Review of River Ice Observation and Data Analysis Technologies
Abstract
:1. Introduction
1.1. Background Information
1.2. Importance of River Ice Information
1.3. Objective
1.4. Paper Structure
2. Literature Review Methodology and Inventory of Observations
- Committee on River Ice Processes and the Environment (CRIPE) (Workshop on the Hydraulics of Ice Covered Rivers) and
- The Symposium on Ice, under the umbrella of the Ice Research and Engineering Committee of International Association for Hydro-Environment Engineering and Research (IAHR).
- -
- Publication year.
- -
- Satellite name.
- -
- Sensor name (important for distinguishing between satellites with multiple sensors).
- -
- Band: electromagnetic wave characteristic (band or combination of bands, polarization).
- -
- Sensor resolution in meters (some publications may use pixel spacing (PS)).
- -
- Ice information (refer to Section 1.2).
- -
- Retrieval method: describes the approach(es) utilized to retrieve river ice characteristics.
- -
- Validation sources: field data or other remote sensing observations and techniques used for validation (ground truth).
- -
- Quantitative or qualitative assessment: indicates the analysis of results.
- -
- Main purpose: describes the primary objective of the publication (e.g., technology demonstration, introduction of new ice information, practical application).
- -
- AOI (area of interest): specifies the river name and country where ice observation occurred (Figure 1).
- -
- Reference: identifies the publication.
- Table 1 provides a synopsis of various sensor and platform combinations for river ice observation. Different colors are used to indicate whether each combination has been documented or if it holds potential for future use in river ice observation.
- Figure 1 (both top and bottom) displays AOIs for river ice presence, highlighting major rivers in the Northern Hemisphere.
- Figure 2 shows ice processes and represents ice information terms identified in the reviewed literature.
- Figure 3 outlines a methodological approach for extracting river ice information from observations.
3. River Ice Observation Technologies
3.1. Sensor
- Electro-optical and infrared (EO/IR): EO/IR sensors, such as multispectral or hyperspectral cameras, acquire images of the surface in ultra-violet, visible, and infrared wavelengths. EO/IR imagery is valuable for monitoring and characterizing ice cover, dynamics, and conditions in clear weather. Thermal infrared images of river ice can be used to measure temperature, and estimate ice floe geometric and statistical characteristics, concentration, structure, and thickness. The resolution for EO/IR sensors spans from a few cm to about 1 km. Stereo imagery and motion videos can be used to extract digital surface models of river ice.
- Synthetic aperture radar (SAR): SAR sensors operate in the microwave frequency range and can provide high-resolution images regardless of cloud cover or sunlight. SAR imagery acquired in X, C, L, and P bands in single (HH or VV), dual (HH-HV, HH-VV, or VV-VH), compact polarimetric, or Quad-Pol (HH-HV-VV) polarizations were used to extract river ice information. The resolution of SAR sensors ranges from sub-meter (e.g., 25 cm) to 100 m. Interferometric SAR (InSAR) technology can be used for change detection in ice cover [49] and for determining ice displacement [50].
- Microwave radiometers: microwave radiometers can measure brightness temperatures emitted by objects in frequencies of the microwave spectrum. They are useful for detecting the presence of ice on rivers and estimating ice thickness.
- Ice penetrating radar or ground penetrating radar (GPR): GPR systems emit an electromagnetic signals down into the ice surface to measure ice thickness and detect internal ice structures, such as layers or fractures.
- Radar altimeters: radar altimeter measures the distance between the platform (aircraft or satellite) and the surface of the river.
- LIDAR (light detection and ranging): LIDAR sensors emit laser pulses and measure the time it takes for the pulses to return after reflecting off a surface. Airborne, satellite, or terrestrial LIDAR can be used to map the topography of ice cover on rivers and measure water level or ice surface height in clear weather conditions.
- GNSS-IR: A global navigation satellite system-interferometric reflectometry (GNSS-IR) sensor exploits the interference between GNSS signals reflected off the observed surface and signals received directly from the GNSS satellite.
- Gravimetry: This sensor system measures changes in gravitational force to map gravitational fields.
- Acoustic profilers: Acoustic profilers are commonly used to measure water velocity and depth in rivers. They can also provide information on the presence of ice and its draft.
- Ice thickness gauges: these sensors/tools can utilize various principles (e.g., temperature or conductivity profiles) to measure the thickness of ice and snow covers.
3.2. Platforms
- Satellites: Satellites orbit the Earth at various (from 160 km and above) altitudes and can carry a range of sensors to observe rivers and provide geospatial data acquisition at various scales. While satellites require substantial initial investment, once launched, they can facilitate global data accessibility and provide near-real-time (NRT) access.
- Aerial: Airplanes, helicopters, and unmanned aerial vehicles (UAVs) (also known as unoccupied aerial systems (UAS), remotely piloted aircraft (RPA), or drones) are commonly used for river ice observation. They can carry a variety of sensors, including cameras, LIDAR, SAR, GPR, and hyperspectral imagers. Aircrafts are particularly useful for high-resolution imaging and targeted observations of specific areas of interest. UAVs are increasingly used for remote sensing tasks due to their versatility and relatively low cost. The cost efficiency of airborne observation compared to commercial SAR satellite data was highlighted in [9]. Air balloons could potentially be employed for river ice observation, but such examples were not found in the literature.
- In situ (also referred to as ground-based): In situ sensors are instruments deployed on the ice, on the ground, underwater, or mounted on towers or other structures. In the case of river ice, ships (icebreakers) and vehicles moving on the ice surface can also be employed. In situ sensors are essential in collecting reliable ice information and are often used for the validation of remote sensing measurements collected from aerial or satellite platforms.
Sensor Type | Platform | ||
---|---|---|---|
Satellite 1 | Aerial | In Situ | |
EO/IR | |||
SAR | |||
Microwave Radiometer | |||
GPR | |||
Radar Altimeter | |||
LIDAR | |||
GNSS-IR | |||
Gravimetry | |||
Acoustic Profilers |
3.3. Satellites
3.3.1. EO/IR
Year | Satellite, Sensor | Band | Resolution, m | Ice Information | Retrieval Method | Validation Sources | Quantitative or Qualitative Assessment | Main Purpose | AOI | Reference |
---|---|---|---|---|---|---|---|---|---|---|
1977 | Landsat 1–2, MSS 1, NOAAVHRR | Band 7 (NIR), Visible (0.6–0.7 µm), TIR (10.5–12.5 µm) | 60, 1000 | Break-up | Photointerpretation | Field observations at ground stations | High correlation (slope 0.98) of break-up dates | Proof of concept | Mackenzie River (Canada) | [52] |
1990 | Landsat 1–5, MSS, RBV, TM | MSS Band 2 (0.6–0.7 µm) RBV (0.58–0.68 µm and 0.505–0.75 µm) TM Band 3 (0.63–0.69 µm) | 30, 60 | Different 4 classes based on appearance tones: (1) ice-free, (2) partial gray ice, (3) complete gray ice, and (4) white ice | Visual photointerpretation | Areal photos, ground observations, water temperature records | Agreed 64–80% of time | Ice conditions, navigation, forecasting model | Allegheny, Monongahela, and Ohio rivers and Illinois Waterway (USA) | [53] |
2004 | MODIS; AVHRR | Visible and near-infrared bands | 250–500, 1000 | Break-up date | Visual interpretation | Ground-based observations; | Mean precision ± 1.75 days | Confirm MODIS and AVHRR utility | Lena, Ob’, Yenisey, and Mackenzie rivers (Canada, Russia) | [54] |
2010 | Landsat-7 ETM+ | 6 bands, NDVI and NDSI | Ice identification/ detection | Decision tree and fuzzy K-means clustering, | Visual image interpretation | 83% of correct identification | Confirm capability | Yellow River (China) | [55] | |
2011 | Terra, ASTER; ALOS, PRISM; IKONOS | Stereo NIR Bands 3N, 3B | 15 2.5 1 | Surface-water velocity based on ice debris tracking | NCC template matching | Cross-check with Landsat 5, 7 images | Accuracy ~0.5 pixels (1.3 m, 0.03 m s−1) | Demonstration | St. Lawrence and Mackenzie rivers (Canada) | [56] |
2013 | Terra, ASTER | Stereo device NIR Bands 3N and 3B | 15 | Ice velocity | NCC matching, manual coregistration | Investigation of matching result variations | Accuracy (0.04 m s−1) and errors analysis | Demonstrate ice velocity over several 100 s km | Lena River (Russia) | [57] |
2014 | Terra, MODIS | Surface reflectance MOD09GQ Band 2 (841–876 nm) | 250 | Intraseasonal cycle from ice onset to ice break-up and total melting | Decision tree classification based on 4 thresholds | Landsat 7, in situ discharge measurements, aerial photographs and ice bulletins | PoD 91%, FAR 37% | River ice mapping system | Susquehanna River (USA) | [58] |
2016 | Aqua and Terra, MODIS | Snow products, radiance products | 500 250 | Break-up, ice-off dates, average ice velocity | Visual interpretation | WSC hydrometric stations | Difference of 5 days | Confirm MODIS utility | Mackenzie River (Canada) | [59] |
2016 | Terra MODIS | MOD09GQ surface reflectance Band 2 (841–876 nm) | 250 | Break-up dates (3 classes: ice, water, and mixed) | Automated classification based on threshold | Hydrometric records | Mean uncertainty ±1.3 days | Develop automated algorithm | Mackenzie, Lena, Ob’ and Yenisey rivers (Canada, Russia) | [45] |
2019 | Aqua and Terra, MODIS | surface reflectance MYD09GQ and MOD09GQ products | 250 | Break-up | Semi-automated classification using optimal threshold | Gauge stations | Mean bias −2.0 to 6.7 days, MAE 3.4 to 6.9 days | Operational flood monitoring | Moose, Albany, Attawapiskat, Winisk and Severn rivers (Canada) | [60] |
2019 | Planet Scope | RGB, NIR | 3.7 | Velocity | NCC-based matching | Error budget and analysis of uncertainties, Landsat 8, Sentinel-2, ASTER stereo | Accuracy ±0.01 m s−1 | Introduce the satellites | Amur River (Russia) Yukon River (USA) | [61] |
2020 | Landsat 5–8, TM, ETM+, OLI | Green, SWIR, (for NDSI) | 30 | Ice spatial extent, multiyear maximum distribution | Calibrated threshold | Temperature, gauged discharge data | Average accuracy vs. visual interp. is 0.973 | Ascertain spatial and temporal distribution | Babao River (Tibetan Plateau, China) | [62] |
2021 | Sentinel-2 MSI, PROBA-V | Red | 10 100 | Movement, velocity | Entropy filter, threshold, template matching | Error analysis | Able to generate product | Show the feasibility | Lena River (Russia) | [63] |
2023 | Sentinel-2 MSI, Landsat 8 OLI | Red, NIR, SWIR (for RDRI) | 10 30 | River ice extent, accumulation, melting, phenology | Threshold | Air temperature | Limitation analysis | Determine phenology and processes | 8 rivers in Tibetan Plateau (China) | [64] |
2023 | NOAA-20, 21, SNPP, VIIRS | Red, NIR, and TIR bands (I01, I02, I03, and I05) | 375 | Ice extent, ice concentration, map with classes: ice, water, land, snow, vegetation, cloud and shadow | Semantic segmentation with U-Net CNN | Ground observations, Sentinel 1, 2, 3, river ice charts | PoD 77%, FAR 12%, CSI 0.697 | Introduce operational system | Lat. [30 to 80], Lon. [−180 to −60] (USA and Canada) | [65] |
3.3.2. SAR
- Ground-based (in situ) observations: drill holes for measurements of ice thickness and snow depth, ice typing, GPR for ice thickness measurement, runoff observations at gauging stations, photographs and videos on ice cover extent, dynamics and ice jams;
- Aerial photos and videos of ice conditions and extent;
- Remote sensing data from other satellite sources (e.g., Landsat and Sentinel-2);
- Environmental data including air temperature and precipitation.
