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Ice plays key roles in regulating hydrological, ecological, biogeochemical, and socioeconomic functions of lakes. Long-term in situ lake ice phenological records indicate that lake ice is trending toward later freeze-up, earlier breakup, and a shorter ice duration. Parallel to study of lake ice using in situ records and process-based models, satellite remote sensing can expand our understanding of lake ice change over large spatial scales. However, most remote sensing studies have focused on large lakes or short periods of time, which may not robustly represent changes over multidecadal time periods or in the much more numerous small lakes. Here, we present a random forest model, Sensitive Lake Ice Detection (SLIDE), to accurately extract ice conditions from Landsat TM, ETM+, and OLI images. We trained the model using a manually labeled lake ice dataset (1089 labeled areas over 995 lakes globally). Our results show that our model achieves accurate classification between ice/snow and water (accuracy: 97.8%, kappa coefficient: 95.5%). Comparing Landsat-derived ice cover with in situ ice conditions, we show that our model produces less bias, lower RMSE, and higher kappa than does the Landsat snow/ice flag from the quality assessment band. This is especially true during the transitional period surrounding the ice on and off dates reported from in situ (mean bias -7.3% from our model, -17.3% from the Landsat quality band). Our results demonstrate the feasibility of mining the rich Landsat archive to study lake ice dynamics and of better flagging ice-affected lake observations.

Timely information on lake ice cover conditions is critical in support of commercial shipping, winter-road transportation, and winter leisure activities such as snowmobiling and ice fishing. Monito...

Despite long‐term analyses of lake ice phenology globally, comparatively little is known about high‐elevation lakes, for which climate shifts are thought to be occurring faster than at lower elevations. Using a 36‐yr dataset (1983–2018) on alpine lakes (> 3000 m ASL) from the Green Lakes Valley, Colorado (GLV), we found that ice‐cover duration decreased by an average of ~ 24 d, due to both earlier ice‐off (9 d) and especially later ice‐on (15 d). Spring ice thickness also decreased by 0.88 cm yr−1. By comparison, ice‐cover duration in the GLV is decreasing ~ 50% faster than for Northern Hemisphere lakes (n = 215), which translates to an increase in open water duration ~ 2.5 times more in the GLV than the average of the Northern Hemisphere. Our analytical comparison demonstrated more rapid trends in this alpine region compared to lakes more broadly, and especially emphasized the influence of elevation on lake ice phenology.

In ice-covered polar lakes, a narrow ice-free moat opens up in spring or early summer, and then persists at the edge of the lake until complete ice loss or refreezing. In this study, we analyzed the horizontal gradients in Ward Hunt Lake, located in the Canadian High Arctic, and addressed the hypothesis that the transition from its nearshore open-water moat to offshore ice-covered waters is marked by discontinuous shifts in limnological properties. Consistent with this hypothesis, we observed an abrupt increase in below-ice concentrations of chlorophyll a beyond the ice margin, along with a sharp decrease in temperature and light availability and pronounced changes in benthic algal pigments and fatty acids. There were higher concentrations of rotifers and lower concentrations of viruses at the ice-free sampling sites, and contrasts in zooplankton fatty acid profiles that implied a greater importance of benthic phototrophs in their inshore diet. The observed patterns underscore the structuring role of ice cover in polar lakes. These ecosystems do not conform to the traditional definitions of littoral versus pelagic zones but instead may have distinct moat, ice-margin, and ice-covered zones. This zonation is likely to weaken with ongoing climate change.

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively.

Further to systematic experiments on the flexural strength of laboratory-grown, fresh water ice loaded cyclically, this paper describes results from new experiments of the same kind on lake ice harvested in Svalbard. The experiments were conducted at −12 °C, 0.1 Hz frequency and outer-fiber stress in the range from ~ 0.1 to ~ 0.7 MPa. The results suggest that the flexural strength increases linearly with stress amplitude, similar to the behavior of laboratory-grown ice.

Snow and ice were monitored by thermistor-string-based Snow and Ice Mass Balance Array (SIMBA) in Lake Orajärvi in northern Finland. An existing automatic SIMBA-algorithm was further developed to derive air/snow, snow/ice and ice/water interfaces based on the SIMBA environment temperature (ET) profiles. The identified interfaces agreed with in situ observations made in 2011/12 winter season. The method was capable to identify upward-moving snow/ice interface that was also visible from SIMBA heating temperature (HT) profiles, which responds to differences in the thermal diffusivities of air, snow, ice and water. The SIMBA data obtained in winters 2017/18 and 2018/19 were used to investigate snow and ice mass balance. An upward-moving snow/ice interface was detected as a result of meteoric ice (snow ice and superimposed ice) formation. Snow contributed to granular lake ice formation up to 40–55% of the total ice thickness on the seasonal mean. Heavy snowfalls and low air temperature in early winter are favourable for granular ice formation. The seasonal mean snow depth on nearby land was 2.7–2.9 times of that on the lake. The estimation of freeboard from snow and ice mass-balance measurement is sensitive to the snow density. Accurate ice freeboard calculation is still a challenge.

