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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.

The prolonged ice cover inherent to alpine lakes incurs unique challenges for aquatic life, which are compounded by recent shifts in the timing and duration of ice cover. To understand the responses of alpine zooplankton, we analyzed a decade (2009–2019) of open-water samples of Daphnia pulicaria and Hesperodiaptomus shoshone for growth, reproduction and ultraviolet radiation tolerance. Due to reproductive differences between taxa, we expected clonal cladocerans to exhibit a more rapid response to ice-cover changes relative to copepods dependent on sexual reproduction. For D. pulicaria, biomass and melanization were lowest after ice clearance and increased through summer, whereas fecundity was highest shortly after ice-off. For H. shoshone, biomass and fecundity peaked later but were generally less variable through time. Among years, ice clearance date varied by 49 days; years with earlier ice-out and a longer growing season supported higher D. pulicaria biomass and clutch sizes along with greater H. shoshone fecundity. While these large-bodied, stress tolerant zooplankton taxa were relatively resilient to phenological shifts during the observation period, continued losses of ice cover may create unfavorably warm conditions and facilitate invasion by montane species, emphasizing the value of long-term data in assessing future changes to these sensitive ecosystems.

To improve the understanding of the seasonal evolution of the mass and heat budget of ice-covered lakes in the cold and arid climate zone, in-situ observations were collected during two winters (2016&ndash, 2017 and 2017&ndash, 2018) in Lake Wuliangsuhai, Inner Mongolia, China. The mean snow thickness was 5.2 and 1.6 cm in these winters, due to low winter precipitation. The mean ice thickness was 50.9 and 36.1 cm, and the ice growth rate was 3.6 and 2.1 mm day&minus, 1 at the lower boundary of ice. Analyses of mass and heat balance data from two winters revealed that the surface heat budget was governed by solar radiation and terrestrial radiation. The net heat flux loss of the ice was 9&ndash, 22 W m&minus, 2, affected by the snow and ice thickness. Compared to boreal lakes, Lake Wuliangsuhai received more solar radiation and heat flux from the water. The ice temperature had a strong diurnal variation, which was produced by the diurnal cycles of solar radiation, and air and water temperatures. These results expand our knowledge of the evolution of mass and heat balance in temperate lakes of mid-latitude arid areas.

Extending ice records and standardizing sampling protocols are among recommendations to help researchers better predict how changing ice cover will affect aquatic ecosystems.

© 2020 Copernicus GmbH. All rights reserved. Lake ice is a strong climate indicator and has been recognised as part of the Essential Climate Variables (ECV) by the Global Climate Observing System (GCOS). The dynamics of freezing and thawing, and possible shifts of freezing patterns over time, can help in understanding the local and global climate systems. One way to acquire the spatiooral information about lake ice formation, independent of clouds, is to analyse webcam images. This paper intends to move towards a universal model for monitoring lake ice with freely available webcam data. We demonstrate good performance, including the ability to generalise across different winters and lakes, with a state-of-the-art Convolutional Neural Network (CNN) model for semantic image segmentation, Deeplab v3+. Moreover, we design a variant of that model, termed Deep-U-Lab, which predicts sharper, more correct segmentation boundaries. We have tested the model's ability to generalise with data from multiple camera views and two different winters. On average, it achieves Intersection-over-Union (IoU) values of ≈71% across different cameras and ≈69% across different winters, greatly outperforming prior work. Going even further, we show that the model even achieves 60% IoU on arbitrary images scraped from photo-sharing websites. As part of the work, we introduce a new benchmark dataset of webcam images, Photi-LakeIce, from multiple cameras and two different winters, along with pixel-wise ground truth annotations., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 5 (2), ISSN:2194-9042, ISSN:2194-9050

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.

Lake ice models can be used to study the latitudinal differences of current and projected changes in ice covered lakes under a changing climate. 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 composition between northern and temperate 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 the High Arctic 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 both an underestimation in ice thickness (~ 4 to 18 cm) and ice-off timing (~ 25 to 30 days). Field measurements were used to adjust the albedo parameterization used in CLIMo, which resulted in improvements to both simulated ice thickness, within 0.1 cm to 10 cm of manual measurements, and ice-off timing, within 1 to 7 days of observations. These findings suggest regionally specific measurements of albedo can improve the accuracy of lake ice simulations. These results further our knowledge regarding of the response of temperate and High Arctic lake ice regimes to climate conditions.

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.

