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  • 1
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    Monitoring bedfast ice and ice phenology in lakes of the Lena River Delta using TerraSAR-X backscatter and coherence time series
    PANGAEA
    In:  Supplement to: Antonova, Sofia; Duguay, Claude R; Kääb, Andreas; Heim, Birgit; Langer, Moritz; Westermann, Sebastian; Boike, Julia (2016): Monitoring bedfast ice and ice phenology in lakes of the Lena River Delta using TerraSAR-X backscatter and coherence time series. Remote Sensing, 8(11), 903, https://doi.org/10.3390/rs8110903
    Details
    Publication Date: 2023-05-12
    Description: Thermokarst lakes and ponds are major elements of permafrost landscapes, occupying up to 40% of the land area in some Arctic regions. Shallow lakes freeze to the bed, thus preventing permafrost thaw underneath them and limiting the length of the period with greenhouse gas production in the unfrozen lake sediments. Radar remote sensing permits to distinguish lakes with bedfast ice due to the difference in backscatter intensities from bedfast and floating ice. This study investigates the potential of a unique time series of three-year repeat-pass TerraSAR-X (TSX) imagery with high temporal (11 days) and spatial (10 m) resolution for monitoring bedfast ice as well as ice phenology of lakes in the zone of continuous permafrost in the Lena River Delta, Siberia. TSX backscatter intensity is shown to be an excellent tool for monitoring floating versus bedfast lake ice as well as ice phenology. TSX-derived timing of ice grounding and the ice growth model CLIMo are used to retrieve the ice thicknesses of the bedfast ice at points where in situ ice thickness measurements were available. Comparison shows good agreement in the year of field measurements. Additionally, for the first time, an 11-day sequential interferometric coherence time series is analyzed as a supplementary approach for the bedfast ice monitoring. The coherence time series detects most of the ice grounding as well as spring snow/ice melt onset. Overall, the results show the great value of TSX time series for monitoring Arctic lake ice and provide a basis for various applications: for instance, derivation of shallow lakes bathymetry, evaluation of winter water resources and locating fish winter habitat as well as estimation of taliks extent in permafrost.
    Type: Dataset
    Format: application/zip, 2 datasets
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 2
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    Circumpolar and regional Land Surface Temperature (Version 2) with links to geotiff images (2007-01 to 2013-12) (2014)
    PANGAEA
    In:  University of Waterloo, Canada
    Details
    Publication Date: 2023-04-15
    Keywords: Arctic; Changing Permafrost in the Arctic and its Global Effects in the 21st Century; Date/time end; Date/time start; DUEPermafrost_Alaska; DUEPermafrost_Central_Yakutia; DUEPermafrost_Laptev_Sea; DUEPermafrost_Lena_Delta; DUEPermafrost_Mackenzie; DUEPermafrost_Ob_Estuary; DUEPermafrost_panarctic; ESA Data User Element - Permafrost; ESA-DUE-Permafrost; Event label; File content; File size; PAGE21; SAT; Satellite remote sensing; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 450 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 3
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    Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model, link to supplementary data (2017)
    PANGAEA
    In:  Supplement to: Zolfaghari, Kiana; Duguay, Claude R; Kheyrollah Pour, Homa (2017): Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model. Hydrology and Earth System Sciences, 21(1), 377-391, https://doi.org/10.5194/hess-21-377-2017
    Details
    Publication Date: 2023-04-15
    Description: A global constant value of the extinction coefficient (Kd) is usually specified in lake models to parameterize water clarity. This study aimed to improve the performance of the 1-D freshwater lake (FLake) model using satellite-derived Kd for Lake Erie. The CoastColour algorithm was applied to MERIS satellite imagery to estimate Kd. The constant (0.2/m) and satellite-derived Kd values as well as radiation fluxes and meteorological station observations were then used to run FLake for a meteorological station on Lake Erie. Results improved compared to using the constant Kd value (0.2/m). No significant improvement was found in FLake-simulated lake surface water temperature (LSWT) when Kd variations in time were considered using a monthly average. Therefore, results suggest that a time independent, lake-specific, and constant satellite-derived Kd value can reproduce LSWT with sufficient accuracy for the Lake Erie station. A sensitivity analysis was also performed to assess the impact of various Kd values on the simulation outputs. Results show that FLake is sensitive to variations in Kd to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compared to clear waters. Dark waters always produce colder mean water column temperature (MWCT) and lake bottom water temperature (LBWT), shallower mixed layer depth (MLD), longer ice cover duration, and thicker ice. The sensitivity of FLake to Kd variations was more pronounced in the simulation of MWCT, LBWT, and MLD. The model was particularly sensitive to Kd values below 0.5/m. This is the first study to assess the value of integrating Kd from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived Kd is found to be a useful input parameter for simulations with FLake and possibly other lake models, and it has potential for applicability to other lakes where Kd is not commonly measured.