Anomalous areas of varying shape and location characterized by low backscatter in Synthetic Aperture Radar (SAR) imagery of lake ice on lake Neyto on the Yamal Peninsula in Russia have been described qualitatively in the literature for many years. Possible suggested causes are the formation of eddies or the release of gas through the lake sediments, which could both lead to local thinning of the ice layer and alter radar backscatter. To date, the phenomenon, its cause, and its spatial and temporal dynamics are poorly understood, and studies from other geographic regions are completely absent. In order to perform first steps towards a better understanding of the phenomenon, we developed a workflow to quantitatively assess the spatial variability of the anomalies in the years 2015 to 2019 for lake Neyto. We introduce a binary image classification algorithm developed with state-of-the-art open-source image processing tools and employ metrics commonly used for describing spatial relationships of vector and raster data. This includes polygon distances, polygon intersections and cumulative pixel counts deduced from the classification results in order to quantify, for the very first time, the dynamics over a number of years. The geospatial analysis reveals large spatial variations, but also some overlap between different years. Locations of anomalies do not seem more similar between consecutive years than when they are compared over the longer period. Some of the spatial properties of the clusters of low backscatter may support the explanation of gas release as the primary cause of the observed patterns.

Ice Engineering is associated with how to solve challenges from different kinds of ice, such as, sea ice, river ice, lake ice, icing, and snow in cold regions. The aim is to design special structures which could resist structural and global impact from drifting ice and freezing ice.

As the global climate warms, lakes are expected and have been observed to experience changes in seasonal ice cover. Previous research has observed decreasing freeze durations, but relatively few studies have investigated the impact of climate change on lake ice intermittency - the tendency of lakes to freeze over in some years but not others. Here we conduct an analysis of a lake dataset that includes nineteen intermittent ice-covered lakes in the northern hemisphere. We use logistic and binomial regression to model the relationship between historical climate changes and freeze events, with log CO2 concentration and mean winter temperature as covariates. Across the lakes, we observe a decrease in freeze probability and years with freeze events, with nine out of nineteen lakes showing a significant relationship between freeze years and log CO2 concentration. Additionally, we find that mean winter temperature can be a simple, readily accessible predictor for intermittent lake freeze. We also examine Lake Carnegie in Princeton, NJ as a case study, taking into account both quantitative data and anecdotal evidence, and find that the probability of ice skating has decreased from nearly 1 to 0.2 over the past century. Accordingly, local newspaper archives semantically suggest that local expectations for lake freezing have reversed over the last century as a societal response to climate change.

The Qinghai-Tibet Plateau possesses the largest alpine lake system, which plays a crucial role in the land-atmosphere interaction. We report first observations on the thermal and radiation regime u...

Antarctic lakes with perennial ice covers provide the opportunity to investigate in-lake processes without direct atmospheric interaction, and to study their ice-cover sensitivity to climate conditions. In this study, a numerical model – driven by radiative, atmospheric and turbulent heat fluxes from the water body beneath the ice cover – was implemented to investigate the impact of climate change on the ice covers from two Antarctic lakes: west lobe of Lake Bonney (WLB) and Crooked Lake. Model results agreed well with measured ice thicknesses of both lakes (WLB – RMSE= 0.11 m over 16 years of data; Crooked Lake – RMSE= 0.07 m over 1 year of data), and had acceptable results with measured ablation data at WLB (RMSE= 0.28 m over 6 years). The differences between measured and modeled ablation occurred because the model does not consider interannual variability of the ice optical properties and seasonal changes of the lake's thermal structure. Results indicate that projected summer air temperatures will increase the ice-cover annual melting in WLB by 2050, but that the ice cover will remain perennial through the end of this century. Contrarily, at Crooked Lake the ice cover becomes ephemeral most likely due to the increase in air temperatures.

Changes in climate, warming temperatures and increased precipitation are impacting surface water resources in the Northwest Territories, Canada. Satellite remote sensing is an important tool to mon...

Climate change is expected to decrease ice coverage and thickness globally while increasing the variability of ice coverage and thickness on midlatitude lakes. Ice thickness affects physical, biological, and chemical processes as well as safety conditions for scientists and the general public. Measurements of ice thickness that are both temporally frequent and spatially extensive remain a technical challenge. Here new observational methods using repurposed soil moisture sensors that facilitate high spatial–temporal sampling of ice thickness are field tested on Lake Mendota in Wisconsin during the winter 2015/16 season. Spatial variability in ice thickness was high, with differences of 10 cm of ice column thickness over 1.05 km of horizontal distance. When observational data were compared with manual measurements and model output from both the Freshwater Lake (FLake) model and General Lake Model (GLM), ice thickness from sensors matches manual measurements, whereas GLM and FLake results showed a thinner and thicker ice layer, respectively. The FLake-modeled ice column temperature effectively remained at 0°C, not matching observations. We also show that daily ice dynamics follows the expected linear function of ice thickness growth/melt, improving confidence in sensor accuracy under field conditions. We have demonstrated a new method that allows low-cost and high-frequency measurements of ice thickness, which will be needed both to advance winter limnology and to improve on-ice safety.

