The data set of ice core-snow black carbon content on the Tibetan plateau (1950-2006) contains five (5) tables: 1 Xu et al. 2006 AG, 2 Xu et al. 2009 PNAS_Conc., 3 Xu et al. 2009 PNAS_flux, 4 Xu et al. 2012 ERL, 5 Wang et al. 2015 ACP. The data collection sites include the Meikuang glacier, Dongkemadi, Qiangyong, Kangwure, Naimona’nyi, Muztagata, Rongbuk, Tanggula Mountain, Ningjin Gangsang, Zuoqipu, and Glacier No. 1 at the headwaters of the Ürüqi River. The latitudes and longitudes of the collection locations, elevations and other information are marked in the data. The main indicators of the data are location, time, organic carbon (OC), elemental carbon (EC), black carbon (BC) content and flux. Location: latitude and longitude Time: year or date OC: organic carbon EC: elemental carbon BC: Black carbon Conc.: content, unit: ng g-1 Flux: flux, unit: mg m-2a-1 The data come from the following subjects. 1. National Program on Key Basic Research Project (973 Program)：Temporal and Spatial Characteristics and Remote Sensing Modeling of Global Change Sensitive Factors; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 2. National Key Basic Research Program: The Response of Formation and Evolution on the Tibetan Plateau to Global Changes and Adaptation Strategy; Person in charge: Tandong Yao; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the Ministry of Science and Technology. 3. The General Program of National Natural Science Foundation of China: High-resolution Carbon Black Recording in Snow Ice of the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 4. The General Program of the National Natural Science Foundation of China: Extraction of Climate and Environment Information from Ice Core Encapsulated Gas on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 5. National Natural Science Foundation of China for Distinguished Young Scholars: Snow and Ice-Atmospheric Chemistry and Environmental Changes on the Tibetan Plateau; Person in charge: Baiqing Xu; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). 6. National Natural Science Foundation of China for Distinguished Young Scholars: Study on the Changes of Aerosol Emissions and Combustion in Human Activities in South Asia in the Past 100 Years; Person in charge: Mo Wang; Unit: Institute of Tibetan Plateau Research, Chinese Academy of Sciences; Supported by the National Natural Science Foundation of China (NSFC). Observation methods: two-step heating method, thermal/optical carbon analysis method, and single-particle black carbon aerosol photometer.
XU Baiqing
There are three types of glacial lakes: supraglacial lakes, lakes attached to the end of the glacier and lakes not attached to the end of the glacier. Based on this classification, the following properties are studied: the variation in the number and area of glacial lakes in different basins in the Third Pole region, the changes in extent in terms of size and area, distance from glaciers, the differences in area changes between lakes with and without the supply of glacial melt water runoff, the characteristics of changes in the glacial lake area with respect to elevation, etc. Data source: Landsat TM/ETM+ 1990, 2000, 2010. The data were visually interpreted, which included checking and editing by comparing the original image with Google Earth images when the area was greater than 0.003 square kilometres. The data were applied to glacial lake changes and glacial lake outburst flood assessments in the Third Pole region. Data type: Vector data. Projected Coordinate System: Albers Conical Equal Area.
ZHANG Guoqing
The Randolph Glacier Inventory (RGI) is a complete inventory of global glacier outlines published by GLIMS (Global Land Ice Measurements from Space). It is currently available in six versions: Version 1.0 was published in February 2012, version 2.0 was published in June 2012, version 3.0 was published in April 2013, version 4.0 was published in December 2014, version 5.0 was published in July 2015, and version 6.0 was published in July 2017. The data sets include four versions, which are 6.0, 5.0, 4.0 and 3.2 (revision, August 2013). The data are organized according to different regions. In each region, each glacier record includes a shape file (.shp file and its corresponding .dbf, .prj, and .shx files) and a .csv file of height measurement data. The data are from GLIMS: Global Land Ice Measurements from Space (http://www.glims.org/RGI/) Data quality checks include geometry, topology, and certain attributes, and the following checks were performed: 1) All polygons were checked by the ArcGIS Repair Geometry tool. 2) Glaciers with areas less than 0.01 square kilometres were removed. 3) The topology was checked with the Does Not Overlap rule. 4) The attribute sheet was checked by Fortran subroutines and Python scripts for data quality.