    Keywords: Lake_Erie
    Type: Dataset
    Format: application/zip, 126.2 kBytes
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 4
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    Lake ice phenology in the Northern Hemisphere extracted from SMMR, SSM/I and SSMIS data from 1979 to 2020 (2021)
    PANGAEA
    Details
    Publication Date: 2023-04-15
    Description: This dataset contains ice phenology for 56 lakes in the Northern Hemisphere from 1979 to 2019. The ice phenology was extracted from 3.125 km 37 GHz H-polarized evening brightness temperature data from Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Image (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data in the Calibrated Enhanced Resolution Brightness Temperature (CETB) dataset. According to the differences in the brightness temperature between lake ice and open water, a threshold algorithm based on Moving t test method was applied to determine the lake ice status for the pixels 6.25 km away from the lake shore, and the ice phenology dates for each lake were then extracted. For the overlapping lake ice phenology results extracted from multiple satellite, the results from the satellite with the highest utilization were prioritized. The lake ice phenology dataset provides valuable information about the changes in the lakes with seasonal ice cover in the past four decades.
    Keywords: Alakol; Amadjuak; Athabasca; Ayakkum; Baikal; Baker; Balkhash; Bosten; Bratsk Reservoir; Calculated; Caspian Sea; Date; Dubawnt; Duration, number of days; Ebi; Erie; Event label; Great Bear; Great Slave; Hulun; Huron; Ice coverage, maximum; Ilmen; Kasba; Khanka; Khar Us; Khovsgol; Khyargas; Kremenchuk Reservoir; Kuybyshev Reservoir; Ladoga; La Grande 3 Reservoir; Lake_01_Great_Bear; Lake_02_Great_Slave; Lake_03_Dubawnt; Lake_04_Baker; Lake_05_Kasba; Lake_06_Athabasca; Lake_07_Netilling; Lake_08_Amadjuak; Lake_09_Winnipegosis; Lake_10_Manitoba; Lake_11_Winnipeg; Lake_12_Woods; Lake_13_La_Grande_3_Reservoir; Lake_14_Saint_Jean; Lake_15_Superior; Lake_16_Michigan; Lake_17_Huron; Lake_18_Erie; Lake_19_Ontario; Lake_20_Vanern; Lake_21_Kremenchuk_Reservoir; Lake_22_Onega; Lake_23_Ladoga; Lake_24_Peipsi; Lake_25_Ilmen; Lake_26_Rybinsk_Reservoir; Lake_27_Kuybyshev_Reservoir; Lake_28_Tsimlyanskoye_Reservoir; Lake_29_Caspian_Sea; Lake_30_Zeyskoye_Reservoir; Lake_31_Khanka; Lake_32_Bratsk_Reservoir; Lake_33_Baikal; Lake_34_Khovsgol; Lake_35_Uvs; Lake_36_Khyargas; Lake_37_Khar_Us; Lake_38_Zaysan; Lake_39_Sasykkol; Lake_40_Alakol; Lake_41_Balkhash; Lake_42_Qapshaghay_Bogeni_Reservoir; Lake_43_Ulungur; Lake_44_Ebi; Lake_45_Bosten; Lake_46_Ayakkum; Lake_47_Qinghai; Lake_48_Ngoring; Lake_49_Ma_pang_yung_tso; Lake_50_Zhari_Namco; Lake_51_Tangra; Lake_52_Siling; Lake_53_Nam; Lake_54_Hulun; Lake_55_Large_Aral_Sea; Lake_56_Sarygamysh; lake ice phenology; Large Aral Sea; Latitude of event; Longitude of event; Manitoba; Ma-p'ang yung-ts'o; Michigan; MULT; Multiple investigations; Nam; Netilling; Ngoring; Onega; Ontario; Optional event label; Passive microwave radiometer; Peipsi; Qapshaghay Bogeni Reservoir; Qinghai; remote sensing; Rybinsk Reservoir; Saint-Jean; Sarygamysh; Sasykkol; Siling; SMMR; SSM/I and SSMIS; Superior; Tangra; Tsimlyanskoye Reservoir; Ulungur; Uvs; Vanern; Winnipeg; Winnipegosis; Woods; Years; Zaysan; Zeyskoye Reservoir; Zhari Namco