Year | Satellite Sensor | Band; Polarization | Resolution, m | Ice Information | Retrieval Method | Validation Sources | Quantitative or Qualitative Assessment | Main Purpose | AOI | Reference |
---|---|---|---|---|---|---|---|---|---|---|
1993 | ERS-1 | C; VV | 26 30 | Monitor/differentiate ice conditions (snow ice, clear ice, thin ice, new ice, broken ice, navigation track, cracks and ridges) | Visual interpretation, pre-processing by contrast stretching, despeckling and brightness adjustment. | Ground observations and photos, ice thickness, aerial photos | Analysis of SAR limitations, potential utility | Evaluate utility for navigation and engineering applications | St. Marys, Connecticut and White rivers (USA) | [71] |
2001 | RADARSAT-1 | C; HH | 8 | Frazil pans and floes, juxtaposed, secondary consolidated, shore ice (frazil slush, melting ice, and brash ice), thermal break-up | Fuzzy K-means classification | Aerial photographs and videos | Most of the ice types can be visually identified and distinguished | Ice formation, ice types, and ice strength are necessary for the operation of hydropower-generating facilities | Peace River (Canada) | [9] |
2003 | RADARSAT-1 | C; HH | 8 | Ice cover (7) types (frazil pans, juxtaposed ice, a juxtaposed ice cover with moderate consolidation, consolidated ice cover) | Visually and Fuzzy K-means classification | Aerial videos | Qualitative assessment shows good agreement | Ice cover maps for hydroelectric operations | Peace River (Canada) | [72] |
2003 | RADARSAT-1 | C; HH | 8 | Ice thickness, sail height, ice classes (open water/skim ice/smooth border, low- or high-concentration ice pans, juxtaposed, consolidated) | Fuzzy K-means classification, ice thickness regression | Field data: drill holes, cross-sectional surveys, air photos | Thickness R2 0.75–0.89 | Measure spatial differences in ice cover for hydroelectric operations | Peace River (Canada) | [73] |
2004 | RADARSAT-1 | C; HH | 100 | Ice front, dark and bright ice classes | Visual interpretation | Predictive model, feedback from users | Good agreement | Reduced uncertainty in flood forecast | Exploits River (Canada) | [74] |
2006 | RADARSAT-1 Envisat ASAR | C; HH C: HH-VV | 8 25 | Ice classes (open water border, frazil pans, juxtaposed, consolidated) | GLCM texture and backscattering, fuzzy K-means | Aerial photos, manual labeling | High degree of confidence | Develop mapping procedure, compare satellites | Peace River (Canada) | [75] |
2007 | RADARSAT-1 | C; HH | 8 | Columnar ice, snow ice and frazil ice | Scattering model | Cores: ice thickness, ice type, ice densities | Model results proved | Understand interaction radar signal with the different ice types | Athabasca River (Canada) | [76] |
2007 | RADARSAT-1 | C; HH | 9 | Ice classes: consolidated frazil, columnar (thermal) ice, heavily consolidated ice, juxtaposed | Fuzzy K-means, object-oriented classification, backscatter, texture | Field and aerial surveys | OA 69–92% | Validate river ice maps and assess classifiers for hydropower companies or flood forecasters | Peace and Saint-François rivers | [77] |
2009 | TerraSAR-X RADARSAT-2 | X, C; HH-VV, QP | 5.2 × 7.6 | Extent of intact frazil and consolidated ice classes | SVM classifier | Ice cores and ground photos, GPR | Mapping accuracy—CI 80.1%, II 64.8% | Evaluate utility of DP SAR | Saint-Francois River (Canada) | [78] |
2009 | RADARSAT-1 | C; HH | II, jam, running ice, ice thickness, thermal, juxtaposed and hummocky ice covers | Backscatter analysis | Field study: holes, helicopter, photographs | plots | Explore application of SAR for river ice characterization | Athabasca River (Canada) | [79] | |
2010 | RADARSAT-2 | C, HH-HV | 25 | Ice thickness | HH backscatter | Field data: ice thickness and snow depth | R2 = 0.43–0.6 | Ice thickness for ice break-up forecasting | Red River (Canada) | [80] |
2011 | RADARSAT-2 | C, QP | Freeze-up process, floes, columnar, consolidated, border ice, ice bridging, frazil, or snow ice | HH-HV-VV RGB color composite interpretation | Field data: roughness, snow properties, ice thickness, and ice stratigraphy, GPR | Successful ice type map | Basis for exploring differences in ice strength and thermal characteristics between the various ice cover types | Red River (Canada) | [81] | |
2011 | RADARSAT-2 ALOS PALSAR | C; QP L; QP | Ice cover (columnar, frazil) characteristic, thickness, classification | Linear regression, polarimetric parameters | Field data: surface roughness, snow properties, ice thickness, and ice cover composition | Coefficient of variation is better for R2, Thickness R2 ≤ 0.7 | Study potential of R2 and ALOS | Mackenzie River (Canada) | [82] | |
2013 | TerraSAR-X RADARSAT-2 | X; HH C; QP | 3.74 10.96 | Extent, estimation of decay | K-means classification | Comparison to manually derived reference | Mean error 10–16% | Analyse classification performance | Lena River (Russia) | [83] |
2013 | RADARSAT-2 MODIS | C; DP, QP | 10, 25 | Ice type identification: consolidated frazil pans, juxtaposed frazil pans, skim ice, thermal ice | Fuzzy K-means classification using backscattering and texture | Photographs, visual data interpretation, cross-satellite comparison | 92% global accuracy | Improve IceMAP-R algorithm for automated ice classification | Peace River (Canada) | [84] |
2012, 2014 | RADARSAT-2 | C; QP | 5.2 × 7.6 | Ice thickness | Polarimetric entropy | Field data: cores, GPR | for some types RMSE 16.6% | Develop methodology | Saint-François, Koksoak, and Mackenzie rivers (Canada) | [85] [86] |
2014 | RADARSAT-1, ERS-2 | C; HH VV | 12.5 (PS) | Break-up process monitoring | Image brightness, its variance, sum of rank order change | Field runoff observations, gauging stations | Successful SAR variables were identified | Determine SAR potential | Kuparuk River (USA) | [87] |
2014 | ALOS; PALSAR, | L; QP | 30, 50 | Freeze/thaw conditions of surrounding forest area | HV backscatter analysis | AVNIR-2 brightness temperature and AMSR-E as ancillary data | Brightness temperature supports SAR results | Detecting thaw/freeze conditions on the ground | Lena River (Russia) | [88] |
2015 | RADARSAT-2 | C; DP and QP | Ice variation and formation, 4 types classification (open water, thermal, juxtaposed, and consolidated ice) | Texture, fuzzy K-means classifier, CV | Field data: time-lapse photos, holes, visual SAR interpretations | River-ice maps were compatible with validation sources | Introduce SAR-based methodology for ice monitoring and mapping | Slave River (Canada) | [70] | |
2015 | RADARSAT-2 | C; DP, QP | 8, 25 | Freeze-up process, different types of ice | Visual data interpretation | Field data: time-lapse cameras, thickness, ice types, etc. | Qualitative analysis | Describe mechanism of ice cover formation | Slave River (Canada) | [89] |
2016 | RADARSAT-2 TerraSAR-X | C; QP X; HH-VV | 5–11 3 (PS) | Skim, juxtaposed skim, agglomerated skim ice, frazil run and consolidated ice | Wishart classification | Landsat | OA TSX 81.3–87.5%, R2 83.8–99% | Compare and evaluate TerraSAR-X with RADARSAT-2 | Peace River (Canada) | [90] |
2017 | Sentinel-1 | C; VV + VH | 20 | Ice cover | Log likelihood change statistic on optimal thresholds | Landsat 8, Sentinel-2, air temperature, precipitation | Visibility analysis | Monitor ice cover changes | Vistula River (Poland) | [91] |
2018 | RADARSAT-2 | C; SLA HH QP | 1.6 × 0.8 5.2 × 7.7 | Monitor ice cover development | Freeman–Durden polarimetric decomposition | Field data: snow depth, ice thickness, crystallography analysis; environmental data | Polarimetric product assessment and comparison with ice structure | Understand interactions between SAR signals and river ice covers to select transportation routes | Slave River (Canada) | [92] |
2019 | RADARSAT-2 | C; QP | 4.7, 8 | Classification of thermal ice, frazil ice, and consolidated ice | Minimum distance, Fisher and Wishart classifiers | Field data on ice typing and thickness measurement | OA 95% | Provide basis for modeling and ice thickness retrieval | Yellow River (China) | [93] |
2019 | RADARSAT-2 | C; QP | PS 10 | Ice thickness | “IceThick-RS” model and polarimetric parameters | Field data: thermal conditions, snow properties, measurements, and cores | Snow depth R2 0.93, 0.97 | Demonstrate utility of framework | Slave River (Canada) | [94] |
2021 | RADARSAT-1/2 | C; HH | Six classes of sheet ice and rubble ice | Two-step supervised classification model IceBC based on thresholds | Oblique aerial and time-lapse photography | OA water 97%, sheet ice 69–85%, and rubble ice 97–99% | For operational IceBC prototype | Mackenzie, Athabasca, Saint John, Moose, Albany rivers (Canada) | [95] | |
2021 | Sentinel-1 | C; VV-VH | PS 15 | Ice classes: sheet and rubble ice, ice jam | Random Forest classifier, pseudo-polarimetric decomposition and GLCM texture | On-site measurements by permanent cameras and observation flights, Sentinel-2 | Confusion matrix, Kappa coefficient 0.87, OA 91% | Assess utility of Sentinel-1 data for operational monitoring of river ice during break-up | Athabasca River (Canada) | [96] |
2022 | Sentinel-1 | C; VV-VH | PS 10 | Ice surface roughness (sheet, rubble) | Random Forest and regression models | UAV-based 3 cm DEM | STD MAPE 5–113% | Investigate effect of roughness on backscatter | Yellowstone River (USA) | [46] |
2022 | Sentinel-1 | C; VV-VH | 5 × 20 | Ice thickness | Regression, inversion | Field data | RMSE 0.109 m, 0.258 m | Evaluate retrieval methods | Babao and Binggou rivers (China) | [97] |
2022 | Sentinel-1 | C; VV-VH | PS 10 | Ice detection, analysis of border, frazil, consolidated ice | Three binary classification models based on thresholds | In situ observations of ice types, Sentinel-2 | Agreement 68–91% | Evaluate SAR potential to detect ice in narrow rivers | Nemunas and Neris rivers (Lithuanian) | [47] |
2022 | Sentinel-1 | C; VV-VH | PS 10 | Ice jam detection, ice-water classification | Ice classification based on threshold | RLIE, LIE, Sentinel-2 | F1, Precision, Recall 0.77 | Develop algorithm | Kemijoki River (Finland) | [98] |
2023 | Sentinel-1 | C; VV-VH | 20 | Ice thickness | Inversion from VV backscatter | Field measurements, water level, and discharge station data | R 0.702, 0.437 (for snow-covered ice), RMSE 11.75 cm | Analyse correlation and long-term trend, compare with model based on temperature | Yellow River (China) | [99] |
3.3.3. Other Satellites Technologies
- Altimeters
- Microwave Radiometers
- Satellite Gravimetry
Year | Satellite, Instrument | Sensor Type or Band | Resolution, m | Ice Information | Retrieval Method | Validation Sources | Quantitative or Qualitative Assessment | Main Purpose | AOI | Reference |
---|---|---|---|---|---|---|---|---|---|---|
2005 | TOPEX/Poseidon | C, Ku | (GS) 596 | River discharge | Relation between water level and river discharge | River level and discharge measured at gauging station | Good agreement, average error up to 17% | Identify potential solutions and benefit for hydrological studies | Ob’ River (Russia) | [100] |
2014 | Jason-1, 2; Altimeter, JMR, AMR | Ku 18.7, 23.8, 34 GHz | 10 km | Determine freezing time | Backscatter coefficient and brightness temperatures histograms | In situ observation data | Good agreement with in situ data | Develop method for distinction between open water and ice cover | Volga and Don rivers (Russia) | [108] |
2017 | GRACE | Gravity | 330 km | Peak river flow and snow mass estimation | Modeling | Global Land Data Assimilation System (GLDAS) datasets | Snow mass is 20% higher than GLDAS, R2 > 0.5, R 0.83 | Evaluate, assess, and examine basin scale performance | Basins of Mackenzie and Red rivers (Canada) | [107] |
2020 | Jason-2, 3; Altimeter, AMR | Ku 18.7, 23.8, 34 GHz | Few km 22–42 km | Ice phenology (freeze-up, break-up) and thickness | Backscatter coefficient behavior and brightness temperature difference | Landsat 8 and Sentinel-2, water level gauging stations, in situ observations | Ice phenology ±10 days in 90% of cases, thickness RMSE 0.07–0.18 m | Demonstrate potential for retrieval of river ice phenology and ice thickness for ice roads | Ob River (Russia) | [109] |
3.3.4. Multi-Sensor Observations
3.3.5. Summary of Satellites Observations
3.4. Airborne Instruments
3.4.1. EO/IR Cameras and Scanners
- their restriction to the visible and infrared portions of the electromagnetic spectrum to operate in low visibility conditions (e.g., clouds, fog),
- difficulties in ice characterization in the presence of snow cover, and
- challenges of aircraft navigation in poor meteorological conditions.