Lake-ice phenology is an essential indicator of climate change impact for different regions (Livingstone, 1997; Duguay, 2010), which helps understand the regional characters of synchrony and asynchrony. The observation of lake ice phenology includes ground observation and remote sensing inversion. Although some lakes have been observed for hundreds of years, due to the limitations of the observation station and the experience of the observers, ground observations cannot obtain the lake ice phenology of the entire lake. Remote sensing has been used for the past 40 years, in particular, has provided data covering the high mountain and high latitude regions, where the environment is harsh and ground observations are lacking. Remote sensing also provides a unified data source and monitoring standard, and the possibility of monitoring changes in lake ice in different regions and making comparisons between them. The existing remote sensing retrieval products mainly cover North America and Europe, and data for Eurasia is lacking (Crétaux et al., 2020).Based on the passive microwave, the lake ice phenology of 522 lakes in the northern hemisphere during 1978-2020 was obtained, including Freeze-Up Start (FUS), Freeze-Up End (FUE), Break-Up Start (BUS), Break-Up End (BUE), and Ice Cover Duration (ICD). The ICD is the duration from the FUS to the BUE, which can directly reflect the ice cover condition. At latitudes north of 60°N, the average of ICD is approximately 8-9 months in North America and 5-6 months in Eurasia. Limited by the spatial resolution of the passive microwave, lake ice monitoring is mainly in Northern Europe. Therefore, the average of ICD over Eurasia is shorter, while the ICD is more than 6 months for most lakes in Russia. After 2000, the ICD has shown a shrinking trend, except northeastern North America (southeast of the Hudson Bay) and the northern Tibetan Plateau. The reasons for the extension of ice cover duration need to be analyzed with parameters, such as temperature, the lake area, and lake depth, in the two regions.

Millions of lakes and ponds occupy large areas of the Arctic discontinuous and continuous permafrost zones. During most of the year, the surfaces of these lakes remain covered by a thick layer of ice. Synthetic Aperture Radar (SAR) data have shown to be useful for studying the ice on Arctic lakes, especially for monitoring lake ice phenology and the grounding state of the ice (ice frozen to the lakebed versus floating lake ice). Significant backscatter is often observed from the floating ice regime in C-band due to scattering on a rough ice-water interface.Recent research has revealed features of anomalously low backscatter in Sentinel-1 C-band SAR imagery on some of the West Siberian lakes that likely belong to the floating ice regime. These anomalies are characterized by prominent shapes and sizes and seem to expand throughout late winter and/or spring. It is currently assumed that some of these features are related to strong emissions of natural gas (methane from hydrocarbon reservoirs), making it important to assess their origin in detail and understand the associated mechanisms. However, in-situ data are still missing.Here, we assess the potential of the combined use of C-band Sentinel-1 (freely available) and L-band ALOS PALSAR-2 data (available through JAXA PI agreement #3068002) to study the backscatter anomalies. We highlight the differences between observed backscatter from the two sensors with respect to different surface types (ground-fast lake ice, floating lake ice and anomalies) and investigate backscatter differences between frozen and melting conditions. Further, polarimetric classification is performed on L-band PALSAR-2 imagery, which reveals differences in scattering mechanisms between anomalies and floating lake ice.

Lake ice cover is essential to conserving the global freshwater supply for the 50 million lakesthat freeze each winter. Here, we ask when lakes across the Northern Hemisphere may permanently loseice cover. A K-means cluster analysis from 31 lakes identified four clusters of lakes vulnerable to losing icecover, including shallow and deep lakes in regions where winter air temperatures hover ∼0 °C and largerand deeper lakes in colder regions. By the end of this century, we estimate that up to 5,679 lakes of 1.35million HydroLAKES may permanently lose ice cover if greenhouse gas emissions (GHG) continue tobe emitted at current levels. In the Northern Hemisphere, lakes in southern and coastal regions, some ofwhich are among the largest lakes in the world and in close proximity to large human populations, are themost vulnerable to permanently losing ice.

This chapter describes the classification of the cryospheric components and their geographical distribution in the world. The first part introduces the overall distribution of the continental cryosphere, marine cryosphere and aerial cryosphere on the global scale, followed by the classification and geographical distribution of glaciers and ice sheets, frozen ground, snow cover, sea ice, river and lake ice, and all solid water in the atmosphere. The geographical distribution of a single cryospheric component is introduced at the global scale, then at the hemispheric scale and finally at the national scale.

