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This study introduces the first use of Global Navigation Satellite System Reflectometry (GNSS-R) for monitoring lake ice phenology. This is demonstrated using Qinghai Lake, Tibetan Plateau, as a case study. Signal-to-Noise Ratio (SNR) values obtained from the Cyclone GNSS (CYGNSS) constellation over four ice seasons (2018 to 2022) were used to examine the impact of lake surface conditions on reflected GNSS signals during open water and ice cover seasons. A moving t-test (MTT) algorithm was applied to time-varying SNR values allowing for the detection of lake ice at daily temporal resolution. Strong agreement is observed between ice phenology records derived from CYGNSS and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Differences during freeze-up (i.e., the period starting with the first appearance of ice on the lake until the lake becomes fully ice covered) ranged from 3 to 21 days with a mean bias error (MBE) and mean absolute error (MAE) of 10 days, while those during breakup (i.e., the period beginning with the first pixel of open water and ending when the whole lake becomes ice-free) ranged from 3 to 18 days (MBE and MAE: 6 and 7 days, respectively). Observations during the breakup period revealed the sensitivity of GNSS reflected signals to the onset of surface (snow and ice) melt before the appearance of open water conditions as determined from MODIS. While the CYGNSS constellation is limited to the coverage of lakes between 38° S and 38° N, the approach presented herein will be applicable to data from other GNSS-R missions that provide opportunities for the monitoring of ice phenology from large lakes globally (e.g., Spire constellation of satellites).

Lake ice plays a critical role in local energy balances and provides crucial socio-economic services such as travel between communities and transportation of goods during winter months. However, over the past 40 years, the number of in situ observations has declined. Additionally, increasing temperatures lead to an increasing number of melt events throughout the season, resulting in the formation of more snow ice and slush layers. The increase in wet ice conditions poses a challenge in monitoring lake ice using active microwave technologies (e.g., synthetic aperture radar) and can be a risk to those who use ice cover as an essential travel route. This study focuses on Lake Oulujärvi in Finland during the 2020-2021 ice season. Using the snow microwave radiative transfer (SMRT) model, backscatter was modelled using observations of dry and wet conditions from the field. Snow density, snow depth, microstructure data, and ice thickness data collected during the field campaign helped parameterize the Snow Microwave Radiative Transfer (SMRT) model. Simulations under dry conditions showed that increasing roughness at the ice-water interface had the largest increase in backscatter. However, when the water content of the overlying snow layers increased, the roughness of the interface with the highest moisture content became the dominant interface impacting backscatter. Melt-freeze events throughout the ice season had a prolonged impact on backscatter resulting in increases of >3.69 dB. Larger increases in backscatter due to higher moisture were a result of larger dielectric contrasts created between overlying dry snow on slush layers. Improved understanding of the impact of wet conditions on backscatter can help to improve the monitoring of hazardous lake ice conditions and aid in the further development of inversion models for lake ice properties.

Monitoring of lake ice is important to maintain transportation routes but in recent decades the number of in situ observations have declined. Remote sensing has worked to fill this gap in observations, with active microwave, particularly synthetic aperture radar (SAR), being a crucial technology. However, the impact of wet conditions on radar and how interactions change under these conditions has been largely ignored. It is important to understand these interactions as warming conditions are likely to lead to an increase in the occurrence of slush layers. This study works to address this gap using the snow microwave radiative transfer (SMRT) model to conduct forward modelling experiments of backscatter for Lake Oulujärvi in Finland. Experiments were conducted under dry conditions, under moderate wet conditions, and under saturated conditions. These experiments reflected field observations during the 2020–2021 ice season. Results of the dry snow experiments support the dominance of surface scattering from the ice-water interface. However, conditions where layers of wet snow are introduced show that the primary scattering interface changes depending on the location of the wet layer. The addition of a saturated layer at the ice surface results in the highest backscatter values due to the larger dielectric contrast created between the overlying dry snow and the slush layer. Improving the representation of these conditions in SMRT can also aid in more accurate retrievals of lake ice properties such as roughness, which is key for inversion modelling of other properties such as ice thickness.

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 is an important feature of the physical landscape at northern latitudes. Not only does the presence of lake ice help modulate weather and climate but it also plays an important role in travel between northern communities. Furthermore, the thickness of ice cover can impact overwintering fish habitat and access to fresh water during winter months. Lake ice cover and lake ice thickness are two notable climate proxies identified by the Global Climate Observing System (GCOS) as Essential Climate Variables (ECVs). Changes in ice thickness can impact lakes freezing to bed which in turn affect permafrost thaw while longer periods of open water can impact heat transfer to the atmosphere. With recent climate warming in the Northern Hemisphere, there are general trends towards thinner ice and an increase in the length of the open water season. However, the paucity of in-situ observations in many countries has led to remote sensing playing an ever-increasing role for lake ice monitoring. This paper reviews progress in lake ice research conducted using active microwave remote sensing over the last 50 years and highlights areas where future developments are needed. Analysis of the literature found that the diversity of study areas where this work is taking place has increased, incorporating more lake sites from the mid-latitudes in North America, Europe, and Asia. Additionally, clear connections can be made between the launch of synthetic aperture radar (SAR) satellites and patterns in the use of certain radar frequencies in scientific studies. This review also discusses evolving theories concerning the scattering mechanisms associated with lake ice as well as the current state of lake ice cover and ice thickness retrieval methods. The paper concludes with a suite of recommendations for future research, highlighting work that is needed on: 1) advancing our understanding of the response of active microwave signals to lake ice properties; 2) applying machine learning algorithms to lake ice classification; 3) further exploring the retrieval of ice thickness from imaging SAR and radar altimetry data; and 4) other considerations such as data availability and the topic of snow on lake ice.

