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Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms

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

Deadline for manuscript submissions: 25 December 2025 | Viewed by 1096

Special Issue Editors


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Guest Editor
Department of Electrical, Electronic and Robotic Engineering, School of Engineering, Faculty of Engineering, Computing and the Environment, Kingston University, London KT1 2EE, UK
Interests: microwave sensing; nondestructive testing; planet exploration; ground penetrating radar; ground-based synthetic aperture radar; environmental monitoring; sustainability

Special Issue Information

Dear Colleagues,

The snow–ice–water system represents a complex and interconnected natural phenomenon. Historically, studies on snow, ice, and water in winter have been conducted in isolation across various disciplines. However, recent advancements in cryosphere science and technology have enabled a more integrated research approach. The interplay between ice and water is governed by critical phenomena, including phase transitions that drive dynamic energy and mass exchanges. These processes are further linked to ecosystems and water quality, fostering unique ecological systems beneath ice covers. Thermodynamic processes in cold-region hydrology are fundamentally shaped by the interactions between snow-ice and ice-water. These interactions often exhibit complex coupled behaviours, influencing energy and nutrient exchange while driving physical, chemical, and ecological processes. Consequently, multidisciplinary investigations are essential to unravel these dynamics. Remote sensing has emerged as a powerful tool for regional and large-scale monitoring, facilitating the study of interactions among the atmosphere, snow, ice, and water. Integrated space–air–ground(ice) remote sensing is particularly valuable in remote rural areas or during high-risk ice periods when on-site human investigations are challenging.

We are pleased to announce a Special Issue titled "Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms" in Remote Sensing. This Special Issue aims to showcase the latest advancements in space-air-ground(ice) remote sensing for studying snow, ice, and water in rivers and lakes. It provides a platform for researchers to share novel findings, methodologies, and insights. We invite contributions across a broad spectrum of topics, including theoretical studies, case analyses, field investigations, data-driven approaches, numerical modelling, and comprehensive reviews.

We welcome submissions on the following topics:

(a) Ice-water phase transition processes, phase transition-influenced properties (e.g., thermal, optical, and electrical), water ecosystems and quality under ice, and remote sensing algorithms for snow/ice/water parameters using multi-sensor and multi-source data.

(b) Field measurements integrating remote sensing for snow, ice, and water research.

(c) Interdisciplinary research on the snow-ice-water system, combining remote sensing techniques for meteorology, hydrology, and ecology.

(d) Assessments of snow, ice, and water interactions in relation to human activities, such as water resource management, tourism, and natural disasters.

(e) Other relevant studies on snow, ice and water in cold regions through remote technology.

Prof. Dr. Zhijun Li
Dr. Lilong Zou
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

  • river ice
  • lake ice
  • ice/snow properties
  • water quality
  • water ecosystem
  • remote sensing
  • remote sensing applications
  • multi-sensor and multi-source data
  • climate change
  • resources assessment
  • machine learning

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Published Papers (2 papers)

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Research

24 pages, 9586 KB  
Article
Optimized Recognition Algorithm for Remotely Sensed Sea Ice in Polar Ship Path Planning
by Li Zhou, Runxin Xu, Jiayi Bian, Shifeng Ding, Sen Han and Roger Skjetne
Remote Sens. 2025, 17(19), 3359; https://doi.org/10.3390/rs17193359 - 4 Oct 2025
Viewed by 297
Abstract
Collisions between ships and sea ice pose a significant threat to maritime safety, making it essential to detect sea ice and perform safety-oriented path planning for polar navigation. This paper utilizes an optimized You Only Look Once version 5 (YOLOv5) model, designated as [...] Read more.
Collisions between ships and sea ice pose a significant threat to maritime safety, making it essential to detect sea ice and perform safety-oriented path planning for polar navigation. This paper utilizes an optimized You Only Look Once version 5 (YOLOv5) model, designated as YOLOv5-ICE, for the detection of sea ice in satellite imagery, with the resultant detection data being employed to input obstacle coordinates into a ship path planning system. The enhancements include the Squeeze-and-Excitation (SE) attention mechanism, improved spatial pyramid pooling, and the Flexible ReLU (FReLU) activation function. The improved YOLOv5-ICE shows enhanced performance, with its mAP increasing by 3.5% compared to the baseline YOLOv5 and also by 1.3% compared to YOLOv8. YOLOv5-ICE demonstrates robust performance in detecting small sea ice targets within large-scale satellite images and excels in high ice concentration regions. For path planning, the Any-Angle Path Planning on Grids algorithm is applied to simulate routes based on detected sea ice floes. The objective function incorporates the path length, number of ship turns, and sea ice risk value, enabling path planning under varying ice concentrations. By integrating detection and path planning, this work proposes a novel method to enhance navigational safety in polar regions. Full article
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20 pages, 6543 KB  
Article
Study of Antarctic Sea Ice Based on Shipborne Camera Images and Deep Learning Method
by Xiaodong Chen, Shaoping Guo, Qiguang Chen, Xiaodong Chen and Shunying Ji
Remote Sens. 2025, 17(15), 2685; https://doi.org/10.3390/rs17152685 - 3 Aug 2025
Viewed by 511
Abstract
Sea ice parameters are crucial for polar ship design. During China’s 39th Antarctic Scientific Expedition, ice condition from the entire navigation process of the research vessel Xuelong 2 was recorded using shipborne cameras. To obtain sea ice parameters, two deep learning models, Ice-Deeplab [...] Read more.
Sea ice parameters are crucial for polar ship design. During China’s 39th Antarctic Scientific Expedition, ice condition from the entire navigation process of the research vessel Xuelong 2 was recorded using shipborne cameras. To obtain sea ice parameters, two deep learning models, Ice-Deeplab and U-Net, were employed to automatically obtain sea ice concentration (SIC) and sea ice thickness (SIT), providing high-frequency data at 5-min intervals. During the observation period, ice navigation accounted for 32 days, constituting less than 20% of the total 163 voyage days. Notably, 63% of the navigation was in ice fields with less than 10% concentration, while only 18.9% occurred in packed ice (concentration > 90%) or level ice regions. SIT ranges from 100 cm to 234 cm and follows a normal distribution. The results demonstrate that, to achieve enhanced navigation efficiency and fulfill expedition objectives, the research vessel substantially reduced duration in high-concentration ice areas. Additionally, the results of SIC extracted from shipborne camera images were compared with the data from the Copernicus Marine Environment Monitoring Service (CMEMS) satellite remote sensing. In summary, the sea ice parameter data obtained from shipborne camera images offer high spatial and temporal resolution, making them more suitable for engineering applications in establishing sea ice environmental parameters. Full article
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