We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
Global solar radiation and diffuse horizontal solar radiation at Dome C (Antarctica) are measured by radiation sensors (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground are obtained from the IPEV/PNRA Project “Routine Meteorological Observation at Station Concordia”, http://www.climantartide.it. This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Lanconelli, C.; Lupi, A.; Driemel, A.; Vitale, V.; Li, K.; Song, T. 2022. Long-Term Variations of Global Solar Radiation and Its Potential Effects at Dome C (Antarctica). Int. J. Environ. Res. Public Health, 19, 3084. https://doi.org/10.3390/ijerph19053084). The observed global solar radiation and meteorological parameters are available at https://doi.org/10.1594/PANGAEA.935421. The data set can be used to study solar radiation and its attenuation at Dome C, Antarctica.
BAI Jianhui
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
BAI Jianhui
Snow, ice, and glaciers have the highest albedo of any part of Earth's surface. The increase in melting of the polar ice sheet results in a rapid and sequential decrease in albedo and subsequently influences the global energy balance. The hydrological system derived from surface melt and basal meltwater will affect the dynamic stability of ice sheet and therefore mass balance. The dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Antarctic ice sheet surface melt daily product for 1985-1986, 2000-2001, 2015-2016 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. It better reflects the melting gradient of mountainous area, groundline area and ice shelf over time, DSSMIS has a higher accuracy. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Antarctic ice sheet, and the data set is stored as *.nc.
WEI Siyi,
Snow over sea ice controls the energy budgets, affects the sea ice growth/melting, and thus has essential climatic effects. Snow depth, one of the fundamental properties of snow cover, is essential for understanding of the rapid change in Antarctic climate and for sea ice thickness estimation. Passive microwave radiometer can be used for basin-scale snow depth estimation in daily scale, however, previous published methods applied for Antarctic snow depth shows clear underestimation, which limits their further application. Here, we construct a new and robust linear regression equation for snow depth retrieval using microwave radiometers by including lower frequencies, and we produce the snow depth product over Antarctic sea ice from 2002 to 2020 from AMSR-E, AMSR-2, SSMIS based on this method. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements shows that the gradient ratio (GR) calculated using brightness temperatures in vertical polarized 37 and 19 GHz, i.e., GR(37/7), is the optimal one for deriving Antarctic snow depth with an root mean square deviation (RMSD) of 8.92 cm and a correlation coefficient of -0.64, the related equation coefficients are then derived. GR(37/19) is used to retrieve snow depth from SSMIS data to fill the observation gaps between AMSR-E and AMSR-2, and the estimated snow depth is corrected for the consistence with these from AMSR-E/2. An averaged uncertainty of 3.81 cm is found based on a Gaussian error propagation, which accounts for 12% of the estimated mean snow depth. The evaluation of proposed method with in-situ measurements from Australian Antarctic Data Centre shows that the proposed method outperforms the previous available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, comparing to -14.47 cm and 19.49 cm. Comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicates that the proposed method shows slight better performance than previous method (RMSDs of 16.85 cm and 17.61 cm, respectively); and comparable performances in growth and melting seasons suggests that the proposed method can still be used in the melting season. We generate a complete snow depth product over Antarctic sea ice from 2002 to 2020 in daily scale, and negative trends can be found in all sea sectors and seasons. This dataset can be further used in the reanalysis data evaluation, sea ice thickness estimation, climate model and other aspects.
SHEN Xiaoyi, KE Changqing
This data set includes daily, annual and multi-year surface mass balance data from Antarctic ice cap poles, ice (snow) cores / snow pits, automatic weather station altimeters and ground penetrating radar observations. The data come from published literature, data reports and international data sharing platform. After quality control, the most perfect data set of daily, annual and multi-year resolution of surface mass balance of Antarctic ice sheet has been formed. Its middle-aged resolution data span the past 1000 years. The data set is mainly used in glaciology, climatology, hydrology and other disciplines, especially in the quantitative analysis of the temporal and spatial changes of Antarctic surface mass balance, climate model validation, driving ice sheet model and snow granulation model, etc.