New systematic experiments reveal that the flexural strength of saline S2 columnar-grained ice loaded normal to the columns can be increased upon cyclic loading by about a factor of 1.5. The experiments were conducted using reversed cyclic loading over ranges of frequencies from 0.1 to 0.6 Hz and at a temperature of −10 ∘C on saline ice of two salinities: 3.0 ± 0.9 and 5.9 ± 0.6 ‰. Acoustic emission hit rate during cycling increases with an increase in stress amplitude of cycling. Flexural strength of saline ice of 3.0 ± 0.9 ‰ salinity appears to increase linearly with increasing stress amplitude, similar to the behavior of laboratory-grown freshwater ice (Murdza et al., 2020b) and to the behavior of lake ice (Murdza et al., 2021). The flexural strength of saline ice of 5.9 ± 0.6 ‰ depends on the vertical location of the sample within the thickness of an ice puck; i.e., the strength of the upper layers, which have a lower brine content, was found to be as high as 3 times that of lower layers. The fatigue life of saline ice is erratic. Cyclic strengthening is attributed to the development of an internal back stress that opposes the applied stress and possibly originates from dislocation pileups.

Based on the features of diurnal variation of ice surface albedo in the Lake Ulansuhai, the relationship between solar elevation angle and geographic coordinates and Julian days, together with normalized time, to express the diurnal variations of the ice albedo. Linear combinations of four probability density distribution functions with exponential forms, including Laplace, Gauss, Gumbel, and Cauchy equations, were used to simulate the diurnal variations in observed ice albedo from sunrise to sunset with solar elevation angle more than 5°. The results reveals that the Laplace combined statistical model is the best fit to the observations. It can not only clearly show the double-peak distribution in the diurnal variation in ice surface albedo when the elevation angle is greater than 5°, but also express the U-type distribution between the two peaks as the elevation angle is more than 15°. This model has advantages of simple form in expression and clear physical meaning. The length parameter is close to half day, and the peak location is associated with the time of sunrise, and the asymmetry of the two peaks can be also shown. It provides a solid foundation for the parameterization schemes on the diurnal variations in lake ice surface albedo in different regions. © 2020 by Journal of Lake Sciences.

Scientists long eschewed studying lakes in winter, expecting that cold temperatures and ice cover limited activity below the surface. Recent findings to the contrary are changing limnologists’ views.

This review focuses upon the distribution, formation, and significance of two types of periglacial features associated with the seasonal formation of ice: (1) icings, and (2) seasonal frost mounds. The term icing refers to sheet-like masses of layered surface ice formed by freezing of successive flows of water seeping from the ground, discharging from springs, or emerging from below river or lake ice through fractures. The term seasonal frost mound refers to small to medium-sized mounds of ice or frozen ground or both that develop during a single winter season in response to increased pressures in ground water trapped within residual unfrozen zones in the active layer during freeze-back. Based on the analysis of structure and genetic process, seasonal frost mounds are subdivided into icing mounds, icing blisters, and frost blisters. Seasonal frost mounds often form in close association with icings. Implicit in these definitions is the seasonal nature of the ice formation and degradation processes. Under extreme environmental conditions, however, individual icings and seasonal frost mounds may persist for more than one year. Permafrost is not a prerequisite for either icings or seasonal frost mounds, but its presence facilitates their formation.

Is lake ice disappearing from the Earth system? Luke Grant2, Zeli Tan7, Marjorie Perroud8, Victor Stepanenko9, Bram Droppers3, Annette B.G. Janssen3, R. Iestyn Woolway4, Martin Schmid11, Jacob Schewe6, Fang Zhao6, Gosia Golub5, Rafael Macré10, Don Pierson5, Wim Thiery1,2, Inne Vanderkelen2, Sonia Seneviratne1 1 ETH Zurich, Institute for Atmospheric and Climate Science, 8092 Zurich, Switzerland.2 Vrije Universiteit Brussel, Department of Hydrology and Hydraulic Engineering, Brussels, Belgium.3 Wageningen University & Research, Water systems and Global Change, Wageningen, the Netherlands.­­4 Dundalk Institute of Technology, Dundalk, Ireland5 Uppsala University, Dept of Ecology and Genetics / Limnology, Uppsala, Sweden.6 Potsdam Institute for Climate Impact Research, 14473 Potsdam, Germany7 Pacific Northwest National Laboratory, Richland, WA, USA8 University of Geneva, Institute for Environmental Sciences, Carouge, Switzerland9 Lomonosov Moscow State University, Moscow, Russia10 Catalan Institute for Water Research, Girona, Spain11 Eawag: Swiss Federal Institute of Aquatic Science and Technology, Surface Waters - Research and Management - Kastanienbaum, Switzerland ­Proposed session: HS10.9 - Lakes and Inland Seas in a Changing EnvironmentCorresponding author’s e-mail address: luke.grant@vub.be Lakes offer manifold ecosystem services and their ice cover stabilizes lake physics, biogeochemistry and ecological processes. The impacts of climate change on lake ice and lake temperatures are well documented for individual lakes and regions through site specific studies. Yet, future global projections of these variables are mostly limited to empirical approaches and singular, coarse-resolution lake model simulations forced by individual climate model outputs. Through the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) we offer an improved global analysis of the lake temperature (2m depth), lake ice cover duration and lake ice thickness projections of six uncalibrated, global-scale lake models to assess the fate of future lake ice and lake temperatures. Lake model simulations were performed on a generic lake within each lake-containing land cell of a global 0.5o grid. Simulations were at daily resolution and forced by four bias-corrected global climate models from the Coupled Model Intercomparison Project version 5 (CMIP5) for pre-industrial to future periods (1661-2099) and representative concentration pathways 2.6, 6.0 and 8.5. In all scenarios, a nearly unanimous increase in lake temperatures and disappearance of lake ice is projected. The largest reductions in ice thickness and duration will occur in northern latitudes and coastal regions, respectively. Simulations show a greater shift in the timing of ice break-up rather than the onset of ice cover. These geographical and seasonal trends require further statistical analysis to clarify their significance. In general, discrepancies in the projected magnitudes of change for lake ice and temperature under different RCPs underline the benefit of mitigating climate change as preventative to large changes in the biogeochemistry and ecology of lakes.