    Type: Dataset
    Format: text/tab-separated-values, 14019 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 5
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    Lake ice phenology in the Northern Hemisphere extracted from SMMR, SSM/I and SSMIS data from 1979 to 2021 (2022)
    PANGAEA
    Details
    Publication Date: 2023-04-15
    Description: This dataset contains ice phenology for 56 lakes in the Northern Hemisphere from 1979 to 2021. The ice phenology was extracted from 3.125 km 37 GHz H-polarized evening brightness temperature data from Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Image (SSM/I) and Special Sensor Microwave Imager/Sounder (SSMIS) data in the Calibrated Enhanced Resolution Brightness Temperature (CETB) dataset.
    Keywords: Alakol; Amadjuak; Athabasca; Ayakkum; Baikal; Baker; Balkhash; Bosten; Bratsk Reservoir; Calculated; Caspian Sea; Date; Dubawnt; Duration, number of days; Ebi; Erie; Event label; Great Bear; Great Slave; Hulun; Huron; Ice coverage, maximum; Ilmen; Kasba; Khanka; Khar Us; Khovsgol; Khyargas; Kremenchuk Reservoir; Kuybyshev Reservoir; Ladoga; La Grande 3 Reservoir; Lake_01_Great_Bear; Lake_02_Great_Slave; Lake_03_Dubawnt; Lake_04_Baker; Lake_05_Kasba; Lake_06_Athabasca; Lake_07_Netilling; Lake_08_Amadjuak; Lake_09_Winnipegosis; Lake_10_Manitoba; Lake_11_Winnipeg; Lake_12_Woods; Lake_13_La_Grande_3_Reservoir; Lake_14_Saint_Jean; Lake_15_Superior; Lake_16_Michigan; Lake_17_Huron; Lake_18_Erie; Lake_19_Ontario; Lake_20_Vanern; Lake_21_Kremenchuk_Reservoir; Lake_22_Onega; Lake_23_Ladoga; Lake_24_Peipsi; Lake_25_Ilmen; Lake_26_Rybinsk_Reservoir; Lake_27_Kuybyshev_Reservoir; Lake_28_Tsimlyanskoye_Reservoir; Lake_29_Caspian_Sea; Lake_30_Zeyskoye_Reservoir; Lake_31_Khanka; Lake_32_Bratsk_Reservoir; Lake_33_Baikal; Lake_34_Khovsgol; Lake_35_Uvs; Lake_36_Khyargas; Lake_37_Khar_Us; Lake_38_Zaysan; Lake_39_Sasykkol; Lake_40_Alakol; Lake_41_Balkhash; Lake_42_Qapshaghay_Bogeni_Reservoir; Lake_43_Ulungur; Lake_44_Ebi; Lake_45_Bosten; Lake_46_Ayakkum; Lake_47_Qinghai; Lake_48_Ngoring; Lake_49_Ma_pang_yung_tso; Lake_50_Zhari_Namco; Lake_51_Tangra; Lake_52_Siling; Lake_53_Nam; Lake_54_Hulun; Lake_55_Large_Aral_Sea; Lake_56_Sarygamysh; lake ice phenology; Large Aral Sea; Latitude of event; Longitude of event; Manitoba; Ma-p'ang yung-ts'o; Michigan; MULT; Multiple investigations; Nam; Netilling; Ngoring; Onega; Ontario; Optional event label; Passive microwave radiometer; Peipsi; Qapshaghay Bogeni Reservoir; Qinghai; remote sensing; Rybinsk Reservoir; Saint-Jean; Sarygamysh; Sasykkol; Siling; SMMR; SSM/I and SSMIS; Superior; Tangra; Tsimlyanskoye Reservoir; Ulungur; Uncertainty; Uvs; Vanern; Winnipeg; Winnipegosis; Woods; Years; Zaysan; Zeyskoye Reservoir; Zhari Namco
    Type: Dataset
    Format: text/tab-separated-values, 22450 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 6
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    (Table 4) Ice phenology characteristics during the break-up period at Great Bear Lake and Great Slave Lake, Canada
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E); DATE/TIME; Date/time end; Day of the year; Deline_MS; Duration, number of days; Event label; Gr_Slave_lake; Northwest Territories, Canada; Standard deviation; Weather station/meteorological observation; WST