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
The original data of the Arctic and Antarctic sea ice data set is generated by the National Snow and Ice Data Center (NSIDC) through remote sensing data. The data format is GeoTIFF format and image format. The spatial resolution of the data is 25km and the time resolution is day. The data content is the sea ice range and sea ice density of the north and south poles. In this study, NetCDF format products are generated by post-processing the extent and density of sea ice in the north and south poles. The product data includes the sea ice range and sea ice density data of the north and south poles from 1979 to 2019. The time resolution is day by day, the coverage range is the South Pole and the north pole, and the horizontal spatial resolution is 12.5km. The data value of 1 in the sea ice range matrix indicates that the grid is sea ice, and the sea ice density is expressed by 0-1000. The grid value divided by 10 is the sea ice density value of the grid.
YE Aizhong
The data of triode ice core mainly comes from NOAA (National Oceanic and Atmospheric Administration, https://www.ncdc.noaa.gov/data-access/paleoclimatology-data/datasets/ice-core). The original data is mainly in text format, which is provided by relevant units and researchers voluntarily. The data mainly includes the original observation data such as oxygen isotope, greenhouse gas concentration, ice core age, etc., as well as the historical temperature, carbon dioxide concentration and methane concentration produced by the researchers according to the observation data. The data are mainly divided into Antarctic, Arctic, Greenland and the third polar region. The database includes drilling address, time, derivative products, corresponding observation site data, references and other elements. Derivative products include product name, type, time and other elements. The space location is divided into the south pole, the north pole and the third pole, including Alaska, Canada, Russia, Greenland and other regions. After sorting and post-processing the collected data, the ice core database is established by using the access database management system of Microsoft office. According to the Antarctic, Arctic, Greenland and the third pole, it is divided into four sub databases. The first table in each database is readme, which contains information and references of each data table.
YE Aizhong
Glaciers are very sensitive to regional and global climate change, so they are often regarded as one of the indicators of climate change, and their relevant parameters are also the key indicators of climate change research. Especially in the comparative study of the three polar environmental changes on the earth, the time and space difference ratio of glacial speed is one of the focuses of climate change research. However, because glaciers are basically located in high altitude, high latitude and high cold areas, the natural environment is poor, and people are rarely seen, and it is difficult to carry out the conventional field measurement of large-scale glacial movement. In order to understand the glacial movement in the three polar areas in a timely, efficient, comprehensive and accurate manner, radar interferometry, radar and optical image pixel tracking are used to obtain the three polar areas. The distribution of surface movement of some typical glaciers in some years from 2000 to 2017 provides basic data for the comparative analysis of the movement of the three polar glaciers. The dataset contains 12 grid files named "glacier movement in a certain period of time in a certain region". Each grid map mainly contains the regional velocity distribution of a typical glacier.
Yan Shiyong
The three pole aerosol type data product is an aerosol type result obtained by integrating the data assimilation of Meera 2 and the active satellite CALIPSO product through a series of data preprocessing, quality control, statistical analysis and comparative analysis. The key of this algorithm is to judge the type of CALIPSO aerosol. In the process of aerosol type data fusion, according to the type and quality control of CALIPSO aerosol, and referring to the type of merra 2 aerosol, the final aerosol type data (12 kinds in total) and quality control results in the three pole area are obtained. The data product fully considers the vertical distribution and spatial distribution of aerosols, with high spatial resolution (0.625 ° × 0.5 °) and time resolution (month).
ZHAO Chuanfeng
The data set includes: population and GDP data of the arctic (1990-2015) and county-level population and GDP data of the third pole region (gansu, qinghai and Tibet) (1970-2016). Socio-economic statistical attributes include: population (ten thousand), GDP (ten thousand yuan), total industrial and agricultural output (ten thousand yuan), total agricultural output (ten thousand yuan), and total industrial output (ten thousand yuan). The arctic population data are mainly derived from the world populationProspects: 2017 revision by the Department of economic and social affairs, which divides the total population by region and country. The data of the third pole mainly refer to the statistical yearbook of gansu province, qinghai province and Tibet autonomous region.County records of gansu, qinghai and Tibet autonomous regions.
Department of Economic and Social Affairs, National Bureau of Statistics, Qinghai Provincial Bureau of Statistics
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
National Aeronautics and Space Administration
The dataset of Antarctic mountains(1:1000000) includes vector spatial distribution data and some related attribute data: Name, Country name of the mountains , Country abbreviation, Latitude、 Longitude The data comes from the ADC World Map（1:1000000） global dataset, the data is topological, and it is the comprehensive, latest and seamless geographic digital data. The world map coordinate system is the latitude and longitude, WGS84 datum, and the Antarctic data set is the South Pole Stereographic.