The article presents theoretical notions concerning possibility of using the lake ice sheets for performing potential flight operations. Theoretical discourse was verified ,,in situ" on national reservoirs in the warminsko- mazurski region. Presented complex of issues and final conclusions allow applying aforementioned areas for flight operations in event of force majeure.

The changing characteristics of lake ice phenology over the Tibetan Plateau (TP) are investigated using historical satellite retrieved datasets during 2002–15 in this study. The results indicate that the freezing process mainly starts in December, and the ice melting process generally occurs in April for most lakes. However, the changes in lake ice phenology have varied depending on the location in recent years, with delayed break-up dates and prolonged ice durations in the southern TP, but no consistent changes have occurred in the lakes in the northern TP. Further analysis presents a close connection between the variation in the lake ice break-up date/ice duration over the southern TP and the winter North Atlantic Oscillation (NAO). The positive NAO generally excites an anomalous wave activity that propagates southward from the North Atlantic to North Africa and, in turn, strengthens the African–Asian jet stream at its entrance. Because of the blocking effect of the TP, the enhanced westerly jet can be divided into two branches and the south branch flow can deepen the India–Myanmar trough, which further strengthens the anomalous cyclonic circulation and water vapor transport. Therefore, the increased water vapor transport from the northern Indian Ocean to the southern region of the TP can increase the snowfall over this region. The increased snow cover over the lake acts as an insulating layer and lowers the lake surface temperature in the following spring by means of snow–ice feedback activity, resulting in a delayed ice break-up date and the increased ice duration of the lakes over the southern TP in recent years.

In this study, we assess the effect of the lake size on the accuracy of a threshold-based classification of ground-fast and floating lake ice from Sentinel-1 Synthetic Aperture Radar (SAR) imagery. For that purpose, two new methods (flood-fill and watershed method) are introduced and the results between the three classification approaches are compared regarding different lake size classes for a study area covering most of the Yamal Peninsula in Western Siberia. The focus is on April, the stage of maximum lake ice thickness, for the years 2016 and 2017. The results indicate that the largest lakes are likely most prone to errors by the threshold classification. The newly introduced methods seem to improve classification results. The results also show differences in fractions of ground-fast lake ice between 2016 and 2017, which might reflect differences in temperatures between the winters with severe impact on wildlife and freshwater fish resources in the region. Patterns of low backscatter responsible for the classification errors in the centre of the lakes were investigated and compared to the optical Sentinel-2 imagery of late-winter. Strong similarities between some patterns in the optical and SAR data were identified. They might be zones of thin ice, but further research is required for clarification of this phenomenon and its causes.

This article is an attempt to analyse and compare several selected parameters regarding ice phenomena using the correlation analysis of two lakes, which are benchmark lakes located in the central part of the Kashubian Lakeland. These lakes are: Raduńskie Górne, a larger one (387.2 hm2) and Ostrzyckie, a smaller one covering an area of 308.0 hm2. The analysis covered measurement sequences for the period 1971–2010. The material for analysis regarding data on ice phenomena on Lake Raduńskie Górne was obtained from the University of Gdańsk Limnological Station in Borucino while the data for Lake Ostrzyckie was obtained from the Institute of Meteorology and Water Management (IMGW). Relations between the following parameters of the ice regime were analysed: duration of the ice phenomena, duration of the ice cover, average and maximum thickness of the ice cover. The analysis and data comparisons have revealed that there are strong and very strong relations between the analysed parameters, which made it possible to develop proper statistical models. Hence, should there be a lack of data on the ice-cover pattern for one of these lakes it is possible to recreate them using the elaborated empirical models and data for the other.

