the irregular spatiotemporal distribution of GNSS data and the variety of contributors to surface displacements, we used synthetic experiments to identify optimal techniques for estimating low degree SH patterns. We confirm a reported retrograde SH degree 2, order 2 displacement using GNSS data from the same 35 stations used in a previous study for the 1995–2015 period. We also note that its amplitude diminished when the time span of observations was extended to 2023, and the retrograde dominance became less significant using a larger 271-station set. Surface loading estimates showed that terrestrial water storage (TWS) loads contributed much more to the GNSS degree 2, order 2 SYO, than atmospheric and oceanic loads, but TWS load estimates were highly variable. Four TWS sources—European Centre for Medium-Range Weather Forecasts Reanalysis 5 (ERA5), Modern-Era Retrospective analysis for Research and Applications (MERRA), Global Land Data Assimilation System (GLDAS), and Gravity Recovery and Climate Experiment (GRACE/GRACE Follow-On)—yielded a wide range (24% to 93%) of predicted TWS contributions with GRACE/GRACE Follow-On being the largest. This suggests that TWS may be largely responsible for SYO variations in GNSS observations. Variations in SYO GNSS amplitudes in the extended period (1995–2023) were also consistent with near surface sources. Full article
(This article belongs to the Special Issue Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations)
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22 pages, 9142 KiB  
Article
Downscaling and Gap-Filling GRACE-Based Terrestrial Water Storage Anomalies in the Qinghai–Tibet Plateau Using Deep Learning and Multi-Source Data
by Jun Chen, Linsong Wang, Chao Chen and Zhenran Peng
Remote Sens. 2025, 17(8), 1333; https://doi.org/10.3390/rs17081333 - 8 Apr 2025
Viewed by 390
Abstract
The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) [...] Read more.
The Qinghai–Tibet Plateau (QTP), a critical hydrological regulator for Asia through its extensive glacier systems, high-altitude lakes, and intricate network of rivers, exhibits amplified sensitivity to climate-driven alterations in precipitation regimes and ice mass balance. While the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On (GRACE-FO) missions have revolutionized monitoring of terrestrial water storage anomalies (TWSAs) across this hydrologically sensitive region, spatial resolution limitations (3°, equivalent to ~300 km) constrain process-scale analysis, compounded by mission temporal discontinuity (data gaps). In this study, we present a novel downscaling framework integrating temporal gap compensation and spatial refinement to a 0.25° resolution through Gated Recurrent Unit (GRU) neural networks, an architecture optimized for univariate time series modeling. Through the assimilation of multi-source hydrological parameters (glacier mass flux, cryosphere–precipitation interactions, and land surface processes), the GRU-based result resolves nonlinear storage dynamics while bridging inter-mission observational gaps. Grid-level implementation preserves mass conservation principles across heterogeneous topographies, successfully reconstructing seasonal-to-interannual TWSA variability and also its long-term trends. Comparative validation against GRACE mascon solutions and process-based hydrological models demonstrates enhanced capacity in resolving sub-basin heterogeneity. This GRU-derived high-resolution TWSA is especially valuable for dissecting local variability in areas such as the Brahmaputra Basin, where complex water cycling can affect downstream water security. Our study provides transferable methodologies for mountainous hydrogeodesy analysis under evolving climate regimes. Future enhancements through physics-informed deep learning and next-generation climatology–hydrology–gravimetry synergy (e.g., observations and models) could further constrain uncertainties in extreme elevation zones, advancing the predictive understanding of Asia’s water tower sustainability. Full article
(This article belongs to the Special Issue Advancing Hydrological Monitoring and Prediction Through Multisource Geodetic Observations)
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