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

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

Deadline for manuscript submissions: 30 June 2026 | Viewed by 5377

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Special Issue Editors

Prof. Dr. Zhijun Li

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Guest Editor

State Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, China
Interests: ice physical and mechanical properties; ice engineering; polar sciences and technology; ecosystem under ice; physical modeling
Special Issues, Collections and Topics in MDPI journals

Dr. Lilong Zou

E-Mail Website
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 250 words) can be sent to the Editorial Office for assessment.

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 (7 papers)

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Research

19 pages, 11015 KB

Open AccessArticle

Analysis of Influencing Factors on Phytoplankton Primary Productivity Across Ice-Free and Ice-Covered Seasons Through Remote Sensing and Optical Parameter Correction

by Haifeng Yu, Yongfeng Ren, Yuhan Gao, Biao Sun and Xiaohong Shi

Remote Sens. 2026, 18(9), 1309; https://doi.org/10.3390/rs18091309 - 24 Apr 2026

Viewed by 181

Abstract

The primary productivity of phytoplankton (PP_eu) is critical to the carbon cycle in aquatic ecosystems. However, in complex lakes covered by ice, the estimation of PP_eu using remote sensing techniques is constrained. To address this limitation, this study developed an estimation model for ice-covered PP_eu by incorporating optical parameters such as the ice surface refractive index and the extinction coefficient of the ice layer into the vertical generalized production model (VGPM). This approach overcomes the challenges associated with remote sensing-based estimation of PP_eu during ice-covered periods. The results indicate that the annual carbon sequestration of the WLSHL is 1.72 × 10⁴ t C, with an average annual PP_eu of 316.96 mg C·m⁻²·d⁻¹. In addition to the indicators that are directly involved in the estimation of PP_eu, the environmental factors that affect PP_eu include water temperature (WT), ice thickness (IT), snow, water depth (D), total dissolved solids (TDSs), salinity (S), ammonia nitrogen (NH₄⁺-N), nitrate nitrogen (NO₃⁻-N), and oxidation–reduction potential (ORP). The PP_eu in the ice period is found to be only 17% lower than that in the ice-free period. However, the PP_eu during the ice period is considerably higher than that during the ice + snow period. The findings indicate that the impact of freezing on PP_eu during the winter is relatively limited, whereas the influence of snowfall is more pronounced. In order to mitigate the elevated PP_eu and the occurrence of algal blooms during the summer, the intensity of underwater radiation can be regulated on a periodic basis. To optimize the function of the carbon sink in winter lakes, the PP_eu can be enhanced through initiatives such as water replenishment prior to freezing and snow removal following freezing. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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25 pages, 15027 KB

Open AccessArticle

Characterization of Local and Long-Distance Ice Floe Motion in the Yellow River Using UAV–GPS Joint Observations

by Chunjiang Li, Jiaqi Dai, Yupeng Leng, Xiaohua Hao, Weiping Li, Shamshodbek Akmalov, Xiangqian Li, Zhichao Wang, Han Gao, Xiang Fu, Shengbo Hu and Yu Zheng

Remote Sens. 2026, 18(5), 823; https://doi.org/10.3390/rs18050823 - 6 Mar 2026

Viewed by 392

Abstract

Understanding the motion parameters of floating ice is very important for characterizing the ice water dynamics of rivers during freezing periods. Due to the low spatiotemporal resolution of satellite images, limited observation range of unmanned aerial vehicles, and deformation of shore-based camera images, it is difficult to simultaneously quantify the translational and rotational motion characteristics of floating ice and long-distance transportation. This study used the unmanned aerial vehicle GPS joint observation method to observe and obtain various motion parameters such as local translation, rotation, and long-distance transportation in the curved section of the upper reaches of the Yellow River and the straight section of the middle reaches of the Yellow River during the winter of 2024–2025 under conditions of ice density of 50–90%. The velocity field obtained by the drone shows an average ice velocity of 1.27 m/s at the bend and 1.18 m/s in the straight section, with lateral velocity gradients of −0.245 to 0.050 s⁻¹ and −0.141 to 0.222 s⁻¹, respectively. The angular velocity of a single floating ice block is 0.008–0.016 rad/s at bends and 0.010–0.036 rad/s in straight sections. The angular velocity is positively correlated with the local shear strength, and the rotation direction is consistent with the sign of the velocity gradient. GPS tracking provides long-distance transportation trajectories, and the average difference between the speeds obtained by GPS and drones is 0.10 m/s, confirming the reliability of speed estimation based on drones. These results indicate that integrated unmanned aerial vehicle GPS observation can quantitatively characterize local floating ice movement and long-distance floating ice transport behavior, providing on-site parameters for river ice water dynamics research and hazard assessment, and has the potential to be applied to rivers in other cold regions. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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18 pages, 2825 KB

Open AccessArticle

Detailed Classification of River Ice Types Using Sentinel-2 Imagery: A Case Study of the Inner Mongolia Reach of Yellow River

by Yupeng Leng, Chunjiang Li, Peng Lu, Xiaohua Hao, Xiangqian Li, Shamshodbek Akmalov, Xiang Fu, Shengbo Hu and Yu Zheng