Monitoring and analyzing the (decreasing) trends in lake freezing provides important information for climate research. Multi-temporal satellite images are a natural data source to survey ice on lakes. In this paper, we describe a method for lake ice monitoring, which uses low spatial resolution (250 m–1000 m) satellite images to determine whether a lake is frozen or not. We report results on four selected lakes in Switzerland: Sihl, Sils, Silvaplana and St. Moritz. These lakes have different properties regarding area, altitude, surrounding topography and freezing frequency, describing cases of medium to high difficulty. Digitized Open Street Map (OSM) lake outlines are back-projected on to the image space after generalization. As a pre-processing step, the absolute geolocation error of the lake outlines is corrected by matching the projected outlines to the images. We define the lake ice detection as a two-class (frozen, non-frozen) semantic segmentation problem. Several spectral channels of the multi-spectral satellite data are used, both reflective and emissive (thermal). Only the cloud-free (clean) pixels which lie completely inside the lake are analyzed. The most useful channels to solve the problem are selected with xgboost and visual analysis of histograms of reference data, while the classification is done with non-linear support vector machine (SVM). We show experimentally that this straight-forward approach works well with both MODIS and VIIRS satellite imagery. Moreover, we show that the algorithm produces consistent results when tested on data from multiple winters., International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-2, ISSN:1682-1750, ISSN:2194-9034, ISSN:1682-1777

Late-winter lake ice regimes are controlled by water depth relative to maximum ice thickness (MIT). When MIT exceeds maximum water depth, lakes freeze to the bottom with bedfast ice (BI) and when MIT is less than maximum water depth lakes have floating ice (FI). Both airborne radar and space-borne synthetic aperture radar (SAR) imagery (Ku-, X-, C-, and L-band) have been used previously to determine whether lakes have a BI or FI regime in a given year, across a number of years, or across large regions. In this study, we use a combination of ERS-1/2, RADARSAT-2, Envisat, and Sentinel-1 SAR imagery for seven lake-rich regions in Arctic Alaska to analyze lake ice regime extents and dynamics over a 25-year period (1992–2016). Our interactive threshold classification method determines a unique statistic-based intensity threshold for each SAR scene, allowing for the comparison of classification results from C-band SAR data acquired with different polarizations and incidence angles. Additionally, our novel method accommodates declining signal strength in aging extended-mission satellite SAR instruments. Comparison of SAR ice regime classifications with extensive field measurements from six years yielded a 93% accuracy. Significant declines in BI regimes were only observed in the Fish Creek area with 3% of lakes exhibiting transitional ice regimes—lakes that switch from BI to FI during this 25-year period. This analysis suggests that the potential conversion from BI to FI regimes is primarily a function of lake depth distributions in addition to regional differences in climate variability. Remote sensing of lake ice regimes with C-band SAR is a useful tool to monitor the associated thermal impacts on permafrost, since lake ice regimes can be used as a proxy for of sub-lake permafrost thaw, considered by the Global Climate Observing System as an Essential Climate Variable (ECV). Continued winter warming and variable snow conditions in the Arctic are expected and our long-term analysis provides a valuable baseline for predicting where potential future lake ice regimes shifts will be most pronounced.

A dataset of annual freshwater ice phenology was compiled for the largest river (Danube) and the largest lake (Lake Balaton) in eastern–central Europe, extending regular river and lake ice monitoring data through the use of historical observations and documentary records dating back to AD 1774 and AD 1885, respectively. What becomes clear is that the dates of the first appearance of ice and freeze-up have shifted, arriving 12–30 and 4–13 days later, respectively, per 100 years. Break-up and ice-off have shifted to earlier dates by 7–13 and 9–27 days/100 years, except on Lake Balaton, where the date of break-up has not changed significantly. The datasets represent a resource for (paleo)climatological research thanks to the strong, physically determined link between water and air temperature and the occurrence of freshwater ice phenomena. The derived centennial records of freshwater cryophenology for the Danube and Balaton are readily available for detailed analysis of the temporal trends, large-scale spatial comparison, or other climatological purposes. The derived dataset is publicly available via PANGAEA at https://doi.org/10.1594/PANGAEA.881056.

Freshwater lakes are an important source of atmospheric methane ([Formula: see text]); however, uncertainties associated with quantifying fluxes limit the accuracy of climate warming projections. Among emission pathways, ebullition (bubbling) is the principal and most challenging to account for given its spatial and temporal patchiness. When lakes freeze, many methane-rich bubbles escaping from lake-bottom sediments are temporarily trapped by downward-growing lake ice. Because bubble position is then seasonally fixed, we postulate that it should be possible to locate bubbles using a geophysical approach sensitive to perturbations in the ice-water interface and ice sheet structure generated by bubbles. We use ground-penetrating radar (GPR) to noninvasively quantify the amount of ebullition gas present in lake ice. To do this, an appropriate petrophysical transformation is required that relates radar wave velocity and volumetric gas content. We use laboratory experiments to show that electromagnetic models and volumetric mixing formulas were good representations of the gas volume-permittivity relationship. We found a standard deviation in dielectric permittivity between the models of 0.03, 0.03, and 0.02 for 20%, 50%, and 70% gas content, respectively. Second, by combining two GPR geometries (common and multioffset), we were able to locate bubbles and estimate gas volume with low uncertainty, with [Formula: see text] being the lowest uncertainty found and [Formula: see text] the largest. Finally, we found that GPR reflection patterns were associated with different previously identified ice-bubble classes. These geophysical results coupled with ancillary field measurements and ice-growth models also suggest how GPR can contribute to estimates of seasonal and annual ebullition fluxes over large spatiotemporal scales within and among lakes, thereby helping to reduce uncertainties in upscaled estimates of ecosystem methane emissions.