- surface roughness [46];
- broken anchor ice dams [118];
- shear wall height [119], identify and measure floe sizes, thickness and volume, and floe size distributions (which can be useful information for estimating loads on structures);
- ice jam, its state and elevations [120],
- ice thickness estimation at various stages of freeze-up on the river Sokna, Norway [121].
3.4.2. LIDAR
3.4.3. Radars
- -
- -
- ice type and drift determination with side-looking radar (SLR, also called SLAR), microwave scatterometers and SAR.
- Ice Penetrating Radar
- SAR
- Microwave Radiometer
3.5. In Situ Observations
3.5.1. Method Implementations
- Shore: including towers, bridges, or other structures which have elevation, and tramway,
- Ice surface: stationary or moving on ice surface such as sled or vehicle (e.g., snowmobile, car, air cushion vehicle (ACV) [138]),
- Ship [126],
- Underwater: submersibles or remotely operated vehicles (ROVs),
- Frozen in ice (gauges and buoys).
- -
- safety considerations favor the aerial platform over measurements taken from the ice surface,
- -
- additional equipment requirements differ for deployment on aerial platforms,
- -
- aerial platforms offer the potential for remote sensing over vast distances and areas, spanning many kilometers.
3.5.2. Observation Technologies
- Visual observations
- Photography and Video
- Thermal Sensors
- Gauges and buoys
- Acoustic
- Ice Penetrating radar
- Imaging Radars
- Seismic Sensors
- GNSS-IR
4. From Observations to River Ice Information
4.1. Observation Data Processing and Analysis
- -
- decision tree (DT) based on threshold and K-means classification methods for river ice classification,
- -
- NCC for ice movement estimation, and
- -
- SAR backscatter modeling, regression, and inversion for ice thickness retrieval.
4.2. ML Technologies for River Ice
4.3. Reporting and Product Dissemination
- Generation of output products in mapping formats (compatible with Geographic Information Systems (GIS) software, such as ESRI shapefiles, GeoTIFFs, KML/KMZ files), and in data formats (such as CSV, JSON, Excel) for sharing and integration with other software tools, supporting statistical analysis and visualization.
- Database storage allowing for easy access, retrieval, and querying, facilitating further analytics and data processing.
- Web-based platforms or portals enabling users to access and interact with the data online, providing interactive maps, visualization tools, and download options.
- Application Programming Interface (API) endpoints, which enable programmatically accessing and integrating river ice data into custom applications or systems, enhancing accessibility and interoperability.
- Social media platforms and media coverage, which can provide real-time updates and raise awareness about river ice conditions.
- Mobile applications, which can provide convenient access to river ice information, including alerts, maps, and crowd-sourced observations.
- Email alerts and newsletters, which deliver updates on river ice conditions, forecasts, and advisories to subscribers.
4.4. River Ice Observations for Hydraulic Models
4.5. Existing Operational River Ice Monitoring Services
4.5.1. Canada
4.5.2. European Products
4.5.3. USA
4.5.4. International Projects
5. Discussion
5.1. Gaps
5.2. Challenges
5.3. Future Directions and Opportunities
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Appendix A. River Ice Terms Representing the Observed River Ice Information
- Agglomerate ice [194]
- Agglomerated skim ice (ASI) is more packed than juxtaposed skim ice and therefore has higher SAR backscatter [90]
- Anchor ice [163]
- Aufeis is a deposit of ice on the surface of the ground or exposed structures, produced by the freezing of periodically flowing water [32]
- Black ice [70]
- Brash ice [9]
- Broken ice [71]
- Congestion is stagnation of the ice cover at choking locations [175]
- Clear ice is a smooth ice sheet, appears in gray tone in SAR image [71]
- Crack [71]
- Frazil floes [9]
- Frazil run [90]
- Hanging dams are made of frazil ice transported under an existing surface ice cover and depositing under the ice surface in slow-flowing locations (e.g., [198])
- Ice bridging [81]
- Ice concentration [65]
- Ice decay is the changes in ice-covered areas with melt onset and start of break-up [83]
- Ice extent (spatial) is the area (in km2) of river ice [62]
- Ice floes [81]
- Ice flow choking points are locations with 100% ice concentration at the water surface [175]
- Ice-free [53]
- Ice front is defined by two criteria: (i) the ice front is the boundary between partial and complete ice coverage; and (ii) the frazil pans and floes must be static [72]
- Ice heap: agglomeration of broken ice up to 10 m thick [131]
- Ice velocity is dividing the measured displacements to the time difference [57]
- Icing shell [5] forms horizontally, close to the water surface, and develop from waves that repeatedly flood cold surfaces (observed along the banks of turbulent channel segments such as rapids or riffles)
- Intraseasonal cycle from ice onset to ice break-up and total melting [58] (i.e., duration of ice cover including time of ice clearing)
- Juxtaposed ice is formed when ice floes gradually thicken and adhere to each other [70]; its rough ice–water interface and a coarse ice structure cause the medium to moderately strong SAR backscatter
- Mixed ice/water is the ice cover condition after beginning of break-up until open water. It was defined based on the reflectance (values 0.1–0.5) of MODIS Band 2 [45]
- Navigation track is an ice opening for ship navigation pathway, may contain brash ice [71]
- New ice is ice which was recently formed [71]
- Phenology is the duration of ice period and time of its appearance, accumulation, and disappearance for a certain river reach [64]
- Ridge [71]
- Rubble ice is resulted from mechanical break-ups and it has a rough top surface (texture) [95]
- Rough ice [71]
- Sail height serves as a measure of the surface roughness of the ice cover, determined from the thermal ice surface up the average tops of larger protruding ice pieces of ice by visually lining them up with the horizon [75]
- Spray ice is made by water splashing and freezing and it is generally observed close to waterfalls, cascades, or steps [5]
- Thermal ice is formed mainly near the shore, where water is slow moving. Thermal ice crystals are large and display a tubular form [70]
- Thickness of ice cover is the distance between the air–ice (or snow–ice) and ice–water interfaces [85]
- White ice [53].
References
- Brooks, R.N.; Prowse, T.D.; O’Connell, I.J. Quantifying Northern Hemisphere Freshwater Ice. Geophys. Res. Lett. 2013, 40, 1128–1131. [Google Scholar] [CrossRef]
- Environment Canada. MANICE: Manual of Standard Procedures for Observing and Reporting Ice Conditions, 9th ed.; Canadian Ice Service: Ottawa, ON, Canada, 2005; ISBN 978-0-660-62858-5.
- Shen, H.T. Mathematical Modeling of River Ice Processes. Cold Reg. Sci. Technol. 2010, 62, 3–13. [Google Scholar] [CrossRef]
- Shen, H.T. River Ice Processes. In Advances in Water Resources Management; Wang, L.K., Yang, C.T., Wang, M.-H.S., Eds.; Springer International Publishing: Cham, Switzerland, 2016; pp. 483–530. ISBN 978-3-319-22923-2. [Google Scholar]
- Turcotte, B.; Morse, B. A Global River Ice Classification Model. J. Hydrol. 2013, 507, 134–148. [Google Scholar] [CrossRef]
- Beltaos, S. River Ice Jams; Water Resources Publications: Highland Rancch, CO, USA, 1995; ISBN 978-0-918334-87-9. [Google Scholar]
- Nafziger, J.; She, Y.; Hicks, F. Dynamic River Ice Processes in a River Delta Network. Cold Reg. Sci. Technol. 2019, 158, 275–287. [Google Scholar] [CrossRef]
- Daly, S.F. International Association for Hydaulic Research Working Group on Thermal Regimes: Report on Frazil Ice; International Association for Hydraulic Research Working Group on Thermal Regimes; Cold Regions Research and Engineering Laboratory (CRREL): Hanover, NH, USA, 1994. [Google Scholar]
- Weber, F.; Nixon, D.; Hurley, J. Identification of River Ice Types on the Peace River Using RADARSAT-1 SAR Imagery. In Proceedings of the 11th River Ice Workshop, Committee on River Ice Processes and the Environment (CGU-HS), Université d’Ottawa, Ottawa, ON, Canada, 14–16 May 2001. [Google Scholar]
- CCRS Tutorial: Fundamentals of Remote Sensing. 2016. Available online: https://natural-resources.canada.ca/sites/nrcan/files/earthsciences/pdf/resource/tutor/fundam/pdf/fundamentals_e.pdf (accessed on 5 April 2024).