ADC WorldMap
Antarctic 1:100,000 airport distribution data set includes vector space data and related attribute data of airports (Antarctic_Airport) and airport runways (Antarctic_Airport_runways)：Airport Name(Name), airport country Name（CNTRY_NAME）, airport country abbreviation（CNTRY_CODE）, LATITUDE, LONGITUDE. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
Antarctic administrative boundary datasets consist of the properties of the state boundaries of the Antarctic states (properties properties), and the corresponding names and types of those properties :(CITY_POP), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (YEAR). The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
The 1:1,000,000 Antarctic settlements data set includes vector spatial data of Antarctic settlements and its related attributes：City name (ENG_NAME), city population (CNTEY_NAME), (CNTRY_CODE), etc. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
The data sets include four sets of data obtained from the Scanning Multi-channel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I) and the Special Sensor Microwave Imager Sounder (SSMIS) sensors using passive microwave remote sensing inversion. SMMR was aboard the Nimbus-7 satellite, and its working period was from October 26, 1978 to July 8, 1987. Since July 1987, the data provided by the SSM/I and the SSMIS aboard the US Defense Meteorological Satellite Program (DMSP) satellite group have been used. The first three data sets contain sea ice concentration data, covering the Antarctic region with a spatial resolution of 25 km: (1) The data were obtained from Nimbus-7 SMMR and DMSP SSM/I-SSMIS Version 1 by applying the NASA Team algorithm inversion. The temporal coverage is from November 1978 to February 2017, with a temporal resolution of one month. A bin file is stored every month. (2) The data source is the same as the first set. The temporal coverage is from 1978-10-26 to 2017-2-28. The temporal resolution is two days, and the spatial resolution is 25 km. A folder was stored every year, and a bin file was stored every other day. (3) The data were obtained from near-real-time DMSP SSMIS by applying the NASA Team algorithm inversion. The temporal coverage is from 2015-1-1 to 2018-2-3, and the temporal resolution is one day. A bin file is stored every day. Each file consists of a 300-byte file title (data time information, projection pattern, file name) and a 316*332 matrix. The fourth set of data is the sea ice coverage and sea ice area time series. The temporal coverage is from November 1978 to December 2017. This data set is a time series sequence of sea ice coverage and sea ice area in the Antarctic. The temporal resolution is one month, and an ASCII file is stored every month. Each file consists of a file title (time, data type), a 39*1 sea ice cover matrix and a 39*1 sea ice area matrix. For further details on the data, please visit the US Ice and Snow Data Center NSIDC website - Data Description http://nsidc.org/data/NSIDC-0051; http://nsidc.org/data/NSIDC-0081; http://nsidc.org/data/G02135
LI Shuanglin, LIU Na
The Sentinel-1A/B satellite uses a near-polar sun-synchronous orbit with an orbital altitude of 693 km, an orbital inclination of 98.18°, and an orbital period of 99 minutes. It is equipped with a C-band Synthetic Aperture Radar (SAR) with a designed service life of 7 years (12 years expected). Sentinel-l has a variety of imaging methods that enable different polarization modes such as single-polarization and dual-polarization. Sentinel-1A SAR has four working modes: Strip Map Mode (SM), Extra Wide Swath (EW), Interferometric Wide Swath (IW) and Wave Mode (WV). Satellite A was successfully launched in April 2014. The revisit period of the same region was 12 days. Satellite B successfully operated on orbit in April 2016. The current revisiting period reached 3 to 6 days. After the operation of two satellites, the S1 data acquisition frequency in the Antarctic region increased greatly. This data set comprises the Sentinel-1 SAR data for the Antarctic ice sheet and the Greenland Ice Sheet area. The data band comprises C-band extra wide multiview data with a resolution of 20 m*40 m. The temporal resolution is 12 days and is related to the round-trip period, the width is 400 km, the noise level is -25 dB, and the radiation measurement accuracy is 1.0 dB. The annual temporal coverage of these data is October to the next March in the Antarctic and April to September in Greenland, and the spatial coverage comprises the Antarctic ice sheet ice shelf area and Greenland ice sheet.
Lu Zhang
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