Evaluation of Machine Learning Algorithms for the Classification of Lake Ice and Open Water from Sentinel-3 SAR Altimetry Waveforms

Lakes cover a significant fraction of the landscape in many northern countries. They play a key role in regulating weather and climate and also have a significant impact on northern communities since the presence (or absence), extent and thickness of lake ice affect transportation (ice roads), food availability, recreational activities, and tourism in wintertime. The drastic decline in in-situ observations of lake ice phenology (i.e., freeze-up and break-up dates and ice cover duration) and lake ice thickness globally over the last three decades make remote sensing technology a viable means for monitoring lake ice conditions. Although satellite radar altimetry has been used in various cryospheric and hydrological studies, little work has been conducted on lake ice compared to, for example, sea ice and the estimation of lake water levels. This study was carried out using Sentinel-3A/B SAR altimetry data acquired over three ice seasons (2018-2019, 2019-2020 and 2020-2021) at 11 large lakes across the Northern Hemisphere. We explored the information provided by radar waveforms to discriminate between open water, first (young) ice, growing ice and melting ice using machine learning models. To characterize the waveforms, seven waveform parameters were derived: Leading Edge Width (LEW), Offset Center of Gravity (OCOG) Width, Pulse Peakiness (PP), backscatter coefficient (Sigma0), late tail to peak power (LTTP), early tail to peak power (ETTP) and the maximum value of the echo power. Four machine learning algorithms including Random Forest (RF), Gradient Boosting Trees (GBT), K Nearest Neighbour (KNN) and Support Vector Machine (SVM) classifiers were tested to assess their capability in classifying the lake surfaces across all years. Manual class labelling based on Sentinel-3 Synthetic Aperture Radar Altimeter (SRAL) waveforms and complementary satellite data (Sentinel-1 imaging SAR data, Sentinel-2 Multispectral Instrument (MSI) Level 1C data, and MODIS Aqua/Terra data) was performed to create training and test samples for the classifiers. Accuracies greater than 95% were achieved across all classifiers using a 4-parameter combination (Sigma0, PP, OCOG Width, and LEW). Amongst all waveform parameters, Sigma0, OCOG width and PP were found to be the most important parameters for discriminating between lake ice and open water. Despite showing comparable classification performances in the overall classification, RF and KNN are found to be a better fit for global lake ice mapping as both are less sensitive to their internal hyperparameters and have faster processing speeds. Additionally, consistent results (>93.7% accuracy in all classifiers) achieved on the accuracy assessment carried out for each lake revealed the strength of the classifiers for spatial transferability. Implementation of RF and KNN could be valuable in a pre-or post-processing step for identifying lake surface conditions under which the retrieval of water level and ice thickness may be limited or not possible and, therefore, inform algorithms currently used for the generation of operational or research products. While the research focused on 11 of the largest lakes of the Northern Hemisphere, the classification approach has potential for application on smaller lakes too since SAR mode data (~300 m along-track resolution) is used in the study

Utilizing Ground-Penetrating Radar to Estimate the Spatial Distribution of Snow Depth over Lake Ice in Canada’s Sub-Arctic

With the expected rise in air temperature, it becomes important to understand how snow will respond in different climate scenarios. The presence of snow over lake ice largely influences the ice thickness, and as Canada’s Arctic and sub-arctic regions are experiencing warming at twice the global rate, concerns rise as changes in the snowpack will significantly impact northern communities that rely on lake ice as a means of transportation, source for drinking water, and feeding their families. The distribution of snow depth is highly sensitive to changes in climate over time, as such a slight increase in air temperature or change in precipitation can substantially alter snowpack dynamics, which in-turn, directly impacts the rate of lake ice growth. The heterogeneity of snow depth over lake ice is driven by wind redistribution and snowpack metamorphism which creates an inconsistent ice thickness across the lake. Currently, daily snow depth measurements are represented as one value, collected at a weather station on land, near lake shorelines, but previous studies show that this data is not representative of the distribution of snow across different landscapes, more specifically lake ice. Due to the exposed nature of lakes, it is shown that snow depth will be redistributed greatly over lake ice, as there is a lack of vegetation compared to land surfaces with differences in topography. To identify the snow spatial distribution, extensive snow depth measurements must be collected across the entire lake. However, the collection of accurate snow depth measurements over lake ice is challenging and requires a great deal of time spent in the field. Studies have explored the use of remote sensing techniques to map snow distribution over land, however our understanding of such over lake ice is minimal. Accurate measurements of the spatial distribution of snow depth over lake ice is limited due to logistical difficulties in manual measurement techniques (i.e., ruler, snow depth probe). This study presents the use of ground-penetrating radar (GPR) and in-situ observations (snow depth and density) to develop a systematic method to estimate the spatial distribution of snow depth over lake ice. Focused on four lakes located in the North Slave Region, Northwest Territories (Landing Lake, Finger Lake, Vee Lake, Long Lake) the snow depth is derived using GPR two-way travel time. Through utilizing a combination of ground-based techniques, this study proposed a methodology to ease the collection process required to get accurate snow depth measurements on a larger spatial scale than current methods allow. The findings of this thesis will benefit the snow and ice community as we can increase our availability of accurate snow depth data over lake ice through an efficient method of collecting larger snow depth datasets. Specifically, with the availability of snow depth data over lake ice, the accuracy of thermodynamic lake ice model can be improved significantly

Application of GNSS Interferometric Reflectometry for Lake Ice Studies

This thesis examines the use of Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) for the study of lake ice with a particular focus on the estimation of ice thickness. Experiments were conducted in two lake regions: (1) sub-Arctic lakes located near Yellowknife and Inuvik in the Northwest Territories during March 2017 and 2019, and (2) MacDonald Lake, Haliburton, Ontario, which is known as a mid-latitude lake, during the ice season of 2019-2020. For both regions, GNSS-IR results are compared and validated against in-situ ice and on-ice snow measurements, and also with ice thickness derived from thermodynamic lake ice models. In the first experiment, GNSS antennas were installed directly on the ice surface and the ice thickness at each site was estimated by analyzing the signal-to-noise ratio (SNR) of the reflected GNSS signals. The GNSS-IR capability of ice thickness estimation tested on sub-Arctic lakes results in a root mean square error (RMSE) of 0.07 m, a mean bias error (MBE) of -0.01 m, and a correlation of 0.66. At MacDonald Lake, a GNSS antenna was mounted on a 5-m tower on the shore to collect reflected signals from the lake surface. The Least-Squares Harmonic Estimation (LS-HE) method was applied to retrieve higher SNR frequencies in order to estimate the depths of multiple layers within lake ice and the overlaying snowpack. Promising results were obtained from this experiment; however, ice thickness estimation using GNSS-IR at this mid-latitude lake site was found to be highly dependent on the presence or absence of wet layers such as slush at the snow-ice interface and wet snow above that interface. On colder days, when there was a lower chance for the formation of wet layers, ice thickness could be estimated with a correlation of 0.68, RMSE of 0.07 m, and MBE of -0.02 m. In addition, GNSS-IR showed the potential for determining the freeze-up and break-up timing based on the SNR amplitude of reflected signals. The novel work presented in this thesis points to the potential of using reflected signals acquired by recent (e.g. Cyclone Global Navigation Satellite System (CYGNSS) and TechDemoSat-1 (TDS-1)) and future GNSS-R missions for lake ice investigations

Remote Sensing of Environmental Changes in Cold Regions

This Special Issue gathers papers reporting recent advances in the remote sensing of cold regions. It includes contributions presenting improvements in modeling microwave emissions from snow, assessment of satellite-based sea ice concentration products, satellite monitoring of ice jam and glacier lake outburst floods, satellite mapping of snow depth and soil freeze/thaw states, near-nadir interferometric imaging of surface water bodies, and remote sensing-based assessment of high arctic lake environment and vegetation recovery from wildfire disturbances in Alaska. A comprehensive review is presented to summarize the achievements, challenges, and opportunities of cold land remote sensing

Comparison of sea-ice freeboard distributions from aircraft data and cryosat-2

The only remote sensing technique capable of obtain- ing sea-ice thickness on basin-scale are satellite altime- ter missions, such as the 2010 launched CryoSat-2. It is equipped with a Ku-Band radar altimeter, which mea- sures the height of the ice surface above the sea level. This method requires highly accurate range measure- ments. During the CryoSat Validation Experiment (Cry- oVEx) 2011 in the Lincoln Sea, Cryosat-2 underpasses were accomplished with two aircraft, which carried an airborne laser-scanner, a radar altimeter and an electro- magnetic induction device for direct sea-ice thickness re- trieval. Both aircraft flew in close formation at the same time of a CryoSat-2 overpass. This is a study about the comparison of the sea-ice freeboard and thickness dis- tribution of airborne validation and CryoSat-2 measure- ments within the multi-year sea-ice region of the Lincoln Sea in spring, with respect to the penetration of the Ku- Band signal into the snow

Forward Modelling of Multifrequency SAR Backscatter of Snow-Covered Lake Ice: Investigating Varying Snow and Ice Properties Within a Radiative Transfer Framework

Lakes are a key feature in the Northern Hemisphere landscape. The coverage of lakes by ice cover has important implications for local weather conditions and can influence energy balance. The presence of lake ice is also crucial for local economies, providing transportation routes, and acting as a source of recreation/tourism and local customs. Both lake ice cover, from which ice dates and duration can be derived (i.e., ice phenology), and ice thickness are considered as thematic variables of lakes as an essential climate variable by the Global Climate Observing System (GCOS) for understanding how climate is changing. However, the number of lake ice phenology ground observations has declined over the past three decades. Remote sensing provides a method of addressing this paucity in observations. Active microwave remote sensing, in particular synthetic aperture radar (SAR), is popular for monitoring ice cover as it does not rely on sunlight and the resolution allows for the monitoring of small and medium-sized lakes. In recent years, our understanding of the interaction between active microwave signals and lake ice has changed, shifting from a double bounce mechanism to single bounce at the ice-water interface. The single bounce, or surface scattering, at the ice-water interface is due to a rough surface and high dielectric contrast between ice and water. However, further work is needed to fully understand how changes in different lake ice properties impact active microwave signals. Radiative transfer modelling has been used to explore these interactions, but there are a variety of limitations associated with past experiments. This thesis aimed to faithfully represent lake ice using a radiative transfer framework and investigate how changes in lake ice properties impact active microwave backscatter. This knowledge was used to model backscatter throughout ice seasons under both dry and wet conditions. The radiative transfer framework used in this thesis was the Snow Microwave Radiative Transfer (SMRT) model. To investigate how broad changes in ice properties impact microwave backscatter, SMRT was used to conduct experiments on ice columns representing a shallow lake with tubular bubbles and a deep lake without tubular bubbles at L/C/X-band frequencies. The Canadian Lake Ice Model (CLIMo) was used to parameterize SMRT. Ice properties investigated included ice thickness, snow ice bubble radius and porosity, root mean square (RMS) height of the ice-water interface, correlation length of the ice-water interface, and tubular bubble radius and porosity. Modelled backscatter indicated that changes in ice thickness, snow ice porosity, and tubular bubble radius and porosity had little impact on microwave backscatter. The property that had the largest impact on backscatter was RMS height at the ice-water interface, confirming the results of other recent studies. L and C-band frequencies were found to be most sensitive to changes in RMS height. Bubble radius had a smaller impact on backscatter, but X-band was found to be most sensitive to changes in this property and would be a valuable frequency for studying surface ice conditions. From the results of these initial experiments, SMRT was then used to simulate the backscatter from lake ice for two lakes during different winter seasons. Malcolm Ramsay Lake near Churchill, Manitoba, represented a shallow lake with dense tubular bubbles and Noell lake near Inuvik, Northwest Territories, represented a deep lake with no tubular bubbles. Both field data and CLIMo simulations for the two lakes were used to parameterize SMRT. Because RMS height was determined to be the ice property that had the largest impact on backscatter, simulations focused on optimizing this value for both lakes. Modelled backscatter was validated using C-band satellite imagery for Noell Lake and L/C/X-band imagery for Malcolm Ramsay Lake. The root mean square error values for both lakes ranged from 0.38 to 2.33 dB and Spearman’s correlation coefficient (ρ) values >0.86. Modelled backscatter for Noell Lake was closer to observed values compared to Malcolm Ramsay Lake. Optimized values of RMS height provided a better fit compared to a stationary value and indicated that roughness likely increases rapidly at the start of the ice season but plateaus as ice growth slows. SMRT was found to model backscatter from ice cover well under dry conditions, however, modelling backscatter under wet conditions is equally important. Detailed field observations for Lake Oulujärvi in Finland were used to parameterize SMRT during three different conditions. The first was lake ice with a dry snow cover, the second with an overlying layer of wet snow, and the third was when a slush layer was present on the ice surface. Experiments conducted under dry conditions continued to support the dominance of scattering from the ice-water interface. However, when a layer of wet snow or slush layer was introduced the dominant scattering interface shifted to the new wet layer. Increased roughness at the boundary of these wet layers resulted in an increase in backscatter. The increase in backscatter is attributed to the higher dielectric constant value of these layers. The modelled backscatter was found to be representative of observed backscatter from Sentinel-1. The body of work of this thesis indicated that the SMRT framework can be used to faithfully represent lake ice and model backscatter from ice covers and improved understanding of the interaction between microwave backscatter and ice properties. With this improved understanding inversion models can be developed to retrieve roughness of the ice-water interface, this could be used to build other models to estimate ice thickness based on other remote sensing data. Additionally, insights into the impact of wet conditions on radar backscatter could prove useful in identifying unsafe ice locations

Passive Microwave Remote Sensing of Ice Cover on Large Northern Lakes: Great Bear Lake and Great Slave Lake, Northwest Territories, Canada

Time series of brightness temperature (TB) measurement obtained at various frequencies by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) are investigated to determine ice phenology parameters and ice thickness on Great Bear Lake (GBL) and Great Slave Lake (GSL), Northwest Territories, Canada. TB measurements from the 6.9, 10.7, 18.7, 23.8, 36.5, and 89.0 GHz channels (H- and V- polarization) are compared to assess their potential for detecting freeze-onset (FO)/melt-onset (MO), ice-on/ice-off dates, and ice thickness on both lakes. The sensitivity of TB measurements at 6.9, 10.7, and 18.7 GHz to ice thickness is also examined using a previously validated thermodynamic lake ice model and the most recent version of the Helsinki University of Technology (HUT) model, which accounts for the presence of a lake-ice layer under snow. This study shows that 18.7 GHz H-pol is the most suitable AMSR-E channel for detecting ice phenology events, while 18.7 GHz V-pol is preferred for estimating lake ice thickness on the two large northern lakes. These two channels therefore form the basis of new ice cover retrieval algorithms. The algorithms were applied to map monthly ice thickness products and all ice phenology parameters on GBL and GSL over seven ice seasons (2002-2009). Through application of the algorithms much was learned about the spatio-temporal dynamics of ice formation, decay and growth rate/thickness on the two lakes. Key results reveal that: 1) both FO and ice-on dates occur on average 10 days earlier on GBL than on GSL; 2) the freeze-up process or freeze duration (FO to ice-on) takes a comparable amount of time on both lakes (two to three weeks); 3) MO and ice-off dates occur on average one week and approximately four weeks later, respectively, on GBL; 4) the break-up process or melt duration (MO to ice-off) lasts for an equivalent period of time on both lakes (six to eight weeks); 5) ice cover duration is about three to four weeks longer on GBL compared to its more southern counterpart (GSL); and 6) end-of-winter ice thickness (April) on GBL tends to be on average 5-15 cm thicker than on GSL, but with both spatial variations across lakes and differences between years