- Daly, S.F. The Corps of Engineers. Ice Engineering Manual. J. Cold Reg. Eng. 2007, 21, 41–46. [Google Scholar] [CrossRef]
- Burrell, B.C.; Turcotte, B.; Comfort, G.; Groeneveld, J. Infrastructure and River Ice: An Overview. In Proceedings of the 21st Workshop on the Hydraulics of Ice, Saskatoon, SK, Canada, 29 August–1 September 2021. CGU HS Committee on River Ice Processes and the Environment. [Google Scholar]
- Hicks, F.E. An Introduction to River Ice Engineering for Civil Engineers and Geoscientists; CreateSpace Independent Publishing Platform: Charleston, SC, USA, 2016. [Google Scholar]
- Beltaos, S.; Burrell, B.C. Climatic Change and River Ice Breakup. Can. J. Civ. Eng. 2003, 30, 145–155. [Google Scholar] [CrossRef]
- Magnuson, J.J.; Robertson, D.M.; Benson, B.J.; Wynne, R.H.; Livingstone, D.M.; Arai, T.; Assel, R.A.; Barry, R.G.; Card, V.; Kuusisto, E.; et al. Historical Trends in Lake and River Ice Cover in the Northern Hemisphere. Science 2000, 289, 1743–1746. [Google Scholar] [CrossRef]
- Prowse, T.D.; Beltaos, S. Climatic Control of River-Ice Hydrology: A Review. Hydrol. Process. 2002, 16, 805–822. [Google Scholar] [CrossRef]
- Prowse, T.D.; Bonsal, B.R. Historical Trends in River-Ice Break-up: A Review. Hydrol. Res. 2004, 35, 281–293. [Google Scholar] [CrossRef]
- Yang, X.; Pavelsky, T.M.; Allen, G.H. The Past and Future of Global River Ice. Nature 2020, 577, 69–73. [Google Scholar] [CrossRef]
- Rokaya, P. Impacts of Climate and Regulation on Ice-Jam Flooding of Northern Rivers and Their Inland Deltas. Ph.D. Thesis, University of Saskatchewan, Saskatoon, SK, Canada, 2018. [Google Scholar]
- Timalsina, N.P. Ice Conditions in Norwegian Rivers Regulated for Hydropower: An Assessment in the Current and Future Climate, Skipnes Kommunikasjon as: Skipnes Kommunikasjon. Ph.D. Thesis, Norwegian University of Science and Technology (NTNU), Trondheim, Norway, 2014. [Google Scholar]
- Government of Yukon. Yukon State of the Environment Report 2020. A Report on Environmental Indicators; 2020. Available online: https://open.yukon.ca/sites/default/files/yukon-state-environment-report-2020.pdf (accessed on 5 April 2024).
- Brown, D.R.N.; Arp, C.D.; Brinkman, T.J.; Cellarius, B.A.; Engram, M.; Miller, M.E.; Spellman, K.V. Long-Term Change and Geospatial Patterns of River Ice Cover and Navigability in Southcentral Alaska Detected with Remote Sensing. Arct. Antarct. Alp. Res. 2023, 55, 2241279. [Google Scholar] [CrossRef]
- Beltaos, S.; Prowse, T.D. Climate Impacts on Extreme Ice-Jam Events in Canadian Rivers. Hydrol. Sci. J. 2001, 46, 157–181. [Google Scholar] [CrossRef]
- Ettema, R. Information Needs When Estimating Ice Jam Floods and Ice Runs. In Extreme Hydrological Events: New Concepts for Security; Vasiliev, O.F., Van Gelder, P.H.A.J.M., Plate, E.J., Bolgov, M.V., Eds.; NATO Science Series; Springer: Dordrecht, The Netherlands, 2007; Volume 78, pp. 285–298. ISBN 978-1-4020-5739zxcv-7. [Google Scholar]
- Lindenschmidt, K.-E.; Alfredsen, K.; Carstensen, D.; Choryński, A.; Gustafsson, D.; Halicki, M.; Hentschel, B.; Karjalainen, N.; Kögel, M.; Kolerski, T.; et al. Assessing and Mitigating Ice-Jam Flood Hazards and Risks: A European Perspective. Water 2022, 15, 76. [Google Scholar] [CrossRef]
- Turcotte, B.; Morse, B.; Pelchat, G. Impact of Climate Change on the Frequency of Dynamic Breakup Events and on the Risk of Ice-Jam Floods in Quebec, Canada. Water 2020, 12, 2891. [Google Scholar] [CrossRef]
- Lindenschmidt, K.-E.; Rokaya, P. A Stochastic Hydraulic Modelling Approach to Determining the Probable Maximum Staging of Ice-Jam Floods. J. Environ Inform. 2019, 34, 45–54. [Google Scholar] [CrossRef]
- Zahmatkesh, Z.; Kumar Jha, S.; Coulibaly, P.; Stadnyk, T. An Overview of River Flood Forecasting Procedures in Canadian Watersheds. Can. Water Resour. J. Rev. Can. Des Ressour. Hydr. 2019, 44, 213–229. [Google Scholar] [CrossRef]
- Arnal, L.; Pietroniro, A.C.; Pomeroy, J.W.; Fortin, V.; Casson, D.R.; Stadnyk, T.A.; Rokaya, P.; Durnford, D.; Friesenhan, E.; Clark, M.P. Towards a Coherent Flood Forecasting Framework for Canada: Local to Global Implications. J. Flood Risk Manag. 2023, e12895. [Google Scholar] [CrossRef]
- van der Sanden, J.J. River Ice Mapping and Monitoring Using SAR Satellites; Natural Resources Canada: Ottawa, ON, Canada, 2012; ISBN 978-1-100-19034-1.
- Kovachis, N.; Burrell, B.C.; Huokuna, M.; Beltaos, S.; Turcotte, B.; Jasek, M. Ice-Jam Flood Delineation: Challenges and Research Needs. Can. Water Resour. J. Rev. Can. Des Ressour. Hydr. 2017, 42, 258–268. [Google Scholar] [CrossRef]
- WMO. International Glossary of Hydrology = Glossaire International d’hydrologie = Mezhdunarodnyĭ Gidrologicheskiĭ Slovarʹ = Glosario Hidrológico Internacional, 3rd ed.; United Nations Educational, Scientific and Cultural Organization: Paris, France; World Meteorological Organization: Geneva, Switzerland, 2013; ISBN 978-92-3-001154-3. [Google Scholar]
- IAHR Multilingual Ice Terminology. 1980. Available online: https://rivergages.mvr.usace.army.mil/WaterControl/Districts/MVP/Reports/ice/iahr_ice_terminology.html (accessed on 5 April 2024).
- Unesco-WMO-IAHS. The Role of Snow and Ice in Hydrology: Proceedings of the Banff Symposia, September 1972; Unesco, the World Meteorological Organization, the International Association of the Hydrological Sciences: Banff, AB, Canada, 1973; Volume 1 and 2. [Google Scholar]
- Page, D.F.; Ramseier, R.O. Application of Radar Techniques to Ice and Snow Studies. J. Glaciol. 1975, 15, 171–191. [Google Scholar] [CrossRef]
- Arcone, S.A.; Delaney, A.J. Airborne River-Ice Thickness Profiling with Helicopter-Borne UHF Short-Pulse Radar. J. Glaciol. 1987, 33, 330–340. [Google Scholar] [CrossRef]
- Arcone, S.A. Dielectric Constant and Layer-Thickness Interpretation of Helicopter-Borne Short-Pulse Radar Waveforms Reflected from Wet and Dry River-Ice Sheets. IEEE Trans. Geosci. Remote Sens. 1991, 29, 768–777. [Google Scholar] [CrossRef]
- Rencz, A.N.; Ryerson, R.A. (Eds.) Manual of Remote Sensing, 3rd ed.; American Society for Photogrammetry and Remote Sensing; J. Wiley: New York, NY, USA, 1998; ISBN 978-0-471-29406-1. [Google Scholar]
- Rees, G. Remote Sensing of Snow and Ice; Taylor & Francis: Boca Raton, FL, USA, 2006; ISBN 978-0-415-29831-5. [Google Scholar]
- Leconte, R.; Daly, S.; Gauthier, Y.; Yankielun, N.; Bérubé, F.; Bernier, M. A Controlled Experiment to Retrieve Freshwater Ice Characteristics from an FM-CW Radar System. Cold Reg. Sci. Technol. 2009, 55, 212–220. [Google Scholar] [CrossRef]
- Kay, R.L.; White, K.D. River Ice Data Instrumentation. In CRREL Report 97-2; Hydrologie Engineering Branch, Engineering Division: Davis, CA, USA, 1997. [Google Scholar]
- U.S. Army Corps of Engineers. Engineering and Design: ICE ENGINEERING; Department of the Army: Washington, DC, USA, 2002; p. 377. [Google Scholar]
- Jeffries, M.O.; Morris, K.; Kozlenko, N. Ice Characteristics and Processes, and Remote Sensing of Frozen Rivers and Lakes. In Geophysical Monograph Series; Duguay, C.R., Pietroniro, A., Eds.; American Geophysical Union: Washington, DC, USA, 2013; pp. 63–90. ISBN 978-1-118-66642-5. [Google Scholar]
- Duguay, C.R.; Bernier, M.; Gauthier, Y.; Kouraev, A. Remote Sensing of Lake and River Ice. In Remote Sensing of the Cryosphere; Tedesco, M., Ed.; Wiley: New York, NY, USA, 2015; pp. 273–306. ISBN 978-1-118-36885-5. [Google Scholar]
- Cooley, S.W.; Pavelsky, T.M. Spatial and Temporal Patterns in Arctic River Ice Breakup Revealed by Automated Ice Detection from MODIS Imagery. Remote Sens. Environ. 2016, 175, 310–322. [Google Scholar] [CrossRef]
- Palomaki, R.T.; Sproles, E.A. Quantifying the Effect of River Ice Surface Roughness on Sentinel-1 SAR Backscatter. Remote Sens. 2022, 14, 5644. [Google Scholar] [CrossRef]
- Stonevicius, E.; Uselis, G.; Grendaite, D. Ice Detection with Sentinel-1 SAR Backscatter Threshold in Long Sections of Temperate Climate Rivers. Remote Sens. 2022, 14, 1627. [Google Scholar] [CrossRef]
- Verfaillie, M.; Cho, E.; Dwyre, L.; Khan, I.; Wagner, C.; Jacobs, J.M.; Hunsaker, A. UAS Remote Sensing Applications to Abrupt Cold Region Hazards. Front. Remote Sens. 2023, 4, 1095275. [Google Scholar] [CrossRef]
- Van der Sanden, J.; Drouin, H.; Bian, Y. Repeat Pass InSAR Observations of River and Lake Ice Cover: A Preliminary Evaluation of Information Content. In Proceedings of the 17th Workshop on River Ice, Edmonton, AB, Canada, 21–24 July 2013; pp. 258–271. [Google Scholar]
- Chu, T.; Lindenschmidt, K.E. Determining River Ice Displacement Using the Differential Interferometry Synthetic Aperture Radar (D-InSAR) Technique. In AGU Fall Meeting Abstracts; American Geophysical Union: Washington, DC, USA, 2016; Volume 2016, p. C33B-0827. [Google Scholar]
- Schneider, S.R. Monitoring River Ice Break-up from Space. Photogramm. Eng. Remote Sens. 1978, 44, 57–68. [Google Scholar]
- Dey, B.; Moore, H.; Gregory, A.F. The Use of Satellite Imagery for Monitoring Ice Break-up along the Mackenzie River, N. W. T. Arctic 1977, 30, 234–242. [Google Scholar] [CrossRef]
- Gatto, L.W. Monitoring River Ice with Landsat Images. Remote Sens. Environ. 1990, 32, 1–16. [Google Scholar] [CrossRef]
- Pavelsky, T.M.; Smith, L.C. Spatial and Temporal Patterns in Arctic River Ice Breakup Observed with MODIS and AVHRR Time Series. Remote Sens. Environ. 2004, 93, 328–338. [Google Scholar] [CrossRef]
