Multi-sensor imaging of winter buried lakes in the Greenland Ice Sheet

Li, Zheng; Li, L.; Chen, Zhuoqi; He, Yabai; Mo, Longyi; Chen, Dairong; Hu, Qin; Wang, L S; Liang, Qi; Cheng, Xiuzhen

doi:10.1016/j.rse.2023.113688

Cited by 3 publications

(2 citation statements)

References 52 publications

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“…The L band exhibits significant penetration capabilities in snowy environments, and its false-positive retrieval during non-melting seasons may be attributed to the presence of buried lakes and firn aquifers within the GrIS during winter [11]. Zheng et al discovered buried lakes between 2017 and 2022, which were primarily located in west-central, southwestern, and northeastern Greenland, at elevations ranging from 800 to 1700 m [40]. Shang et al investigated firn aquifers during the 2010-2020 period and discovered that they were primarily situated in southeastern and southern Greenland [41].…”

Section: Discussionmentioning

confidence: 99%

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Li,

Wang,

et al. 2024

Remote Sensing

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The occurrence of Supraglacial Lakes (SGLs) may influence the signals acquired with microwave radiometers, which may result in a degree of uncertainty when employing microwave radiometer data for the detection of surface melt. Accurate monitoring of surface melting requires a reasonable assessment of this uncertainty. However, there is a scarcity of research in this field. Therefore, in this study, we computed surface melt in the vicinity of Automatic Weather Stations (AWSs) by employing Defense Meteorological Satellite Program (DMSP) Ka-band data and Soil Moisture and Ocean Salinity (SMOS) satellite L-band data and extracted SGL pixels by utilizing Sentinel-2 data. A comparison between surface melt results derived from AWS air temperature estimates and those obtained with remote sensing inversion in the two different bands was conducted for sites below the mean snowline elevation during the summers of 2016 to 2020. Compared with sites with no SGLs, the commission error (CO) of DMSP morning and evening data at sites where these water bodies were present increased by 36% and 30%, respectively, and the number of days with CO increased by 12 and 3 days, respectively. The omission error (OM) of SMOS morning and evening data increased by 33% and 32%, respectively, and the number of days with OM increased by 17 and 21 days, respectively. Identifying the source of error is a prerequisite for the improvement of surface melt algorithms, for which this study provides a basis.

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Section: Discussionmentioning

confidence: 99%

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Li,

Wang,

et al. 2024

Remote Sensing

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“…C-band SAR images are generally not hindered by atmospheric effects and are capable of imaging through tropical clouds and rain showers. Its penetration capability with snow on the ground is limited, usually around 1 to 2 m [43], and the specific depth of snow penetration is related to the physical properties of the snow and the radar's incidence angle [44].…”

Section: Advantages Of Multisource Remote Sensingmentioning

confidence: 99%

Monitoring of Supraglacial Lake Distribution and Full-Year Changes Using Multisource Time-Series Satellite Imagery

Zhu,

Zhou,

Zhu

et al. 2023

Remote Sensing

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Change of supraglacial lakes (SGLs) is an important hydrological activity on the Greenland ice sheet (GrIS), and storage and drainage of SGLs occur throughout the year. However, current studies tend to split SGL changes into melt/non-melt seasons, ignoring the effect of buried lakes in the exploration of drainage, and the existing threshold-based approach to SGL extraction in a synthetic aperture radar (SAR) is influenced by the choice of the study area mask. In this study, a new method (Otsu–Canny–Otsu (OCO)), which accesses the features of SGLs on optical and SAR images objectively, is proposed for full-year SGL extraction with Google Earth Engine (GEE). The SGLs on the Petermann Glacier were monitored well by OCO throughout 2021, including buried lakes and more detailed rapid drainage events. Some SGLs’ extent varied minimally in a year (area varying by 10–25%) while some had very rapid drainage (a rapid drainage event from July 26 to 30). The SGL extraction results were influenced by factors such as the mode of polarization, the surface environment, and the depth of the lake. The OCO method can provide a more comprehensive analysis for SGL changes throughout the year.

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Most of Buried Meltwater in Greenland Ice Sheet Persist Throughout Winter Based on Weekly Sentinel-1 Observations

Li,

Zheng,

Liang

et al. 2024

Preprint

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Multi-sensor imaging of winter buried lakes in the Greenland Ice Sheet

Cited by 3 publications

References 52 publications

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Influence of Supraglacial Lakes on Accuracy of Inversion of Greenland Ice Sheet Surface Melt Data in Different Passive Microwave Bands

Monitoring of Supraglacial Lake Distribution and Full-Year Changes Using Multisource Time-Series Satellite Imagery

Most of Buried Meltwater in Greenland Ice Sheet Persist Throughout Winter Based on Weekly Sentinel-1 Observations

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