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of Great Slave Lake (GSL) and Great Bear Lake (GBL), large freshwater lakes in Northern Canada. However, limited information is available about ice thickness changes. Here, we present and validate a method to retrieve lake ice thickness using the CryoSat-2 (CS2) radar altimeter. These are the first satellite altimeter retrievals of lake ice thickness. Under optimal conditions, the CS2 signal is scattered from both the snow–ice and the ice–water interfaces, with returns from each interface being of sufficient power to be identified in the radar waveform. The distance between the scattering horizons is used to determine the ice thickness, similar to traditional ground penetrating radar measurements. The seasonal evolution of ice thickness of GBL and GSL is compared with in situ measurements, modeled ice thicknesses, and previous studies. The impact of ice and snow properties on signal penetration and the thickness retrieval are examined with synthetic aperture radar imagery. The CS2 ice thickness retrievals are able to observe the seasonal thickening of the lake ice and closely match the in situ measurements over both lakes ( $R > 0.65$ , $\text {RMSE} m). Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. Although not designed for lake ice studies, CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes.

Lake ice is a sensitive indicator for the environment and climate change. In the comparative study of climate change from the three poles (Antarctic, Arctic, and the Tibetan Plateau), the lake ice phenology which is mirrored the seasonal changes of lake ice, and it is an important index for assessing the synchronization and difference of climate change. Based on passive microwave, lake ice phenology of 48 lakes from 1978 to 2017 were extracted for northern Europe, Tibetan Plateau and Mongolia Plateau. A comparative analysis of the temporal variation for the lake ice phenology shows that from 1978 to 2017, the average ice cover duration showed a shortening trend of all regions, and the Northern Europe with the most dramatic shortening. The year of 2000 may be a turning of the lake ice cover duration in three regions. Especially in the northern Tibetan Plateau where is a trend of prolonged ice cover duration after 2000, and the delay of the break-up end date plays a more important role than the advance of the freeze-up start date. For the spatial changes, the variation of lake ice cover duration in the Tibetan Plateau shows a spatially aggregate phenomenon, and both the Northern Europe and Mongolian Plateau show changes with latitude. The comparison of lake ice phenology changes of three regions can provide more basis for climate characteristics analysis or climate change research. In the future work, various remote sensing data will be comprehensively used to obtain more information on lake ice phenology in High Mountain and cold regions. Meanwhile, it will combine other reanalysis data to find the reasons that affect the lake ice phenology, and provide support for the comparative study of the three-pole environment.

Synthetic Aperture Radar (SAR) remote sensing has become a valuable tool for sea ice monitoring. A recently proposed SAR configuration for Earth observation called compact polarimetric (CP) SAR could be a good compromised choice between conventional (single or dual) and fully polarimetric SAR for operational sea ice observation. Given its enhanced radar target information compared to conventional SAR systems over wider swath coverage compared to fully polarimetric SAR systems, CP SAR systems could play important role in the new generation of Earth observation systems. In this study, fully polarimetric SAR images were collected over the Resolute Bay area during the fall of 2017. Acquired images are used for the simulation of CP SAR images and the derivation of a set of 23 CP SAR parameters from each image. The derived CP parameters were analysed in relation to the ice thickness and salinity of lake ice and fast sea ice. Results are compared against backscattering and decomposition parameters derived from the fully polarimetric SAR imagery.

Algorithms for the generation of a bedfast/floating lake ice product from Sentinel-1A/B synthetic aperture radar (SAR) data were implemented, cross-compared, and validated for various permafrost regions (Alaska, Canada and Russia). The algorithms consisted of: 1) thresholding; 2) Iteration Region Growing with Semantics (IRGS); and 3) K-means. The thresholding algorithm (92.4%) was found to perform slightly better on average than the IRGS algorithm (90.1%), and to outperform K-means (85.3%). The thresholding algorithm was therefore selected for implementation of a processing chain to generate a novel bedfast/floating lake ice product. Using a time series of Sentinel-1 SAR data, the new map product shows the day of year (DOY) when the ice becomes bedfast or remains afloat for individual lake sections.