- Liu, L.; Xu, Q.; Yang, S. Identification of River Ice on the Yellow River Using LANDSAT Images. In Proceedings of the 2010 18th International Conference on Geoinformatics, Beijing, China, 18–20 June 2010; IEEE: Piscataway, NJ, USA; pp. 1–4. [Google Scholar]
- Kääb, A.; Prowse, T. Cold-Regions River Flow Observed from Space: COLD-REGIONS RIVER FLOW FROM SPACE. Geophys. Res. Lett. 2011, 38, 1–5. [Google Scholar] [CrossRef]
- Kääb, A.; Lamare, M.; Abrams, M. River Ice Flux and Water Velocities along a 600 Km-Long Reach of Lena River, Siberia, from Satellite Stereo. Hydrol. Earth Syst. Sci. 2013, 17, 4671–4683. [Google Scholar] [CrossRef]
- Chaouch, N.; Temimi, M.; Romanov, P.; Cabrera, R.; McKillop, G.; Khanbilvardi, R. An Automated Algorithm for River Ice Monitoring over the Susquehanna River Using the MODIS Data. Hydrol. Process. 2014, 28, 62–73. [Google Scholar] [CrossRef]
- Muhammad, P.; Duguay, C.; Kang, K.-K. Monitoring Ice Break-up on the Mackenzie River Using MODIS Data. Cryosphere 2016, 10, 569–584. [Google Scholar] [CrossRef]
- Beaton, A.; Whaley, R.; Corston, K.; Kenny, F. Identifying Historic River Ice Breakup Timing Using MODIS and Google Earth Engine in Support of Operational Flood Monitoring in Northern Ontario. Remote Sens. Environ. 2019, 224, 352–364. [Google Scholar] [CrossRef]
- Kääb, A.; Altena, B.; Mascaro, J. River-Ice and Water Velocities Using the Planet Optical Cubesat Constellation. Hydrol. Earth Syst. Sci. 2019, 23, 4233–4247. [Google Scholar] [CrossRef]
- Li, H.; Li, H.; Wang, J.; Hao, X. Monitoring High-Altitude River Ice Distribution at the Basin Scale in the Northeastern Tibetan Plateau from a Landsat Time-Series Spanning 1999–2018. Remote Sens. Environ. 2020, 247, 111915. [Google Scholar] [CrossRef]
- Altena, B.; Kääb, A. Quantifying River Ice Movement through a Combination of European Satellite Monitoring Services. Int. J. Appl. Earth Obs. Geoinf. 2021, 98, 102315. [Google Scholar] [CrossRef]
- Li, H.; Li, H.; Wang, J.; Hao, X. Revealing the River Ice Phenology on the Tibetan Plateau Using Sentinel-2 and Landsat 8 Overlapping Orbit Imagery. J. Hydrol. 2023, 619, 129285. [Google Scholar] [CrossRef]
- Temimi, M.; Abdelkader, M.; Tounsi, A.; Chaouch, N.; Carter, S.; Sjoberg, B.; Macneil, A.; Bingham-Maas, N. An Automated System to Monitor River Ice Conditions Using Visible Infrared Imaging Radiometer Suite Imagery. Remote Sens. 2023, 15, 4896. [Google Scholar] [CrossRef]
- Beltaos, S.; Kääb, A. Estimating River Discharge during Ice Breakup from Near-Simultaneous Satellite Imagery. Cold Reg. Sci. Technol. 2014, 98, 35–46. [Google Scholar] [CrossRef]
- Pelletier, K.; Hicks, F.; van der Sanden, J. Monitoring Breakup on the Athabasca River Using RADARSAT-1 SAR Imagery. In Proceedings of the 12th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. Edmonton, AB, Canada, 19–20 June 2003; pp. 69–94. [Google Scholar]
- Pelletier, K.; van der Sanden, J.; Hicks, F. Synthetic Aperture Radar: Current Capabilities and Limitations for River Ice Monitoring. In Proceedings of the 17th Canadian Hydrotechnical Conference, Edmonton, AB, Canada, 17–19 August 2005; pp. 1–10. [Google Scholar]
- Pelletier, K. Radar Remote Sensing of River Ice on the Athabasca River at Fort McMurray, Alberta. Master’s Thesis, University of Alberta, Edmonton, AB, Canada, 2006. [Google Scholar]
- Chu, T.; Das, A.; Lindenschmidt, K.-E. Monitoring the Variation in Ice-Cover Characteristics of the Slave River, Canada Using RADARSAT-2 Data—A Case Study. Remote Sens. 2015, 7, 13664–13691. [Google Scholar] [CrossRef]
- Gatto, L. River Ice Conditions Determined from ERS-1 SAR. In Proceedings of the 50th Eastern Snow Conference and the 61 st Western Snow Conference, Quebec City, QC, Canada, 8–10 June 1993; Volume 7. [Google Scholar]
- Weber, F.; Nixon, D.; Hurley, J. Semi-Automated Classification of River Ice Types on the Peace River Using RADARSAT-1 Synthetic Aperture Radar (SAR) Imagery. Can. J. Civ. Eng. 2003, 30, 11–27. [Google Scholar] [CrossRef]
- Jasek, M.; Weber, F.; Hurley, J. Ice Thickness and Roughness Analysis on the Peace River Using RADARSAT-1 SAR Imagery. In Proceedings of the 12th Workshop on the Hydraulics of Ice Covered Rivers, Edmonton, AB, Canada, 19–20 June 2003; pp. 19–20. [Google Scholar]
- Puestow, T.; Randell, C.; Rollings, K.; Khan, A.A.; Picco, R. Near Real-Time Monitoring of River Ice in Support of Flood Forecasting in Eastern Canada: Towards the Integration of Earth Observation Technology in Flood Hazard Mitigation. In Proceedings of the IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium, Anchorage, AK, USA, 20–24 September 2004; Volume 4, pp. 2268–2271. [Google Scholar]
- Gauthier, Y.; Weber, F.; Savary, S.; Jasek, M.; Paquet, L.-M.; Bernier, M. A Combined Classification Scheme to Characterise River Ice from SAR Data. A Comb. Classif. Scheme Characterise River Ice SAR Data 2006, 5, 77–88. [Google Scholar]
- Gherboudj, I.; Bernier, M.; Leconte, R. Validation of a Backscatter Model of a River Ice Covers Using Radarsat-1 Images. In Proceedings of the 2007 IEEE International Geoscience and Remote Sensing Symposium, Barcelona, Spain, 23–28 July 2007; pp. 1087–1090. [Google Scholar]
- Drouin, H.; Gauthier, Y.; Bernier, M.; Jasek, M.; Penner, O.; Weber, F. Quantitative Validation of RADARSAT-1 River Ice Maps. In Proceedings of the 14th Workshop of the Committee on River Ice Processes and the Environment, Quebec City, QC, Canada, 19–22 June 2007. [Google Scholar]
- Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E. Investigation of Radarsat-2 and Terrasar-X Data for River Ice Classification. In Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa, 12–17 July 2009; pp. II-29–II-32. [Google Scholar]
- Unterschultz, K.D.; van der Sanden, J.; Hicks, F. Potential of RADARSAT-1 for the Monitoring of River Ice: Results of a Case Study on the Athabasca River at Fort McMurray, Canada. Cold Reg. Sci. Technol. 2009, 55, 238–248. [Google Scholar] [CrossRef]
- Lindenschmidt, K.-E.; Syrenne, G.; Harrison, R. Measuring Ice Thicknesses along the Red River in Canada Using RADARSAT-2 Satellite Imagery. JWARP 2010, 2, 923–933. [Google Scholar] [CrossRef]
- Lindenschmidt, K.-E.; van der Sanden, J.; Demski, A.; Drouin, H.; Geldsetzer, T. Characterising River Ice along the Lower Red River Using RADARSAT-2 Imagery. In Proceedings of the 16th Workshop on River Ice, Winnipeg, MB, Canada, 18–22 September 2011; pp. 18–22. [Google Scholar]
- Van der Sanden, J.; Drouin, H. Satellite SAR Observations of River Ice Cover: A RADARSAT-2 (C-Band) and ALOS PALSAR (L-Band) Comparison. In Proceedings of the 16th Workshop on River Ice, Winnipeg, MB, Canada, 18–22 September 2011; pp. 18–22. [Google Scholar]
- Sobiech, J.; Dierking, W. Observing Lake- and River-Ice Decay with SAR: Advantages and Limitations of the Unsupervised k -Means Classification Approach. Ann. Glaciol. 2013, 54, 65–72. [Google Scholar] [CrossRef]
- Jasek, M.; Gauthier, Y.; Poulin, J.; Bernier, M. Monitoring of Freeze-up on the Peace River at the Vermilion Rapids Using RADARSAT-2 SAR Data. In Proceedings of the 17th Workshop on River Ice, Edmonton, AB, Canada, 21–24 July 2013. [Google Scholar]
- Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E.; Van Der Sanden, J.; Chokmani, K. Retrieval of River Ice Thickness from C-Band PolSAR Data. In Proceedings of the 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, Germany, 22–27 July 2012; pp. 3237–3240. [Google Scholar]
- Mermoz, S.; Allain-Bailhache, S.; Bernier, M.; Pottier, E.; Van Der Sanden, J.; Chokmani, K. Retrieval of River Ice Thickness from C-Band PolSAR Data. IEEE Trans. Geosci. Remote Sens. 2014, 52, 3052–3062. [Google Scholar] [CrossRef]
- Floyd, A.L.; Prakash, A.; Meyer, F.J.; Gens, R.; Liljedahl, A. Using Synthetic Aperture Radar to Define Spring Breakup on the Kuparuk River, Northern Alaska. ARCTIC 2014, 67, 462. [Google Scholar] [CrossRef]
- Watanabe, M.; Takakura, H.; Yonezawa, C.; Yoshikawa, Y.; Shimada, M. Estimation of Cause of Ice Jam Flooding in Sub-Arctic Regions Using PALSAR Full Polarimetry Data. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 843–846. [Google Scholar]
- Das, A.; Sagin, J.; Van Der Sanden, J.; Evans, E.; McKay, H.; Lindenschmidt, K.-E. Monitoring the Freeze-up and Ice Cover Progression of the Slave River. Can. J. Civ. Eng. 2015, 42, 609–621. [Google Scholar] [CrossRef]
- Łoś, H.; Osińska-Skotak, K.; Pluto-Kossakowska, J.; Bernier, M.; Gauthier, Y.; Jasek, M.; Roth, A. Comparison of C-Band and X-Band Polarimetric Sar Data for River Ice Classification on the Peace River. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci. 2016, XLI-B7, 543–548. [Google Scholar] [CrossRef]
- Los, H.; Pawlowski, B. The Use of Sentinel-1 Imagery in the Analysis of River Ice Phenomena on the Lower Vistula in the 2015–2016 Winter Season. In Proceedings of the 2017 Signal Processing Symposium (SPSympo), Jachranka Village, Poland, 12–14 September 2017; pp. 1–5. [Google Scholar]
- Lindenschmidt, K.-E.; Li, Z. Monitoring River Ice Cover Development Using the Freeman–Durden Decomposition of Quad-Pol Radarsat-2 Images. J. Appl. Rem. Sens. 2018, 12, 026014. [Google Scholar] [CrossRef]
- Huang, P.; Shi, Q.; Tan, W.; Xu, W. Yellow River Ice Decision Tree Classification Method Based on Polarimetric Sar Data. In Proceedings of the IGARSS 2019—2019 IEEE International Geoscience and Remote Sensing Symposium, Yokohama, Japan, 28 July–2 August 2019; pp. 4129–4132. [Google Scholar]