    Type: Dataset
    Format: text/tab-separated-values, 182 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 7
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    (Table 3) Ice phenology characteristics during the freeze-up period at Great Bear Lake and Great Slave Lake, Canada
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E); DATE/TIME; Date/time end; Day of the year; Deline_MS; Duration, number of days; Event label; Gr_Slave_lake; Northwest Territories, Canada; Standard deviation; Weather station/meteorological observation; WST
    Type: Dataset
    Format: text/tab-separated-values, 182 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 8
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    (Table 5) Duration of ice cover and open water season at Great Bear Lake and Great Slave Lake, Canada
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E); DATE/TIME; Date/time end; Deline_MS; Duration, number of days; Event label; Gr_Slave_lake; Northwest Territories, Canada; Standard deviation; Weather station/meteorological observation; WST
    Type: Dataset
    Format: text/tab-separated-values, 118 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 9
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    (Table 2) Seasonal mean air temperatures at Great Bear Lake and Great Slave Lake, Canada
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Keywords: DATE/TIME; Deline_MS; Event label; Gr_Slave_lake; Northwest Territories, Canada; Temperature, annual mean; Temperature, autumn; Temperature, spring; Temperature, summer; Temperature, winter; Weather station/meteorological observation; WST
    Type: Dataset
    Format: text/tab-separated-values, 80 data points
    Location Call Number Limitation Availability
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    DATA PANGAEA
  • 10
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    Three-year time series of TerraSAR-X backscatter intensity and interferometric coherence for lakes in the Lena River Delta, Siberia, links to GeoTIFF images
    PANGAEA
    Details
    Publication Date: 2023-05-12
    Description: The goal of this study was to examine the potential of a unique X-band SAR dataset to monitor ice phenology and bedfast ice on a number of thermokarst lakes in the Siberian Arctic. Three-year repeat-pass TSX time series with high temporal (11 days) and spatial (10 m) resolution were used. Two different parameters derived from SAR imagery were employed in the analysis: backscatter intensity and 11-day interferometric coherence. In situ ice thickness measurements were collected at 14 locations from a sample of 10 lakes in April 2015. A region of interest (ROI) was created around each in situ ice thickness measurement location as a circle with a diameter of approximately 10 pixels. We provide extracted backscatter and coherence for all the ROIs as georeferenced .tif files for the entire time series.
    Keywords: AWI Arctic Land Expedition; File format; File name; File size; Kurungnakh_Island; Lena2013; RU-Land_2013_Lena; SAT; Satellite remote sensing; Uniform resource locator/link to file
    Type: Dataset
    Format: text/tab-separated-values, 12 data points
    Location Call Number Limitation Availability
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