Global Land Ice Measurements from Space
This is the 1976, 1991, 2000, and 2010 vector data set of glaciers and glacial lakes in the Boqu Basin in Central Himalaya based on Landsat satellite images. The data source is from Landsat remote images. 1976: LM21510411975306AAA05, LM21510401976355AAA04 1991: LT41410401991334XXX02, LT41410411991334XXX02 2000: LE71410402000279SGS00, LE71400412000304SGS00, LE71410402000327EDC00, LE71410412000327EDC00 2010: LT51400412009288KHC00, LT51410402009295KHC00, LT51410412009311KHC00, LT51410402011237KHC00. The boundaries of glaciers and glacial lakes are extracted manually from the various remote sensing images. The extraction error of the boundaries of glaciers and glacial lakes is estimated to be 0.5 pixels. Data file: Glacial_1976: Glacier vector data in 1976 Glacial_1991: Glacier vector data in 1991 Glacial_2000: Glacier vector data in 2000 Glacial_2010: Glacier vector data in 2010 Glacial_Lake_1976: Glacial lake vector data in 1976年 Glacial_Lake_1991: Glacial lake vector data in 1991 Glacial_Lake_2000: Glacial lake vector data in 2000 Glacial_Lake_2010: Glacial lake vector data in 2010 The glacial lake vector data fields include Number, name, latitude and longitude, altitude, area, orientation, type of glacial lake, length, width, and distance from the glacier.
WANG Weicai
The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
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
The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes（92% coverage on TP）in 1972-79，including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s（1981,1986-89,which covers 6.5% of TP） and 2 scenes in 1994（by 1.5% coverage on TP）.Among all satellite data，77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.
YE Qinghua, WU Yuwei
The continuous advancement of SAR interferometry technology makes it possible to obtain multitemporal DEMs with high precision in the glacial area. In particular, in 2000, the Shuttle Radar Topography Mission (SRTM) led by NASA provided DEM data covering the area from 56ºS to 60ºN; the TanDEM-X bistatic SAR interferometry system of DLR could provide the global DEM data with high resolution and precision. These high-quality, large-coverage SAR interferometry data, as well as published DEM data products, provided valuable information for using the multitemporal DEMs to detect changes in ice thickness. The temporal coverage of the ice thickness variation data of typical glaciers on the Tibetan Plateau was from 2000 to 2013, covering Puruogangri and the west Qilian Mountains with a spatial resolution of 30 meters. Using TanDEM-X bistatic InSAR data and a C-band SRTM DEM, the differential radar interferometry method was first used to generate a TanDEM-X DEM with high precision. Then, based on the precise registration of DEM, the DEM data obtained in different periods were compared. Lastly, the ice thickness changes were estimated. The format of the data set was GeoTIFF, and each typical glacier ice thickness change was stored in a folder. For details of the data, please refer to the Ice elevation changes for typical glaciers on the Tibetan Plateau - Data Description.
JIANG Liming
The Tibetan Plateau Glacial Data –TPG2001 is a glacial coverage data on the Tibetan Plateau in around 2000 from 150 scenes of Landsat7 TM/ETM+ images by 30 m spatial resolution. The selected Landsat7 TM/ETM+ images were within the period between 1999 and 2002, including 61 scenes (41%) in 2001 and 47 scenes (31%) in 2000. Among all the images, 71% was taken in winter. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2001. Glacier outlines were digitized on-screen manually from the 2001 image mosaic, relying on false-colour image composites (RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2001. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2001 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.8%.
YE Qinghua, WU Yuwei
The glacial bacterial resource database of the Tibetan Plateau provides the bacterial 16S ribosomal RNA gene sequences of several glaciers, which are seven glaciers of the Tibetan Plateau separated by an experimental group led by Yongqin Liu during 2010 to 2018 (East Rongbuk Glacier of Mt. Qomolangma, Tianshan Glacier No.1, Guliya Glacier, Laohugou Glacier, Muztagh Ata Glacier, Qiyi Glacier and Yuzhufeng Glacier), the Malan Glacier separated by Shurong Xiang and the Puruogangri Glacier separated by Xinfang Zhang. After the glacier samples were collected, they were taken to the Ecological Laboratory of the Institute of Tibetan Plateau Research of the Chinese Academy of Sciences in Beijing and the National Cryosphere Laboratory in Lanzhou. After applying the spread plate method, the samples were cultured at different temperatures (4-25 °C) for 20 days to 90 days, and single colonies were picked out for purification. After the DNA was extracted from the isolated bacteria, the 16S ribosomal RNA gene fragment was amplified with 27F/1492R primer and sequenced using the Sanger method. The 16S ribosomal RNA gene sequence was compared with the RDP database using the "Classifier" software and identified as level one when the reliability exceeded 80%. These data contain the 16S ribosomal RNA gene fragment sequence and glacier sources of each sequence. Compared with sequences based on high-throughput sequencing, these data have a longer sequence and more accurate classification and can better serve in glacier microbiology research.
JI Mukan
Glacier monitoring mass balance data are the most direct and reliable data for glaciers responding to climate change. The data set of global glacier monitoring mass balance collects information on 76 glaciers and their glacier mass balance data, both with continuous (uninterrupted) observation time series and by collecting and arranging globally accessible mass balance data with a time resolution of one year from 1950 to 2016.