- Zhang, F.; Li, Z.; Lindenschmidt, K.-E. Potential of RADARSAT-2 to Improve Ice Thickness Calculations in Remote, Poorly Accessible Areas: A Case Study on the Slave River, Canada. Can. J. Remote Sens. 2019, 45, 234–245. [Google Scholar] [CrossRef]
- van der Sanden, J.J.; Drouin, H.; Geldsetzer, T. An Automated Procedure to Map Breaking River Ice with C-Band HH SAR Data. Remote Sens. Environ. 2021, 252, 112119. [Google Scholar] [CrossRef]
- De Roda Husman, S.; Van Der Sanden, J.J.; Lhermitte, S.; Eleveld, M.A. Integrating Intensity and Context for Improved Supervised River Ice Classification from Dual-Pol Sentinel-1 SAR Data. Int. J. Appl. Earth Obs. Geoinf. 2021, 101, 102359. [Google Scholar] [CrossRef]
- Zhang, H.; Li, H.; Li, H. Monitoring the Ice Thickness in High-Order Rivers on the Tibetan Plateau with Dual-Polarized C-Band Synthetic Aperture Radar. Remote Sens. 2022, 14, 2591. [Google Scholar] [CrossRef]
- Marthandavilakom Prakasam, G. Detecting Ice Jams on the Rivers in Northern Finland Using Sentinel-1. Master’s Thesis, Aalto University, Espoo, Finland, 2022. [Google Scholar]
- Liu, B.; Ji, H.; Zhai, Y.; Luo, H. Estimation of River Ice Thickness in the Shisifenzi Reach of the Yellow River With Remote Sensing and Air Temperature Data. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2023, 16, 5645–5659. [Google Scholar] [CrossRef]
- Kouraev, A.V.; Zakharova, E.A.; Samain, O.; Mognard, N.M.; Cazenave, A. Ob’ River Discharge from TOPEX/Poseidon Satellite Altimetry (1992–2002). Remote Sens. Environ. 2004, 93, 238–245. [Google Scholar] [CrossRef]
- Michailovsky, C.I.; McEnnis, S.; Berry, P.A.M.; Smith, R.; Bauer-Gottwein, P. River Monitoring from Satellite Radar Altimetry in the Zambezi River Basin. Hydrol. Earth Syst. Sci. 2012, 16, 2181–2192. [Google Scholar] [CrossRef]
- Bogning, S.; Frappart, F.; Blarel, F.; Niño, F.; Mahé, G.; Bricquet, J.-P.; Seyler, F.; Onguéné, R.; Etamé, J.; Paiz, M.-C.; et al. Monitoring Water Levels and Discharges Using Radar Altimetry in an Ungauged River Basin: The Case of the Ogooué. Remote Sens. 2018, 10, 350. [Google Scholar] [CrossRef]
- Zakharova, E.; Nielsen, K.; Kamenev, G.; Kouraev, A. River Discharge Estimation from Radar Altimetry: Assessment of Satellite Performance, River Scales and Methods. J. Hydrol. 2020, 583, 124561. [Google Scholar] [CrossRef]
- Bjerklie, D.M.; Birkett, C.M.; Jones, J.W.; Carabajal, C.; Rover, J.A.; Fulton, J.W.; Garambois, P.-A. Satellite Remote Sensing Estimation of River Discharge: Application to the Yukon River Alaska. J. Hydrol. 2018, 561, 1000–1018. [Google Scholar] [CrossRef]
- Guo, X.; Jin, S.; Zhang, Z. Evaluation of Water Level Estimation in the Upper Yangtze River from ICESat-2 Data. In Proceedings of the 2021 Photonics & Electromagnetics Research Symposium (PIERS), Hangzhou, China, 21–25 November 2021; pp. 2260–2264. [Google Scholar]
- Podkowa, A.; Kugler, Z.; Nghiem, S.V.; Brakenridge, G.R. Ice Freeze-Up and Break-Up in Arctic Rivers Observed With Satellite L-Band Passive Microwave Data From 2010 to 2020. Water Resour. Res. 2023, 59, e2022WR031939. [Google Scholar] [CrossRef]
- Wang, S.; Zhou, F.; Russell, H. Estimating Snow Mass and Peak River Flows for the Mackenzie River Basin Using GRACE Satellite Observations. Remote Sens. 2017, 9, 256. [Google Scholar] [CrossRef]
- Rybushkina, G.; Troitskaya, Y.; Soustova, I. Ice Cover Determination of the Volga and the Don River Reservoirs on the Base of Jason-2 Sattelite Observations. In Proceedings of the 2014 IEEE Geoscience and Remote Sensing Symposium, Quebec City, QC, Canada, 13–18 July 2014; pp. 149–152. [Google Scholar]
- Zakharova, E.; Agafonova, S.; Duguay, C.; Frolova, N.; Kouraev, A. River ice phenology and thickness from satellite altimetry: Potential for ice bridge road operation and climate studies. Cryosphere 2021, 15, 5387–5407. [Google Scholar] [CrossRef]
- Chu, T.; Lindenschmidt, K.-E. Integration of Space-Borne and Air-Borne Data in Monitoring River Ice Processes in the Slave River, Canada. Remote Sens. Environ. 2016, 181, 65–81. [Google Scholar] [CrossRef]
- Lindenschmidt, K.-E.; Chun, K.P. Geospatial Modelling to Determine the Behaviour of Ice Cover Formation during Freeze-up of the Dauphin River in Manitoba. Hydrol. Res. 2014, 45, 645–659. [Google Scholar] [CrossRef]
- Zhang, X.; Yue, Y.; Han, L.; Li, F.; Yuan, X.; Fan, M.; Zhang, Y. River Ice Monitoring and Change Detection with Multi-Spectral and SAR Images: Application over Yellow River. Multimed. Tools Appl. 2021, 80, 28989–29004. [Google Scholar] [CrossRef]
- Amani, M.; Mahdavi, S.; Jin, S. River Ice Monitoring Using Unsupervised ISODATA Algorithm and Different Optical and SAR Satellite Datasets: A Case Study from the Churchill River in Labrador, Canada. J. Ocean. Technol. 2024, 18, 99. [Google Scholar]
- Alberta Environment. Peace River Ice Observation Report; Alberta Environment: Edmonton, AB, Canada, 2007. [Google Scholar]
- Morse, B. Winter Navigation on the St. Lawrence River. In Proceedings of the International Conference on Port and Ocean Engineering under Arctic Conditions, Ottawa, Canada, 12–17 August 2001. [Google Scholar]
- Lin, J.; Shu, L.; Zuo, H.; Zhang, B. Experimental Observation and Assessment of Ice Conditions with a Fixed-Wing Unmanned Aerial Vehicle over Yellow River, China. J. Appl. Remote Sens 2012, 6, 063586. [Google Scholar] [CrossRef]
- Garver, J.I.; Capovani, E.; Pokrzywka, D. Photogrammetric Models from UAS Mapping and Ice Thickness Estimates of the 2018 Mid-Winter Ice Jam on the Mohawk River, NY. Cockburn JMH Garver JI Proc. 2018, 10, 19–24. [Google Scholar]
- Alfredsen, K.; Haas, C.; Tuhtan, J.A.; Zinke, P. Brief Communication: Mapping River Ice Using Drones and Structure from Motion. Cryosphere 2018, 12, 627–633. [Google Scholar] [CrossRef]
- Alfredsen, K.; Juarez, A. Modelling Stranded River Ice Using LiDAR and Drone-Based Models. In Proceedings of the 25th International Symposium, The International Association for Hydro-Environment Engineering and Research. Ice Trondheim, Norway, 23–25 November 2020. [Google Scholar]
- Duguay, J.; Lindenschmidt, K.-E.; Trudel, M.; Pruneau, A. Aerial Photogrammetry to Characterise and Numerically Model an Ice Jam in Southern Quebec. Hydrol. Res. 2023, 54, 1329–1343. [Google Scholar] [CrossRef]
- Rødtang, E.; Alfredsen, K.; Juárez, A. Drone Surveying of Volumetric Ice Growth in a Steep River. Front. Remote Sens. 2021, 2, 767073. [Google Scholar] [CrossRef]
- Emond, J.; Morse, B.; Richard, M.; Stander, E.; Viau, A.A. Surface Ice Observations on the St. Lawrence River Using Infrared Thermography. River Res. Apps 2011, 27, 1090–1105. [Google Scholar] [CrossRef]
- Dugdale, S.J.; Kelleher, C.A.; Malcolm, I.A.; Caldwell, S.; Hannah, D.M. Assessing the Potential of Drone-based Thermal Infrared Imagery for Quantifying River Temperature Heterogeneity. Hydrol. Process. 2019, 33, 1152–1163. [Google Scholar] [CrossRef]
- Ren, H.C.; Yan, Q.; Liu, Z.J.; Zuo, Z.Q.; Xu, Q.Q.; Li, F.F.; Song, C. Study on Analysis from Sources of Error for Airborne LIDAR. IOP Conf. Ser. Earth Environ. Sci. 2016, 46, 012030. [Google Scholar] [CrossRef]
- Kucharczyk, M.; Hugenholtz, C.H.; Zou, X. UAV–LiDAR Accuracy in Vegetated Terrain. J. Unmanned Veh. Sys. 2018, 6, 212–234. [Google Scholar] [CrossRef]
- Dean, A.M. Remote Sensing of Accumulated Frazil and Brash Ice in the St. Lawrence River; Department of Defense, Department of the Army, Corps of Engineers, Cold: Hanover, NH, USA, 1977. [Google Scholar]
- Finkel’shteyn, M.; Lazarev, Ė.I. A Radar Video Pulse Device for Measuring the Thickness of Sea Ice as a New Promising Means of Ice Reconnaissance; Cold Regions Research and Engineering Laboratory: Hanover, NH, USA, 1981. [Google Scholar]
- Yankielun, N.E.; Arcone, S.A.; Crane, R.K. Thickness Profiling of Freshwater Ice Using a Millimeter-Wave FM-CW Radar. IEEE Trans. Geosci. Remote Sens. 1992, 30, 1094–1100. [Google Scholar] [CrossRef]
- Yankielun, N.E.; Ferrick, M.G.; Weyrick, P.B. Development of an Airborne Millimeter-Wave FM-CW Radar for Mapping River Ice. Can. J. Civ. Eng. 1993, 20, 1057–1064. [Google Scholar] [CrossRef]
- Peshkov, A.N.; Finkelstein, M.I.; Smutov, A.I.; Vasin, A.G.; Klemiato, K.I. Using of Radiolocation Measuring Devices of Freshmater Ice Thickness. In Proceedings of the IGARSS’91 Remote Sensing: Global Monitoring for Earth Management, Espoo, Finland, 3–6 June 1991; Volume 3, p. 1575. [Google Scholar]
- Fedorov, M.P.; Fedorova, L.L.; Savvin, D.V.; Prudetskaya, T.P.; Omelyanenko, A.V. Investigation of Ice Jams Using the GPR Method. In Proceedings of the 2018 17th International Conference on Ground Penetrating Radar (GPR), Rapperswil, Switzerland, 18–21 June 2018; pp. 1–4. [Google Scholar]
- Bai, X.; Wang, L.; Luo, X.; Mi, H.; Chen, H.; Liu, L.; Ji, M.; Gao, Y. A Layer Tracking Method for Ice Thickness Detection Based on GPR Mounted on the UAV. In Proceedings of the 2020 4th International Conference on Imaging, Signal Processing and Communications (ICISPC), Kumamoto, Japan, 23–25 October 2020; pp. 24–28. [Google Scholar]
- Leconte, R.; Klassen, P.D. Lake and River Ice Investigations in Northern Manitoba Using Airborne SAR Imagery. ARCTIC 1991, 44, 153–163. [Google Scholar] [CrossRef]
- Mermoz, S.; Allain, S.; Bernier, M.; Pottier, E.; Gherboudj, I. Classification of River Ice Using Polarimetric SAR Data. Can. J. Remote Sens. 2009, 35, 460–473. [Google Scholar] [CrossRef]
- Catoe, C.; Ling, G.; Nordberg, W.; Thaddeus, P. Preliminary Results from Aircraft Flight Tests of an Electrically Scanning Microwave Radiometer; NASA, Goddard Space Flight Center: Greenbelt, MD, USA, 1967.
