Climate warming is occurring at an unprecedented rate in the Arctic due to regional amplification, potentially accelerating land cover change. Measuring and monitoring land cover change utilizing optical remote sensing in the Arctic has been challenging due to persistent cloud and snow cover issues and the spectrally similar land cover types. Google Earth Engine (GEE) represents a powerful tool to efficiently investigate these changes using a large repository of available optical imagery. This work examines land cover change in the Lower Yenisei River region of arctic central Siberia and exemplifies the application of GEE using the random forest classification algorithm for Landsat dense stacks spanning the 32-year period from 1985 to 2017, referencing 1641 images in total. The semiautomated methodology presented here classifies the study area on a per-pixel basis utilizing the complete Landsat record available for the region by only drawing from minimally cloud- and snow-affected pixels. Climatic changes observed within the study area’s natural environments show a statistically significant steady greening (~21,000 km² transition from tundra to taiga) and a slight decrease (~700 km²) in the abundance of large lakes, indicative of substantial permafrost degradation. The results of this work provide an effective semiautomated classification strategy for remote sensing in permafrost regions and map products that can be applied to future regional environmental modeling of the Lower Yenisei River region.

A winter time series of ground-based (X- and Ku-bands) scatterometer and spaceborne synthetic aperture radar (SAR) (C-band) fully polarimetric observations coincident with in situ snow and ice measurements are used to identify the dominant scattering mechanism in bubbled freshwater lake ice in the Hudson Bay Lowlands near Churchill, Manitoba. Scatterometer observations identify two physical sources of backscatter from the ice cover: the snow-ice and ice-water interfaces. Backscatter time series at all frequencies show increases from the ice-water interface prior to the inclusion of tubular bubbles in the ice column based on in situ observations, indicating scattering mechanisms independent of double-bounce scatter. The co-polarized phase difference of interactions at the ice-water interface from both scatterometer and SAR observations is centered at 0° during the time series, also indicating a scattering regime other than double bounce. A Yamaguchi three-component decomposition of the RADARSAT-2 C-band time series is presented, which suggests the dominant scattering mechanism to be single-bounce off the ice-water interface with appreciable surface roughness or preferentially oriented facets, regardless of the presence, absence, or density of tubular bubble inclusions. This paper builds on newly established evidence of single-bounce scattering mechanism for freshwater lake ice and is the first to present a winter time series of ground-based and spaceborne fully polarimetric active microwave observations with polarimetric decompositions for bubbled freshwater lake ice.

Recent investigations using polarimetric decomposition and numerical models have helped to improve the understanding of how radar signals interact with lake ice. However, further research is needed on how radar signals are impacted by varying lake ice properties. Radiative transfer (RT) models provide one method of improving this understanding. These are the first published experiments using the snow microwave RT (SMRT) model to investigate the response of different frequencies (L-, C-, and X-band) at horizontal-horizontal (HH) and vertical-vertical (VV) polarizations using various incidence angles (20°, 30°, and 40°) to changes in ice thickness, porosity, bubble radius, and ice–water interface roughness. This is also the first use of SMRT in combination with a thermodynamic lake ice model. Experiments were for a lake with tubular bubbles and one without tubular bubbles under difference scenarios. An analysis of the backscatter response to different properties indicates that increasing ice thickness and layer porosity have little impact on backscatter from lake ice. X-band backscatter shows increased response to surface ice layer bubble radius; however, this was limited to other frequencies except at shallower incidence angles (40°). All three frequencies display the largest response to increasing root mean square (rms) height at the ice–water interface, which supports surface scattering at the ice–water interface as being the dominant scattering mechanism. These results demonstrate that the SMRT is a valuable tool for understanding the response of backscatter to changes in freshwater lake ice properties and could be used in the development of inversion models.

The rate of technological innovation within aquatic sciences outpaces the collective ability of individual scientists within the field to make appropriate use of those technologies. The process of in situ lake sampling remains the primary choice to comprehensively understand an aquatic ecosystem at local scales; however, the impact of climate change on lakes necessitates the rapid advancement of understanding and the incorporation of lakes on both landscape and global scales. Three fields driving innovation within winter limnology that we address here are autonomous real‐time in situ monitoring, remote sensing, and modeling. The recent progress in low‐power in situ sensing and data telemetry allows continuous tracing of under‐ice processes in selected lakes as well as the development of global lake observational networks. Remote sensing offers consistent monitoring of numerous systems, allowing limnologists to ask certain questions across large scales. Models are advancing and historically come in different types (process‐based or statistical data‐driven), with the recent technological advancements and integration of machine learning and hybrid process‐based/statistical models. Lake ice modeling enhances our understanding of lake dynamics and allows for projections under future climate warming scenarios. To encourage the merging of technological innovation within limnological research of the less‐studied winter period, we have accumulated both essential details on the history and uses of contemporary sampling, remote sensing, and modeling techniques. We crafted 100 questions in the field of winter limnology that aim to facilitate the cross‐pollination of intensive and extensive modes of study to broaden knowledge of the winter period.

Arctic lakes are sensitive to climate change, and the timing and duration of ice presence and absence (i.e., ice phenology) on the lake surface can be used as a climate indicator. In this study of Linnévatnet, one of the largest lakes on Svalbard, we compare inferences of lake ice duration from satellite data with continuously monitored lake water temperature and photographs from automatic cameras. Visible surface reflectance data from the moderate-resolution imaging spectroradiometer (MODIS) were used to observe the change in the lake-wide mean surface reflectance of Linnévatnet from 2003–2019, and smoothing splines were applied to the to determine the date of summer ice-off (also called “break-up end”—BUE). Similarly, BUE and fall ice-on (or “freeze-up end”—FUE) were determined from lake-wide mean time series of Sentinel-1 microwave backscatter from 2014–2019. Overall, the ice timing dates identified from the satellite observations agree well with the in-situ observations (RMSE values of approximately 2–7 days for BUE and FUE, depending on the method and in-situ dataset), lending confidence to the accuracy of remote sensing of lake ice phenology in remote Arctic regions. Our observations of Linnévatnet indicate that BUE dates do not have a significant trend, while FUE dates have been occurring approximately 1.5 days later per year during the study period. These results support an overall decrease in annual duration of lake ice cover in this part of Svalbard.