Seepage of geological methane through sediments of Arctic lakes might contribute conceivably to the atmospheric methane budget. However, the abundance and precise locations of such seeps are poorly quantified. For Lake Neyto, one of the largest lakes on the Yamal Peninsula in Northwestern Siberia, temporally expanding regions of anomalously low backscatter in C-band SAR imagery acquired in late winter and spring have been suggested to be related to seepage of methane from hydrocarbon reservoirs. However, this hypothesis has not been verified using in-situ observations so far. Similar anomalies have also been identified for other lakes on Yamal, but it is still uncertain whether or how many of them are related to methane seepage. This study aimed to document similar lake ice backscatter anomalies on a regional scale over four study regions (the Yamal Peninsula and Tazovskiy Peninsulas, the Lena Delta in Russia, the National Petroleum Reserve Alaska) during different years using a time series based approach on Google Earth Engine (GEE) that quantifies changes of σ0 from the Sentinel-1 C-band SAR sensor over time. An algorithm for assessing the coverage that takes the number of acquisitions and maximum time between acquisitions into account is presented, and differences between the main operating modes of Sentinel-1 are evaluated. Results show that better coverage can be achieved in extra wide swath (EW) mode, but interferometric wide swath (IW) mode data could be useful for smaller study areas and to substantiate EW results. A classification of anomalies on Lake Neyto from EW Δσ0 images derived from GEE showed good agreement with the classification presented in a previous study. Automatic threshold-based per-lake counting of years where anomalies occurred was tested, but a number of issues related to this approach were identified. For example, effects of late grounding of the ice and anomalies potentially related to methane emissions could not be separated efficiently. Visualizations of Δσ0 images likely reflect the temporal expansions of anomalies and are expected to be particularly useful for identifying target areas for future field-based research. Characteristic anomalies that clearly resemble the ones observed for Lake Neyto could be identified solely visually in the Yamal and Tazovskiy study regions. All data and algorithms produced in the framework of this study are openly provided to the scientific community for future studies and might potentially aid our understanding of geological lake seepage upon the progression of related field-based studies and corresponding evaluations of formation hypotheses.

Lake ice models are a vital tool for studying the response of ice-covered lakes to changing climates throughout the world. The Canadian Lake Ice Model (CLIMo) is a one-dimensional freshwater ice cover model that simulates Arctic and sub-Arctic lake ice cover well. Modelling ice cover in temperate regions has presented challenges due to the differences in ice composition between northern and temperate region lake ice. This study presents a comparison of measured and modelled ice regimes, with a focus on refining CLIMo for temperate regions. The study sites include two temperate region lakes (MacDonald Lake and Clear Lake, Central Ontario) and two High Arctic lakes (Resolute Lake and Small Lake, Nunavut) where climate and ice cover information have been recorded over three seasons. The ice cover simulations were validated with a combination of time lapse imagery, field measurements of snow depth, snow density, ice thickness and albedo data, and historical ice records from the Canadian Ice Database (for Resolute Lake). Simulations of High Arctic lake ice cover show good agreement with previous studies for ice-on and ice-off dates (MAE 6 to 8 days). Unadjusted simulations for the temperate region lakes show good ice-on timing, but an under-representation of ice thickness, and earlier complete ice-off timing (~3 to 5 weeks). Field measurements were used to adjust the albedo values used in CLIMo, which resulted in improvements to both simulated ice thickness (~3 cm MAE compared to manual measurements), and ice-off timing, within 0 to 7 days (2 days MAE) of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations, which further our knowledge of the response of temperate and High Arctic lake ice regimes to climate conditions.

Lake-ice properties at Lake Abashiri, Hokkaido, Japan, were examined using field observations and a 1-D thermodynamic model to clarify formation processes at mid-latitudes subject to significant snowfall as well as moderate air temperature. At all lake sites examined, the ice comprised two distinct layers: a snow ice (SI) layer on top and a congelation ice (CI) layer below. The SI layer occupied as much as 29–73% of the total ice thickness, a much greater fraction than that reported for lakes at Arctic high latitudes. In the model, the CI growth rate was estimated using the traditional heat budget method, while the SI growth rate was calculated assuming the excessive snowfall from the isostatic balance is converted to SI by a snow compression rate (β) with the surface melting rate added when the surface heat budget becomes positive. By tuning the value of β to the observational results of SI thickness, the model outcome successfully reproduced the observational thicknesses of CI and SI, and the break-up date of the lake. Essentially, the model findings show how snow and its formation into SI reduce, by about half, the seasonal variability of total ice thickness.

Several lake ice phenology studies from satellite data have been undertaken. However, the availability of long-term lake freeze-thaw-cycles, required to understand this proxy for climate variability and change, is scarce for European lakes. Long time series from space observations are limited to few satellite sensors. Data of the Advanced Very High Resolution Radiometer (AVHRR) are used in account of their unique potential as they offer each day global coverage from the early 1980s expectedly until 2022. An automatic two-step extraction was developed, which makes use of near-infrared reflectance values and thermal infrared derived lake surface water temperatures to extract lake ice phenology dates. In contrast to other studies utilizing thermal infrared, the thresholds are derived from the data itself, making it unnecessary to define arbitrary or lake specific thresholds. Two lakes in the Baltic region and a steppe lake on the Austrian–Hungarian border were selected. The later one was used to test the applicability of the approach to another climatic region for the time period 1990 to 2012. A comparison of the extracted event dates with in situ data provided good agreements of about 10 d mean absolute error. The two-step extraction was found to be applicable for European lakes in different climate regions and could fill existing data gaps in future applications. The extension of the time series to the full AVHRR record length (early 1980 until today) with adequate length for trend estimations would be of interest to assess climate variability and change. Furthermore, the two-step extraction itself is not sensor-specific and could be applied to other sensors with equivalent near- and thermal infrared spectral bands.