Lake ice is a dominant component of Canada’s landscape and can act as an indicator for how freshwater aquatic ecosystems are changing with warming climates. While lake ice monitoring through government networks has decreased in the last three decades, the increased availability of remote sensing images can help to provide consistent spatial and temporal coverage for areas with annual ice cover. Synthetic aperture radar (SAR) data are commonly used for lake ice monitoring, due to the acquisition of images in any condition (time of day or weather). Using Sentinel-1 A/B images, a high-density time series of SAR images was developed for Lake Hazen in Nunavut, Canada, from 2015−2018. These images were used to test two different methods of monitoring lake ice phenology: one method using the first difference between SAR images and another that applies the Otsu segmentation method. Ice phenology dates determined from the two methods were compared with visual interpretation of the Sentinel-1 images. Mean errors for the pixel comparison of the first difference method ranged 3−10 days for ice-on and ice-off, while average error values for the Otsu method ranged 2−10 days. Mean errors for comparisons of different sections of the lake ranged 0−15 days for the first difference method and 2−17 days for the Otsu method. This research demonstrates the value of temporally consistent image acquisition for improving the accuracy of lake ice monitoring.

Lake ice, as part of the Essential Climate Variable (ECV) lakes, is an important indicator to monitor climate change and global warming. The spatio-temporal extent of lake ice cover, along with the timings of key phenological events such as freeze-up and break-up, provide important cues about the local and global climate. We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network. In previous studies that used optical satellite imagery for lake ice monitoring, frequent cloud cover was a main limiting factor, which we overcome thanks to the ability of microwave sensors to penetrate clouds and observe the lakes regardless of the weather and illumination conditions. We cast ice detection as a two class (frozen, non-frozen) semantic segmentation problem and solve it using a state-of-the-art deep convolutional network (CNN).We report results on two winters (2016–17 and 2017–18) and three alpine lakes in Switzerland. The proposed model reaches mean Intersection-over-Union (mIoU) scores >90% on average, and >84% even for the most difficult lake. Additionally, we perform cross-validation tests and show that our algorithm generalises well across unseen lakes and winters., ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-3-2020, ISSN:2194-9042, ISSN:2194-9050

Studying of characteristics of the Lake Onega ice regime and investigating of the climate influence on the formation and destruction of the ice cover requires a continuous chronological series of data on the Lake ice coverage. Ice cover is the percentage of the ice area to the total area of the lake. In 1955–1990, calculations of the ice coverage of the Lake were based on the use of the results of airborne ice reconnaissance. On average for this period, from 5 to 15 values were annually obtained, which was not enough for a comprehensive analysis of the ice coverage variability. In this paper, for the frst time, a daily series of values of the ice coverage of the Lake Onega for the period 2000–2018 had been formed basing on the results of a combined analysis of the following satellite data sets: NSIDC, NOAA NESDIS, corrected by data of the satellite MODIS sensor. Values from November to May were grouped from the above data sets without regards for years of observations, and then the regression analysis of these values made possible to create a model (a polynomial of the 8th degree) of the chronological course of the ice coverage during the period of the ice phenomena existence on the Lake Onega. Te coefcient of determination of the model is 0.74, and the error in determining the ice coverage is 21%. Te average dates of the beginning, end, and duration of periods of the formation (from November 25th to January 19th), the destruction (from April 13th to May 17th) of the ice cover, as well as the total freeze-up time (from January 20th to April 12th) on the Lake Onega for the period 2000–2018 was determined. Te period of ice phenomena on the Lake Onega on average lasts for almost half a year (175 days), of which a signifcant part of the time is a complete freeze-up (84 days). It was found that the rate of formation of the ice cover (1.76% per day) on the Lake Onega is 1.65 times smaller than the rate of its destruction (2.90% per day), which approximately corresponds to similar results obtained for the Lake Ladoga (1.5 times).

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.

Lakes play an important role in terrestrial ecosystems and have a significant impact on human production and life. Remote sensing techniques have been widely used to monitor changes in lakes. Several studies have been conducted pertaining to extraction of lake bodies using optical remote sensing images; however, owing to the influence of ice and snow in the lake and other parameters such as noise, the accuracy and stability of lake extraction still need to be improved. This study designed a new method by combing K-means clustering and the flood fill method (KCFFM) to improve the accuracy and stability of extraction results from complex backgrounds. Using Landsat 8 Operational Land Imager (OLI) data for five lakes in Central Asia, the accuracy and stability of KCFFM in summer and winter were evaluated and compared with those of five other methods. KCFFM showed the highest accuracy and stability among all the methods, especially during the lake ice period. For KCFFM, the Kappa coefficient was greater than 0.97 and 0.92 and the overall accuracy was greater than 99% and 98% in summer and winter, respectively. The area error rate of KCFFM was less than 3%, except for Lake Alakol (less than 10%). In addition, KCFFM significantly decreased the area error rate compared to other methods (1% to 15% in summer and 10% to 50% in winter). The proposed method optimized the time continuity, accuracy, and stability of lake body extraction. Thus, KCFFM can provide valuable basic data for monitoring lake water bodies.