ALBERT

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.

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.

Description: The lake ice backscatter time-series dataset was created for the purpose of developing an automated temporal deep learning method of lake ice regime classification and study of lake ice dynamics in the Old Crow Flats (OCF), Yukon, Canada. The dataset consists of approximately 129,000 labeled backscatter time-series collected using imagery from four C-band synthetic aperture radar (SAR) spaceborne platforms: Sentinel-1 A (VV polarization), ERS-1 and 2 (VV polarization), and RADARSAT-1 (HH polarization), which cover the time period between 1992 to 2021. Labeling was done in Sentinel Application Platform (SNAP) by manually placing pins at locations identified as either floating ice, bedfast ice, or land through visual assessment of the ice regime/land on the last day of the time-series for a given season. Due to variable temporal coverage, the dates of labeling ranged from March 4 to March 22. The labeling date was selected as close as possible to mid-March, and care was taken to ensure that the air temperature was below 0°C. Then, the backscatter values at the locations marked by each pin were extracted for each of the scenes in a SAR stack, creating time-series of labeled backscatter values for each year covering the October to mid-March period. Labels were assigned based on three factors: 1) backscatter values, 2) value of the projected incidence angle of the SAR pulse, and 3) location of the pixel within the scene. Resampling to a daily frequency and linear interpolation were applied to compensate for the temporal irregularity of the data gearing it for the deep learning classification. The final labeled time-series consist of 161 time steps (i.e., one time step per day) covering the time period between October 4 and March 13. In addition, lake ice maps (containing three classes: bedfast ice, floating ice, and land) created using the novel temporal deep learning approach developed based on the time-series dataset are provided in PNG and GeoTIFF formats.

Description: The data publication contains supplementary data to the article "Supplementary Dataset to: The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: Fast-forward into the future" This data publication includes four datasets: 1. Lake change datasets for 1999-2014 and 2017-2018 based on Landsat and Sentinel-1 data as Polygon Shapefiles 2. Lake change datasets for 2017 and 2018 based on high-temporal resolution PlanetScope imagery as Polygon Shapefiles and csv. 3. Lake ice simulations for the study area for 1980-2018. 4. Study sites in two versions: a) including seawater and b) clipped to land area. Files are Polygon Shapefiles. The datasets cover the land area of the Baldwin Peninsula and northern Seward Peninsula in north-western Alaska. The datasets are (#1&#2) remote sensing based observations and (#3) modelled data. Methods are described in detail in the original manuscript (open access). Dataset #4 is the extent of the study site in two versions, a) full extent including seawater and b) land only including lakes. The land boundary was clipped with the “Global Self-consistent, Hierarchical, High-resolution Geography Database” (GSHHG; Wessel and Smith, 1996) dataset in scale “h”. The datasets cover different temporal periods and have a different temporal resolution. Data were collected to measure the extent of a rapid and widespread thermokarst lake drainage event in northwestern Alaska in 2018 and to compare the affected number of lakes and area to previous periods. Lake-ice model data were calculated to simulate lake-ice conditions since 1980 and to put the lake-ice and weather conditions in 2017/2018 into context.