Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.6
4.1
Journals
Remote Sensing
Special Issues
Monitoring Terrestrial Water Resources Using Multiple Satellite Sensors (Second Edition)
remotesensing-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Monitoring Terrestrial Water Resources Using Multiple Satellite Sensors (Second Edition)

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: 15 November 2025 | Viewed by 2785

Special Issue Editors

Prof. Dr. Yao Li

E-Mail Website1 Website2
Guest Editor
School of Geographical Sciences, Southwest University, Chongqing, China
Interests: hydrological remote sensing; water resources management; ocean optics
Dr. Nan Xu

E-Mail Website
Guest Editor
School of Earth Sciences and Engineering, Hohai University, Nanjing, China
Interests: application of satellite technology in hydrology and ocean; multi-source remote sensing processing; coastal/inland water applications
Special Issues, Collections and Topics in MDPI journals
Dr. Yue Ma

E-Mail Website
Guest Editor
School of Electronic Information, Wuhan University, Wuhan, China
Interests: lidar signal modelling and system simulation; signal processing and calibration/validation; coastal applications for satellite laser altimetry
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Yi Ma

E-Mail Website
Guest Editor
Ministry of Land and Resources P.R.C., Qingdao, China
Interests: oceanographical remote sensing; active and passive remote sensing applications in island and coastal zone

Special Issue Information

Dear Colleagues,

In recent decades, climate change and population growth have increased global demands for water resources, especially in arid and densely populated regions. In order to better implement water resource management in the future, it is critical to accurately evaluate terrestrial water resources (such as lakes, reservoirs, and rivers) and track their changes over time. Satellite technology (such as satellite altimeters, gravity satellites, optical remote sensing, and microwave remote sensing) provides an unprecedented tool to quantitatively monitor terrestrial water resources from local to global scales.

In this Special Issue, our focus is on the pioneering applications of multi-satellite techniques for the detailed observation and management of terrestrial water resources across scales, from local to global. We are particularly interested in the integration of multiple satellite sensors to enhance the monitoring and assessment of water resources. Submissions to this issue may cover a wide range of topics, including, but not limited to, tracking lake/reservoir water levels and storage, monitoring river water levels and discharge, mapping shallow water bathymetry, and observing lake ice dynamics. This Special Issue aims to showcase the innovative use of multi-satellite strategies—especially the synergistic combination of different satellite sensors—in the fields of hydrology and limnology. We also invite submissions that present novel theories and methodologies in satellite technology applications for hydrology and related areas.

  • Monitoring surface water environments;
  • Assessing water resources and security;
  • Evaluating drought and flood risk;
  • Advancing water-related Sustainable Development Goals (SDGs);
  • Tracking lake/reservoir levels and storage;
  • Observing river levels and discharge;
  • Mapping the bathymetry of inland waters;
  • Surveying lake ice and snow cover;
  • Leveraging big data and machine learning in water resource monitoring;
  • Exploring other applications of satellite technology in hydrology and limnology.

Prof. Dr. Yao Li
Dr. Nan Xu
Dr. Yue Ma
Prof. Dr. Yi Ma
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 sensors
  • terrestrial water resources
  • lake/reservoir/river/wetland
  • coast and ocean
  • hydrology
  • drought/flooding risk
  • water environment and security
  • sustainable development goals