Lakes are a prominent geographic feature in northern landscapes and play an important role in understanding regional climate systems. In order to better model changes within climate systems it is important to study lake ice processes. Although the availability of records for lake ice through ground measurements has declined in recent years, the increased use of remote sensing provides an alternative to this. Using a pre-classified snow and ice remote sensing product with a 500 metre resolution, based on images from the Moderate Resolution Imaging Spectroradiometer (MODIS/MOD10A1), and the use of measured and reanalysis temperature data, this study evaluated lake ice phenology dates in connection to recent trends in temperature and 0°C isotherms within Ontario and Manitoba between 2001 and 2014. Temperature trends indicated both regional warming and cooling, with significant cooling observed in Southern Ontario (p < 0.05) and significant warming in Southern Manitoba (p < 0.1) during the fall. Spatial analysis of the trends in the lake ice data showed significant clustering of significant trends in ice on dates (p < 0.01). When analyzing the trends in ice phenology in connection to the trends in temperature, it was found that 70% of lakes experienced a change in the ice on date with the expected change in temperature and 85% of lakes for ice off date. When shifting ice on and ice off dates are investigated in relation to 0°C isotherms, it was seen that 80% of ice on dates and 100% of ice off dates shifted in sync with the isotherm dates. This demonstrates that the ice phenology of lakes in Ontario and Manitoba, Canada is responding to short-term variability in temperature. The MODIS product could be used to investigate ice phenology on a large scale and contribute towards expanding existing records of ice phenology. Establishing long term ice records could be a valuable asset for other research ranging from water balance studies to the response of lake biota under changing climate.

Lake ice thickness is a sensitive indicator of climate change largely through its dependency on near-surface air temperature and on-ice snow mass (depth and density). Monitoring of the seasonal variations and trends in ice thickness is also important for the operation of winter ice roads that northern communities rely on for the movement of goods as well as for cultural and leisure activities (e.g., snowmobiling). Therefore, consistent measurements of ice thickness over lakes is important; however, field measurements tend to be sparse in both space and time in many northern countries. Here, we present an application of L-band frequency Global Navigation Satellite System (GNSS) Interferometric Reflectometry (GNSS-IR) for the estimation of lake ice thickness. The proof of concept is demonstrated through the analysis of Signal-to-Noise Ratio (SNR) time series extracted from Global Positioning System (GPS) constellation L1 band raw data acquired between 8 and 22 March (2017 and 2019) at 14 lake ice sites located in the Northwest Territories, Canada. Dominant frequencies are extracted using Least Squares Harmonic Estimation (LS-HE) for the retrieval of ice thickness. Estimates compare favorably with in-situ measurements (mean absolute error = 0.05 m, mean bias error = −0.01 m, and root mean square error = 0.07 m). These results point to the potential of GPS/GNSS-IR as a complementary tool to traditional field measurements for obtaining consistent ice thickness estimates at many lake locations, given the relatively low cost of GNSS antennas/receivers.

Lake ice is an important component in understanding the local climate as changes in temperature have an impact on the timing of key ice phenology events. In recent years, there has been a decline in the in-situ monitoring of lake ice events in Canada and microwave remote sensing imagery from synthetic aperture radar (SAR) is more widely used due to the high spatial resolution and response of backscatter to the freezing and melting of the ice surface. RADARSAT-2 imagery was used to develop a threshold-based method for determining lake ice events for mid-latitude lakes in Central Ontario from 2008 to 2017. Estimated lake ice phenology events are validated with ground-based observations and are compared against the Moderate Resolution Imaging Spectroradiometer (MODIS band 2). The threshold-based method was found to accurately identify 12 out of 17 freeze events and 13 out of 17 melt events from 2015–2017 when compared to ground-based observations. Mean absolute errors for freeze events ranged from 2.5 to 10.0 days when compared to MODIS imagery while the mean absolute error for water clear of ice (WCI) ranged from 1.5 to 7.1 days. The method is important for the study of mid-latitude lake ice due to its unique success in detecting multiple freeze and melting events throughout the ice season.

The invasive emerald ash borer (EAB, Agrilus planipennis Fairmaire) infects and eventually kills endemic ash trees and is currently spreading across the Great Lakes region of North America. The need for early detection of EAB infestation is critical to managing the spread of this pest. Using WorldView-2 (WV2) imagery, the goal of this study was to establish a remote sensing-based method for mapping ash trees undergoing various infestation stages. Based on field data collected in Southeastern Ontario, Canada, an ash health score with an interval scale ranging from 0 to 10 was established and further related to multiple spectral indices. The WV2 image was segmented using multi-band watershed and multiresolution algorithms to identify individual tree crowns, with watershed achieving higher segmentation accuracy. Ash trees were classified using the random forest classifier, resulting in a user’s accuracy of 67.6% and a producer’s accuracy of 71.4% when watershed segmentation was utilized. The best ash health score-spectral index model was then applied to the ash tree crowns to map the ash health for the entire area. The ash health prediction map, with an overall accuracy of 70%, suggests that remote sensing has potential to provide a semi-automated and large-scale monitoring of EAB infestation.