- Melloh, R.A.; Gatto, L.W. Interpretation of Passive and Active Microwave Imagery over Snow-Covered Lakes and Rivers near Fairbanks Alaska. In Proceedings of the Application Remote Sensing Hydrology Workshop, Saskatoon, SK, Canada, 13–14 February 1990; pp. 259–278. [Google Scholar]
- Melloh, R.A.; Eppler, D.T.; Farmer, L.D.; Gatto, L.W.; Chacho, E.F. Interpretation of Passive Microwave Imagery of Surface Snow and Ice: Harding Lake, Alaska; U.S. Army Corps of Engineers, Cold Regions Research & Engineering Laboratory: Hanover, NH, USA, 1991. [Google Scholar]
- Venier, G.O.; Cross, F.R.; Ramseier, R.O. Experiments with Mobile X-Band FM Radar in Measuring the Thi Ckness of Fresh-Water Ice; CRC, Department of Communications: Ottawa, ON, Canada, 1975. [Google Scholar]
- de Koven Leffingwell, E. The Canning River Region, Northern Alaska; Proffesional Paper 109; US Government Printing Office, USGS: Washington, DC, USA, 1919.
- Rossiter, J.R.; Crissman, R.D. Assessment of Instrumentation for Application to Winter Hydropower Operations on the Upper Niagara River. In Proceedings of the 12th IAHR International Symposium on Ice (Trondheim 1994), Trondheim, Norway, 23 August 1994; pp. 744–751. [Google Scholar]
- Ansari, S.; Rennie, C.D.; Seidou, O.; Malenchak, J.; Zare, S.G. Automated Monitoring of River Ice Processes Using Shore-Based Imagery. Cold Reg. Sci. Technol. 2017, 142, 1–16. [Google Scholar] [CrossRef]
- Bharathi, P.; Subashini, P. Texture Feature Extraction of Infrared River Ice Images Using Second-Order Spatial Statistics. World Acad. Sci. Eng. Technol. 2013, 74, 747–757. [Google Scholar]
- Beltaos, S.; Carter, T.; Rowsell, R. Measurements and Analysis of Ice Breakup and Jamming Characteristics in the Mackenzie Delta, Canada. Cold Reg. Sci. Technol. 2012, 82, 110–123. [Google Scholar] [CrossRef]
- Zufelt, J. Ice Motion Detector System. In Ice Engineering; U.S. Army Cold Regions Research and Engineering Laboratory: Hanover, NH, USA, 1993. [Google Scholar]
- Arnborg, L.; Peippo, J.; Larsson, R. The Ice Gauge. An Instrument for Measuring Vertical Movements of Ice Surfaces. Geogr. Annaler. Ser. A Phys. Geogr. 1965, 47, 237. [Google Scholar] [CrossRef]
- Sherstone, D.A.; Prowse, T.D.; Gross, H. Development and Use of’ Hot-Wire’ and Conductivity Type Ice Measurement Gauges For Determination of Ice Thickness in Arctic Rivers. In Proceedings of the Symposium: Cold Regions Hydrology; American Water Resources Association, Bethesda Maryland; University of Alaska-Fairbanks: Fairbanks, AK, USA, 1986; pp. 121–129. [Google Scholar]
- Du, C.; Wang, Q.; Liu, X.; Zhao, Y.; Deng, X.; Cui, L. Research and Application of Ice Thickness and Snow Depth Automatic Monitoring System. IEEE Trans. Instrum. Meas. 2017, 66, 325–331. [Google Scholar] [CrossRef]
- Cui, L.; Qin, J.; Deng, X. Freshwater Ice Thickness Apparatus Based on Differences in Electrical Resistance and Temperature. Cold Reg. Sci. Technol. 2015, 119, 37–46. [Google Scholar] [CrossRef]
- Ackley, S.; Dellile, B.; Tison, J.; Carnat, G.; Weissling, B.; Lewis, M.; Fristen, C.; Stammerjohn, S. Documentation for Sea Ice Mass Balance in the Antarctic (SIMBA). 2016. Available online: https://openpolar.no/Record/ftdatacite:10.7265%2Fn53f4mj7 (accessed on 5 April 2024).
- Rafat, A.; Kheyrollah Pour, H.; Spence, C.; Palmer, M.J.; MacLean, A. An Analysis of Ice Growth and Temperature Dynamics in Two Canadian Subarctic Lakes. Cold Reg. Sci. Technol. 2023, 210, 103808. [Google Scholar] [CrossRef]
- Lynch, M.; English, J.; Briggs, R. Churchill River Ice Thickness Monitoring–SIMBA; Report R-20-005-1572; C-CORE: St. John’s, NL, Canada, 2020. [Google Scholar]
- Chave, R.A.J.; Lemon, D.D.; Fissel, D.B.; Dupuis, L.; Dumont, S. Real-Time Measurements of Ice Draft and Velocity in the St. Lawrence River. In Proceedings of the Oceans ’04 MTS/IEEE Techno-Ocean ’04 (IEEE Cat. No.04CH37600), Kobe, Japan, 9–12 November 2004; Volume 3, pp. 1629–1633. [Google Scholar]
- Morse, B.; Hessami, M.; Bourel, C. Characteristics of Ice in the St. Lawrence River. Can. J. Civ. Eng. 2003, 30, 766–774. [Google Scholar] [CrossRef]
- Mudge, T.; Sloat, J.; Chen, J. Discharge and Current Profiles under the Fee. In Proceedings of the IEEE/OES Eighth Working Conference on Current Measurement Technology, Southampton, UK, 28–29 June 2005; pp. 101–105. [Google Scholar]
- Wang, Y.; Tang, Y.; Zhang, Z. Experiment of Acoustic Tomography under Ice in Songhua River Basin. In Proceedings of the 2022 8th International Conference on Hydraulic and Civil Engineering: Deep Space Intelligent Development and Utilization Forum (ICHCE), Xi’an, China, 25–27 November 2022; pp. 431–438. [Google Scholar]
- Ismail, S.; Davis, J. Ice Jam Thickness Profiling on the Saint John River, New Brunswick. In Proceedings of the 11th International Association for Hydraulic Research Symposium on Ice, Banff, AB, Canada, 11–14 August 1992; pp. 383–394. [Google Scholar]
- Kämäri, M.; Alho, P.; Colpaert, A.; Lotsari, E. Spatial Variation of River-Ice Thickness in a Meandering River. Cold Reg. Sci. Technol. 2017, 137, 17–29. [Google Scholar] [CrossRef]
- Crissman, R.D.; Lalumiere, L.A. Radar Monitoring of Ice on the Upper Niagara River. In Proceedings of the 6th Cold Regions Engineering Specialty Conference, CRREL, Hanover, NH, USA, 26–28 February 1991; American Society of Civil Engineers (ASCE). pp. 406–415. [Google Scholar]
- Ursica, S. Identifying Subarctic River Thermal and Mechanical Ice Break-Up Using Seismic Sensing. Master’s Thesis, Umeå Universitet, Umeå, Sweden, 2021. [Google Scholar]
- Ghiasi, Y.; Duguay, C.R.; Murfitt, J.; van der Sanden, J.J.; Thompson, A.; Drouin, H.; Prévost, C. Application of GNSS Interferometric Reflectometry for the Estimation of Lake Ice Thickness. Remote Sens. 2020, 12, 2721. [Google Scholar] [CrossRef]
- Purnell, D.; Dabboor, M.; Matte, P.; Peters, D.; Anctil, F.; Ghobrial, T.; Pierre, A. Observations of River Ice Breakup Using GNSS-IR, SAR, and Machine Learning. IEEE Trans. Geosci. Remote Sens. 2024, 62, 5800613. [Google Scholar] [CrossRef]
- Pei, C.; She, Y.; Loewen, M. Deep Learning Based River Surface Ice Quantification Using a Distant and Oblique-Viewed Public Camera. Cold Reg. Sci. Technol. 2023, 206, 103736. [Google Scholar] [CrossRef]
- Singh, A.; Kalke, H.; Loewen, M.; Ray, N. Alberta River Ice Segmentation Dataset; IEEE: Piscataway, NJ, USA, 2019. [Google Scholar] [CrossRef]
- Singh, A.; Kalke, H.; Loewen, M.; Ray, N. River Ice Segmentation with Deep Learning. IEEE Trans. Geosci. Remote Sens. 2020, 58, 7570–7579. [Google Scholar] [CrossRef]
- Sola, D.; Scott, K.A. Efficient Shallow Network for River Ice Segmentation. Remote Sens. 2022, 14, 2378. [Google Scholar] [CrossRef]
- Yang, Z.; Zhu, Y.; Zeng, X.; Zong, J.; Liu, X.; Tao, R.; Cong, X.; Yu, Y. An Easy Zero-Shot Learning Combination: Texture Sensitive Semantic Segmentation IceHrNet and Advanced Style Transfer Learning Strategy. arXiv 2023, arXiv:2310.00310. [Google Scholar]
- Zhang, X.; Jin, J.; Lan, Z.; Li, C.; Fan, M.; Wang, Y.; Yu, X.; Zhang, Y. ICENET: A Semantic Segmentation Deep Network for River Ice by Fusing Positional and Channel-Wise Attentive Features. Remote Sens. 2020, 12, 221. [Google Scholar] [CrossRef]
- Zhang, X.; Zhou, Y.; Jin, J.; Wang, Y.; Fan, M.; Wang, N.; Zhang, Y. ICENETv2: A Fine-Grained River Ice Semantic Segmentation Network Based on UAV Images. Remote Sens. 2021, 13, 633. [Google Scholar] [CrossRef]
- Reuß, F.; Wessel, B.; Roth, A. TerraSAR-X Ice/Non-Ice Mapping of the Mackenzie River Using a Convolution-al Neural Network. In Proceedings of the TerraSAR-X/TanDEM-X Science Team Meeting 2019, Oberpfaffenhofen, Germany, 21–24 October 2019. [Google Scholar] [CrossRef]
- Yang, Z.; Zong, J.; Zhu, Y.; Liu, X.; Tao, R.; Yu, Y. River Ice Regime Recognition Based on Deep Learning: Ice Concentration, Area, and Velocity. Water 2023, 16, 58. [Google Scholar] [CrossRef]
- Sun, X.; Zhang, X.; Huang, W.; Han, Z.; Lyu, X.; Ren, P. Sea Ice Classification Using Mutually Guided Contexts. IEEE Trans. Geosci. Remote Sens. 2023, 61, 4204019. [Google Scholar] [CrossRef]
- Barrette, P.D.; Khan, A.A.; Lindenschmidt, K.-E. A Glimpse at Twenty-Five Hydraulic Models for River Ice. In Proceedings of the 27th IAHR International Symposium on Ice, Gdańsk, Poland, 9 June 2024. [Google Scholar]
- Hicks, F.; Andrishak, R.; She, Y. Modeling Thermal and Dynamic River Ice Processes. In Proceedings of the 13th International Conference on Cold Regions Engineering, Orono, ME, USA, 23–26 July 2006; pp. 1–11. [Google Scholar]
- Zhang, F.; Mosaffa, M.; Chu, T.; Lindenschmidt, K.-E. Using Remote Sensing Data to Parameterize Ice Jam Modeling for a Northern Inland Delta. Water 2017, 9, 306. [Google Scholar] [CrossRef]
- Turcotte, Benoit Will There Be an Ice Bridge This Winter? Predicting Spatio-Temporal Freeze-up Patterns Along the Yukon River, Canada. In Proceedings of the 25th IAHR International Symposium on Ice, Trondheim, Norway, 24 November 2020.