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Related Special Issue

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

26 pages, 6055 KiB  
Article
Assessment of Remote Sensing Reflectance Glint Correction Methods from Fixed Automated Above-Water Hyperspectral Radiometric Measurement in Highly Turbid Coastal Waters
by Behnaz Arabi, Masoud Moradi, Annelies Hommersom, Johan van der Molen and Leon Serre-Fredj
Remote Sens. 2025, 17(13), 2209; https://doi.org/10.3390/rs17132209 - 26 Jun 2025
Viewed by 63
Abstract
Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (Rrs(λ), sr−1). This study evaluates various above-water Rrs(λ) glint correction [...] Read more.
Fixed automated (unmanned) above-water radiometric measurements are subject to unavoidable sky conditions and surface perturbations, leading to significant uncertainties in retrieved water surface remote sensing reflectances (Rrs(λ), sr−1). This study evaluates various above-water Rrs(λ) glint correction methods using a comprehensive dataset collected at the Royal Netherlands Institute for Sea Research (NIOZ) Jetty Station located in the Marsdiep tidal inlet of the Dutch Wadden Sea, the Netherlands. The dataset includes in-situ water constituent concentrations (2006–2020), inherent optical properties (IOPs) (2006–2007), and above-water hyperspectral (ir)radiance observations collected every 10 min (2006–2023). The bio-optical models were validated using in-situ IOPs and utilized to generate glint-free remote sensing reflectances, Rrs,ref(λ), using a robust IOP-to-Rrs forward model. The Rrs,ref(λ) spectra were used as a benchmark to assess the accuracy of glint correction methods under various environmental conditions, including different sun positions, wind speeds, cloudiness, and aerosol loads. The results indicate that the three-component reflectance model (3C) outperforms other methods across all conditions, producing the highest percentage of high-quality Rrs(λ) spectra with minimal errors. Methods relying on fixed or lookup-table-based glint correction factors exhibited significant errors under overcast skies, high wind speeds, and varying aerosol optical thickness. The study highlights the critical importance of surface-reflected skylight corrections and wavelength-dependent glint estimations for accurate above-water Rrs(λ) retrievals. Two showcases on chlorophyll-a and total suspended matter retrieval further demonstrate the superiority of the 3C model in minimizing uncertainties. The findings highlight the importance of adaptable correction models that account for environmental variability to ensure accurate Rrs(λ) retrieval and reliable long-term water quality monitoring from hyperspectral radiometric measurements. Full article
(This article belongs to the Special Issue Monitoring Terrestrial Water Resources Using Multiple Satellite Sensors (Second Edition))
►▼ Show Figures

Figure 1

18 pages, 11579 KiB  
Article
Exploring the Most Effective Information for Satellite-Derived Bathymetry Models in Different Water Qualities
by Zhen Liu, Hao Liu, Yue Ma, Xin Ma, Jian Yang, Yang Jiang and Shaohui Li
Remote Sens. 2024, 16(13), 2371; https://doi.org/10.3390/rs16132371 - 28 Jun 2024
Cited by 2 | Viewed by 1652
Abstract
Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning [...] Read more.
Satellite-derived bathymetry (SDB) is an effective means of obtaining global shallow water depths. However, the effect of inherent optical properties (IOPs) on the accuracy of SDB under different water quality conditions has not been clearly clarified. To enhance the accuracy of machine learning SDB models, this study aims to assess the performance improvement of integrating the quasi-analytical algorithm (QAA)-derived IOPs using the Sentinel-2 and ICESat-2 datasets. In different water quality experiments, the results indicate that four SDB models (the Gaussian process regression, neural networks, random forests, and support vector regression) incorporating QAA-IOP parameters equal to or outperform those solely based on the remote sensing reflectance (Rrs) datasets, especially in turbid waters. By analyzing information gains in SDB, the most effective inputs are identified and prioritized under different water qualities. The SDB method incorporating QAA-IOP can achieve an accuracy of 0.85 m, 0.48 m, and 0.74 m in three areas (Wenchang, Laizhou Bay, and the Qilian Islands) with different water quality. Also, we find that incorporating an excessive number of redundant bands into machine learning models not only increases the demand of computing resources but also leads to worse accuracy in SDB. In conclusion, the integration of QAA-IOPs offers promising improvements in obtaining bathymetry and the optimal feature selection should be carefully considered in diverse aquatic environments. Full article
(This article belongs to the Special Issue Monitoring Terrestrial Water Resources Using Multiple Satellite Sensors (Second Edition))
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop