Evaluating RADARSAT-2 for the Monitoring of Lake Ice Phenology Events in Mid-Latitudes

Murfitt, Justin; Brown, Laura C.; Howell, Stephen E. L.

doi:10.3390/rs10101641

Cited by 19 publications

(30 citation statements)

References 43 publications

(73 reference statements)

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“…Ice cover typically forms between mid-December to early January and the process of melt begins between the middle of March and the beginning of April. Full ice cover typically lasts until the end of April but can persist until early May [22,27].…”

Section: Methodsmentioning

confidence: 99%

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Estimating lake ice thickness in Central Ontario

2018

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Lakes are a key geographical feature in Canada and have an impact on the regional climate. In the winter, they are important for recreational activities such as snowmobiling and ice fishing and act as part of an important supply route for northern communities. The ability to accurately report lake ice characteristics such as thickness is vital, however, it is underreported in Canada and there is a lack of lake ice thickness records for temperate latitude areas such as Central Ontario. Here, we evaluate the application of previously developed temperature models and RADARSAT-2 for estimating lake ice thickness in Central Ontario and provide insight into the regions long term ice thickness variability. The ALS Environmental Science Shallow Water Ice Profiler (SWIP) was used for validation of both temperature and radar-based models. Results indicate that the traditional approach that uses temperatures to predict ice thickness during ice growth has low RMSE values of 2.3 cm and correlations of greater than 0.9. For ice decay, similar low RMSE values of 2.1 cm and high correlations of 0.97 were found. Using RADARSAT-2 to estimate ice thickness results in R2 values of 0.6 (p < 0.01) but high RMSE values of 11.7 cm. Uncertainty in the RADARSAT-2 approach may be linked to unexplored questions about scattering mechanisms and the interaction of radar signal with mid-latitude lake ice. The application of optimized temperature models to a long-term temperature record revealed a thinning of ice cover by 0.81 cm per decade.

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Section: Methodsmentioning

confidence: 99%

“…Similar to ice estimates from temperature, remote sensing techniques to estimate freshwater lake ice thickness have seen limited application in mid-latitudes. Therefore, it is unclear whether or not these methods will see similar success for lake ice in these regions where ice conditions differ from the northern latitudes [22,23].…”

Section: Introductionmentioning

confidence: 99%

Estimating lake ice thickness in Central Ontario

2018

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“…They put forward a threshold-based classification methodology and observed that the HH and HV backscatter from the lake ice have significant temporal variability and interlake diversity. Murfitt et al (2018) used the RADARSAT-2 imagery to develop a threshold-based method to determine lake phenology events for the mid-latitude lakes in Central Ontario from 2008 to 2017. Wang et al (2018) also used RADARSAT-2 imagery (dual polarised) for developing a lake ice classification system acquired over lake Erie, with the IRGS method.…”

Section: Related Workmentioning

confidence: 99%

Lake Ice Detection From Sentinel-1 Sar With Deep Learning

Tom

Aguilar

Imhof

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. 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.

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“…Their analysis showed that the algorithm could provide reliable discrimination between ice and open water with high overall classification accuracy (90.4%) when compared to Great Lakes image analysis charts from the Canadian Ice Service. Murfitt et al [87] developed a threshold-based approach for estimating ice phenology events for mid-latitude lakes in Central Ontario by tracking the temporal evolution in backscatter from HH-polarization RADARSAT-2 imagery (2008-2017). The authors reported mean absolute errors of 2.5-10 days for freeze events and 1.5-7.1 days for water clear-of-ice when compared to MODIS imagery.…”

Section: Lake Ice Covermentioning

confidence: 99%

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

Watts

Jiang

et al. 2019

Remote Sensing

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Cold regions, including high-latitude and high-altitude landscapes, are experiencing profound environmental changes driven by global warming. With the advance of earth observation technology, remote sensing has become increasingly important for detecting, monitoring, and understanding environmental changes over vast and remote regions. This paper provides an overview of recent achievements, challenges, and opportunities for land remote sensing of cold regions by (a) summarizing the physical principles and methods in remote sensing of selected key variables related to ice, snow, permafrost, water bodies, and vegetation; (b) highlighting recent environmental nonstationarity occurring in the Arctic, Tibetan Plateau, and Antarctica as detected from satellite observations; (c) discussing the limits of available remote sensing data and approaches for regional monitoring; and (d) exploring new opportunities from next-generation satellite missions and emerging methods for accurate, timely, and multi-scale mapping of cold regions.

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Evaluating RADARSAT-2 for the Monitoring of Lake Ice Phenology Events in Mid-Latitudes

Cited by 19 publications

References 43 publications

Estimating lake ice thickness in Central Ontario

Estimating lake ice thickness in Central Ontario

Lake Ice Detection From Sentinel-1 Sar With Deep Learning

Remote Sensing of Environmental Changes in Cold Regions: Methods, Achievements and Challenges

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