- Gauthier, Y.; Paquet, L.-M.; Gonzalez, A.; Hicks, F.; Andrishak, R.; Bernier, M. Using the FRAZIL System in Support of Winter River Flow Modelling. In Proceedings of the 14th Workshop on the Hydraulics of Ice Covered Rivers, Quebec City, QC, Canada, 19 June 2007. [Google Scholar]
- Gauthier, Y.; Tremblay, M.; Bernier, M.; Furgal, C. Adaptation of a Radar-Based River Ice Mapping Technology to the Nunavik Context. Can. J. Remote Sens. 2010, 36, S168–S185. [Google Scholar] [CrossRef]
- Pryse-Phillips, A.; Woolgar, R.; Rogers, K.; Lynch, M.; Puestow, T.; Randell, C. Ice Observations on the Churchill River Using Satellite Imagery. In Proceedings of the 15th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. St. John’s, NL, Canada, 15 June 2009. [Google Scholar]
- Russell, K.; Warren, S.; Howell, C.; Puestow, T.; Randell, C.; Khan, A.A.; Mahabir, C.; Tang, P.; Burakov, D.; Novik, N. Improved Satellite-Based River Ice Monitoring Using Dual-Polarized SAR Imagery. CGU HS Committee on River Ice Processes and the Environment. In Proceedings of the 15th Workshop on River Ice, Canadian Geophysical Union—Hydrology Section, Committee on River Ice Processes and the Environment. St. John’s, NL, Canada, 15 June 2009. [Google Scholar]
- Warren, S.; Puestow, T.; Richard, M.; Khan, A.A.; Mohammad, K.; Lindenschmidt, K.-E. Near Real-Time Ice-Related Flood Hazard Assessment on the Exploits River, Newfoundland. In Proceedings of the CGU HS Committee on River Ice Processes and the Environment (CRIPE), Whitehorse, Yukon, 9–12 July 2017. [Google Scholar]
- Puestow, T.; Cuff, A.; Richard, M.; Van Der Sanden, J.; Deschamps, A.; Proulx-Bourque, J.S.; Warren, S. The River Ice Automated Classifier Tool (RIACT). Presented at the CGU HS Committee on River Ice Processes and the Environment. In Proceedings of the 19th Workshop on River Ice, Whitehorse, YT, Canada, 9–12 July 2017. [Google Scholar]
- Rob, B.; Khan, A.A.; Lynch, M.; English, J.; Puestow, T. River Ice Monitoring: An Integrated Space, Airborne and In-Situ Sensing Approach. In Proceedings of the Geomatics Atlantic 2020, Virtual Conference, 25–26 November 2020. [Google Scholar]
- Lynch, M.; Briggs, R.; English, J.; Khan, A.A.; Khan, H.; Puestow, T. Operational Monitoring of River Ice on the Churchill River, Labrador. In Proceedings of the 21st Workshop on the Hydrolics of Ice Covered Rivers, Saskatoon, SK, Canada, 29 August–1 September 2021. [Google Scholar]
- Lynch, M.; Warren, S.; Power, D. Investigation of Leading Indicators for Pipeline Encroachment. Contract; Prepared for the Operations & Integrity Technical Committee of Pipeline Research Council International; C-CORE: St. John’s, NL, Canada, 2015. [Google Scholar]
- CCORE. Churchill River Ice Thickness Monitoring—SIMBA, 2020–2021 Ice Season; C-CORE: St. John’s, NL, Canada, 2021. [Google Scholar]
- Geldsetzer, T.; van der Sanden, J.; Drouin, H. Advanced SAR Applications for Canada’s River and Lake Ice. In Proceedings of the 2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver, BC, Canada, 24–29 July 2011; pp. 3168–3170. [Google Scholar]
- NRCan River Ice in Canada—Product Specifications. 2020. Available online: https://open.canada.ca/data/en/dataset/8ca6f047-ddef-43d7-81c2-47654f4c69bd (accessed on 11 July 2024).
- EEA Pan-European High-Resolution Snow & Ice Monitoring of the COPERNICUS Land Monitoring Service—Production of Basics Products; In Product User Manual for Ice Products; European Environment Agency: Copenhagen, Denmark, 2021.
- EEA Pan-European High-Resolution Snow & Ice Monitoring (HR-S&I) of the Copernicus Land Monitoring Service. In Algorithm Theoretical Basis Document FFor Ice Products Based on Sentinel-1 & Sentinel-2; European Environment Agency: Copenhagen, Denmark, 2022.
- EEA Pan-European High-Resolution Snow & Ice Monitoring (HR-S&I) of the Copernicus Land Monitoring Service. In Algorithm Theoretical Basis Document FFor Ice Products Based on Sentinel-2; European Environment Agency: Copenhagen, Denmark, 2023.
- Abdelkader, M.; Bravo Mendez, J.H.; Temimi, M.; Brown, D.R.N.; Spellman, K.V.; Arp, C.D.; Bondurant, A.; Kohl, H. A Google Earth Engine Platform to Integrate Multi-Satellite and Citizen Science Data for the Monitoring of River Ice Dynamics. Remote Sens. 2024, 16, 1368. [Google Scholar] [CrossRef]
- Stewart, E.J.; Liggett, D.; Lamers, M.; Ljubicic, G.; Dawson, J.; Thoman, R.; Haavisto, R.; Carrasco, J. Characterizing Polar Mobilities to Understand the Role of Weather, Water, Ice and Climate (WWIC) Information. Polar Geogr. 2020, 43, 95–119. [Google Scholar] [CrossRef]
- Zakharov, I.; Bobby, P.; Power, D.; Warren, S.; Howell, M. Satellite-Based Identification and Characterization of Extreme Ice Features: Hummocks and Ice Islands. Remote Sens. 2023, 15, 4065. [Google Scholar] [CrossRef]
- Łoś, H.; Osińska-Skotak, K.; Pluto-Kossakowska, J.; Bernier, M.; Gauthier, Y.; Pawłowski, B. Performance Evaluation of Quad-Pol Data Compare to Dual-Pol SAR Data for River Ice Classification. Eur. J. Remote Sens. 2019, 52, 79–95. [Google Scholar] [CrossRef]
- Lincoln, S. River Ice Spotter Network Training Information; National Weather Service: Silver Spring, MD, USA, 2023. [Google Scholar]
- Elhadi, N.; Lockhart, J. New Brunswick River Ice Manual; Communication New-Brunswick: Fredericton, NB, Canada, 1989. [Google Scholar]
- Gherboudj, I.; Bernier, M.; Leconte, R. A Backscatter Modeling for River Ice: Analysis and Numerical Results. IEEE Trans. Geosci. Remote Sens. 2010, 48, 1788–1798. [Google Scholar] [CrossRef]
- Ashton, G.D. River and Lake Ice Engineering; Water Resources Publications: Littleton, CO, USA, 1986; ISBN 978-0-918334-59-6. [Google Scholar]
- Wazney, L.; Clark, S.; Wall, A. Freeze-up Jam Observations on the Dauphin River. In Proceedings of the Committee on River Ice Processes and the Environment (CRIPE), Whitehorse, YT, Canada, 9 July 2017. [Google Scholar]
- Zhao, S.-X.; Wang, W.-J.; Shi, X.-H.; Zhao, S.-N.; Wu, Y.-J.; Quan, Q.; Li, C.; Szydlowski, M.; Li, W.; Kolerski, T. Freeze-Up Ice Jam Formation in the River Bend, a Case Study on the Inner Mongolia Reach of Yellow River. Crystals 2021, 11, 631. [Google Scholar] [CrossRef]
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Zakharov, I.; Puestow, T.; Khan, A.A.; Briggs, R.; Barrette, P. Review of River Ice Observation and Data Analysis Technologies. Hydrology 2024, 11, 126. https://doi.org/10.3390/hydrology11080126
Zakharov I, Puestow T, Khan AA, Briggs R, Barrette P. Review of River Ice Observation and Data Analysis Technologies. Hydrology. 2024; 11(8):126. https://doi.org/10.3390/hydrology11080126
Chicago/Turabian StyleZakharov, Igor, Thomas Puestow, Amir Ali Khan, Robert Briggs, and Paul Barrette. 2024. "Review of River Ice Observation and Data Analysis Technologies" Hydrology 11, no. 8: 126. https://doi.org/10.3390/hydrology11080126