Remote Sens. 2026, 18(5), 672; https://doi.org/10.3390/rs18050672 - 24 Feb 2026

Viewed by 501

Abstract

Due to the complexity inherent in river ice dynamics, the utilization of remote sensing imagery represents the most crucial and effective method currently available for monitoring changes in river ice. In the Inner Mongolia segment of the Yellow River during winter, two distinct types of ice surfaces are observed: juxtaposed ice and consolidated ice. Additionally, certain areas of open water remain unfrozen. Rapid identification and classification of extensive ice formations and open water zones along this lengthy river section constitute critical information for informed decision-making in ice prevention and management strategies within the Yellow River basin. This paper takes the formation and characteristic analysis of different types of ice in the Yellow River channels in Inner Mongolia as the starting point. It employs a support vector machine (SVM) as the classifier and introduces an optimized model for classifying river ice types using high-resolution Sentinel-2 optical imagery. The model utilizes multi-band spectral features, along with multi-spectral fusion indices such as the normalized difference snow index (NDSI) and the normalized difference frozen surface index (NDFSI), as feature vectors. This approach attains an overall accuracy of 94.91% in classifying different types of ice and can significantly contribute to river ice monitoring by offering robust theoretical support. In the winter of 2023–2024, the proportion of juxtaposed ice on the Yellow River section in Inner Mongolia changed from 45% to 55%, the proportion of consolidated ice changed from 30% to 40%, and the proportion of open water changed from 9% to 19%. This research investigates the characteristics of river ice formations and develops a classification methodology for river ice patterns utilizing high-resolution Sentinel-2 imagery in conjunction with a supervised classification algorithm. The findings of this study are intended to offer technical support for the expedited interpretation of ice conditions in the Yellow River, thereby serving as a scientific basis for precise monitoring and effective disaster prevention and management related to river ice phenomena. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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30 pages, 11104 KB

Open AccessArticle

Monitoring Oxbow Lakes with Remote Sensing: Insights into Turbidity, Connectivity, and Fish Habitat

by Lina G. Terrazas-Villarroel, Jochen Wenninger, Marcelo Heredia-Gómez, Nick van de Giesen and Michael E. McClain

Remote Sens. 2026, 18(3), 474; https://doi.org/10.3390/rs18030474 - 2 Feb 2026

Cited by 1 | Viewed by 707

Abstract

In meandering river floodplain systems, remote sensing is a valuable tool for assessing connectivity processes relevant to fish ecological functions. This study used the Google Earth Engine platform and multispectral Landsat 7 imagery. A random forest classifier was used to evaluate water types and area changes in oxbow lakes of the Beni River in Bolivia. Water type dynamics were mainly associated with lake age and distance from the main channel. Seasonal variations highlighted the role of wind-driven sediment resuspension and overflow during high discharge conditions. Long-term lake area changes reflected typical oxbow lake evolution as well as alterations caused by the main channel. Multiannual changes showed a notable area decrease during years of low discharge. Relationships between discharge and lake area dynamics allowed the classification of three lake groups with different levels of connectivity and overbank flow influence. The ecological relevance of these groups was evaluated based on fish habitat preferences and migration patterns. Results emphasize the importance of preserving natural hydrologic variability, with flooding associated with increased habitat availability. Overall, this study demonstrates the usefulness of satellite remote sensing for detecting ecohydrological processes and offers insights to preserve ecological functions in data-scarce regions. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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26 pages, 5049 KB

Open AccessArticle

Spatiotemporal Dynamics and Drivers of Potential Winter Ice Resources in China (1990–2020) Using Multi-Source Remote Sensing and Machine Learning

by Donghui Shi

Remote Sens. 2026, 18(2), 250; https://doi.org/10.3390/rs18020250 - 13 Jan 2026

Viewed by 363

Abstract

River and lake ice are sensitive indicators of climate change and important components of hydrological and ecological systems in cold regions. In this study, we develop a simple and transferable “surface water + land surface temperature (LST)” framework on Google Earth Engine to map potential winter ice area across China from 1990 to 2020. The framework enables consistent, large-scale, long-term monitoring without relying on complex remote sensing models or region-specific thresholds. Our results show that, despite a pronounced northwestward shift in the freezing-zone boundary, more than 400 km in the Northeast Plain and about 13 km per year along the eastern coast, the total ice-covered area increased by approximately 1.1% per year. At the same time, the average ice season became slightly shorter. This indicates asynchronous spatial and temporal responses of potential winter ice to warming. We identify a persistent “Northwest–Northeast dual-core” spatial pattern with strong positive spatial autocorrelation, characterized by increasing ice cover in Tibet, Qinghai, Xinjiang, Inner Mongolia, and Northeast China, and decreasing ice cover mainly in Beijing and Yunnan, where intense urbanization and low-latitude warming dominate. Random Forest modeling further shows that water area fraction, nighttime lights, built-up area, altitude, and water–heat indices are the main controls on potential winter ice. These findings highlight the combined influence of hydrological and thermal conditions and urbanization in reshaping potential winter ice patterns under climate change. Full article

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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24 pages, 9586 KB

Open AccessArticle

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

Cited by 1 | Viewed by 1162

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 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

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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20 pages, 6543 KB

Open AccessArticle

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 1238

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 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

(This article belongs to the Special Issue Remote Sensing of River and Lake Ice/Water Using Spaceborne, Airborne, and Ground Platforms)

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