Lake ice phenology variations and influencing factors of Selin Co from 2000 to 2020

In the Tibetan Plateau (TP), the changes of lake ice phenology not only reflect regional climate change, but also impose substantial ecohydrological impacts on the local environment. Due to the limitation of ground observation, remote sensing has been used as an alternative tool to investigate recent changes of lake ice phenology. However, uncertainties exist in the remotely sensed lake ice phenology owing to both the data and methods used. In this paper, three different remotely sensed datasets are used to investigate the lake ice phenology variation in the past decade across the Tibetan Plateau, with the consideration of the underlying uncertainties. The remotely sensed data used include reflectance data, snow product, and land surface temperature (LST) data of moderate resolution imaging spectroradiometer (MODIS). The uncertainties of the three methods based on the corresponding data are assessed using the triple collocation approach. Comparatively, it is found that the method based on reflectance data outperforms the other two methods. The three methods are more consistent in determining the thawing dates rather than the freezing dates of lake ice. It is consistently shown by the three methods that the ice-covering duration in the northern part of the TP lasts longer than that in the south. Though there is no general trend of lake ice phenology across the TP for the period of 2000–2015, the warmer climate and stronger wind have led to the earlier break-up of lake ice.

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Lake ice phenology changes in the northern hemisphere

10.5194/egusphere-egu21-15155 ◽

2021 ◽

Author(s):

Yubao Qiu ◽

Xingxing Wang ◽

Matti Leppäranta ◽

Bin Cheng ◽

Yixiao Zhang

Keyword(s):

Remote Sensing ◽

North America ◽

Northern Hemisphere ◽

Ice Cover ◽

Passive Microwave ◽

Lake Area ◽

Lake Ice ◽

Northern Tibetan Plateau ◽

Break Up ◽

Ice Phenology

<p>Lake-ice phenology is an essential indicator of climate change impact for different regions (Livingstone, 1997; Duguay, 2010), which helps understand the regional characters of synchrony and asynchrony. The observation of lake ice phenology includes ground observation and remote sensing inversion. Although some lakes have been observed for hundreds of years, due to the limitations of the observation station and the experience of the observers, ground observations cannot obtain the lake ice phenology of the entire lake. Remote sensing has been used for the past 40 years, in particular, has provided data covering the high mountain and high latitude regions, where the environment is harsh and ground observations are lacking. Remote sensing also provides a unified data source and monitoring standard, and the possibility of monitoring changes in lake ice in different regions and making comparisons between them. The existing remote sensing retrieval products mainly cover North America and Europe, and data for Eurasia is lacking (Cr&#233;taux et al., 2020).</p><p>Based on the passive microwave, the lake ice phenology of 522 lakes in the northern hemisphere during 1978-2020 was obtained, including Freeze-Up Start (FUS), Freeze-Up End (FUE), Break-Up Start (BUS), Break-Up End (BUE), and Ice Cover Duration (ICD). The ICD is the duration from the FUS to the BUE, which can directly reflect the ice cover condition. At latitudes north of 60&#176;N, the average of ICD is approximately 8-9 months in North America and 5-6 months in Eurasia. Limited by the spatial resolution of the passive microwave, lake ice monitoring is mainly in Northern Europe. Therefore, the average of ICD over Eurasia is shorter, while the ICD is more than 6 months for most lakes in Russia. After 2000, the ICD has shown a shrinking trend, except northeastern North America (southeast of the Hudson Bay) and the northern Tibetan Plateau. The reasons for the extension of ice cover duration need to be analyzed with parameters, such as temperature, the lake area, and lake depth, in the two regions.</p>

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Approximating Lake Ice Phenology with Satellite Surface Temperature Data

10.1109/igarss47720.2021.9555006 ◽

2021 ◽

Author(s):

Sophia Karina Skoglund ◽

Abdou Rachid Bah ◽

Hamidreza Norouzi ◽

Kathleen C Weathers ◽

Holly A Ewing ◽

...

Keyword(s):

Surface Temperature ◽

Temperature Data ◽

Lake Ice ◽

Surface Temperature Data ◽

Ice Phenology

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Nam Co Lake Ice Period and Influencing Factors Dataset in Tibet (2000-2013)

GCdataPR ◽

10.3974/geodb.2016.01.05.v1 ◽

2020 ◽

Author(s):

Peng GOU ◽

Rui ZHAO ◽

Qinghua YE

Keyword(s):

Influencing Factors ◽

Nam Co ◽

Lake Ice ◽

Nam Co Lake ◽

Ice Period

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Assessing the Performance of Methods for Monitoring Ice Phenology of the World’s Largest High Arctic Lake Using High-Density Time Series Analysis of Sentinel-1 Data

Remote Sensing ◽

10.3390/rs12030382 ◽

2020 ◽

Vol 12 (3) ◽

pp. 382 ◽

Cited By ~ 2

Author(s):

Justin Murfitt ◽

Claude R. Duguay

Keyword(s):

Time Series ◽

Difference Method ◽

Time Of Day ◽

High Density ◽

Average Error ◽

Visual Interpretation ◽

Lake Ice ◽

Sar Images ◽

Otsu Method ◽

Ice Phenology

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.

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