XIAO Yao, SHANGGUAN Donghui
The data set of prokaryotic microorganism distribution in the snow and ice of the Arctic Antarctic and the Tibetan Plateau provides the bacterial 16S ribosomal RNA gene sequence collected by the experimental group led by Yongqin Liu from the NCBI database during 2010 to 2018. The keywords for NCBI database search are Antarctic, Arctic Tibetan, and Glacier. The collected sequences were calculated using the DOTOUR software to obtain the similarities between sequences, the sequences with similarities above 97% were clustered into one OTU, and the OTU representative sequence was defined. The OTU representative sequence was compared with the RDP database by the "Classifier" software and was identified as level one when the reliability exceeded 80%. After acquiring the sequence, the GPS coordinates of the sample were obtained by reading the sample information in the sequence file. These data contain the sequence of 16S ribosomal RNA gene fragments for each sequence, evolutionary classification, and sample GPS coordinates. Compared with sequences based on high-throughput sequencing, these data have a longer sequence and more accurate classification. It is significant for comparing the evolutionary information of three-pole microorganisms and understanding the evolution of psychrophilic microorganisms.
JI Mukan
The DEMs of the typical glaciers on the Tibetan Plateau were provided by the bistatic InSAR method. The data were collected on November 21, 2013. It covered Puruogangri and west Qilian Mountains with a spatial resolution of 10 meters, and an elevation accuracy of 0.8 m which met the requirements of national 1:10 000 topographic mapping. Considering the characteristics of the bistatic InSAR in terms of imaging geometry and phase unwrapping, based on the TanDEM-X bistatic InSAR data, and adopting the improved SAR interference processing method, the surface DEMs of the two typical glaciers above were generated with high resolution and precision. The data set was in GeoTIFF format, and each typical glacial DEM was stored in a folder. For details of the data, please refer to the Surface DEMs for typical glaciers on the Tibetan Plateau - Data Description.
JIANG Liming
Climate records obtained by most instruments are relatively short in time, which limits the study of climate change, necessitating the use of proxy data to extend records to the past. It was not until the late 1940s that atmospheric data of sufficient quality and spatial resolution were available to determine the main patterns of climate change such as the North American Pacific model and the Pacific Decadal Oscillation. The global ice cores are from the north and south poles and the third pole, and there are also mountain glaciers in Alaska. The ice core data obtained in that area are of great significance for revealing the climate in North America and climate change in the Arctic regions at both low and high latitudes. The physical meaning of each variable: First column: time; second column: accumulation rate data; third column: oxygen isotope data value
Du Zhiheng
The Greenland Ice Sheet Project Two (GISP2), initiated by the United States, has provided detailed oxygen isotope data for a time span of more than 100,000 years, covering almost the entire glacial-interglacial cycle. These data include the oxygen isotope changes from 818 to 1987, with a clear record showing that the Little Ice Age was the coldest period of the past 1000 years. Fluctuating warming occurred from 1850 to 1987, and the changes were consistent with those of GRIP, NGRIP and the latest NEEM ice core obtained in Greenland. This finding indicated that the snow and ice records from the Greenland ice sheet were highly consistent. The physical meaning of each variable is as follows: First column: ice core depth; second column: oxygen isotope value; third column: time
Du Zhiheng
From 1000 AD to the present, the concentration of methane in the atmosphere has increased significantly in the ice cores of the Antarctic and Arctic. These data came from the Tasmanian laboratory of Australia, where the high resolution data were obtained by using wet extraction of ice core samples, and the same measurement and calibration procedures were applied to all samples. The results are consistent with the results of internationally renowned ice core greenhouse gas laboratories such as the University of Bern, the University of Copenhagen and the University of Ohio. The physical meaning of each variable: First column: time; second column: methane concentration value
Du Zhiheng
The microwave radiometer data set comprises brightness temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015), with temporal coverage from 1978 to 2015 and a spatial resolution of 25 km. Each Antarctic data file consists of 316*332 grids, and each Arctic freeze-thaw data file consists of 304*448 grids. The microwave scatterometer data set comprises backscattering data from QScat (2000-2009) and ASCAT (2009-2015), with a temporal coverage from 2000 to 2015 and a spatial resolution of 4.45 km. Each Antarctic data file consists of 1940*1940 grids, and each Arctic data file consists of 810*680 grids. The temporal resolution of the data set is one day, and the data cover both Antarctica and Arctic ice sheets.
Li Xinwu, Liang Lei
Using the Modis1B data of 11 scenes from 2003 to 2013 (the ice shelf Modis1B data published on the NSIDC website), the surface velocity of the Antarctic Amery Ice Shelf was extracted by the subpixel cross-correlation method, the ice velocity was extracted by the COSI-Corr software, and then the time sequence of annual average velocities for nearly ten years was obtained. Due to the lack of field observations in the study area, the accuracy of the ice flow results was estimated by using the offset value of the stable region, and the ice flow error was approximately ±50 m/year. The ice velocity data date from 2003 to 2013, the temporal resolution is one year, and the data cover the Amery area with a spatial resolution of 500 m. A GeoTIFF file of velocity data is stored every year. For details regarding the data, please refer to the Amery Ice Flow Field - Data Description.
JIANG Liming
Under the background of global warming, mountain glaciers worldwide are facing strong ablation and retreat, but from existing field observations, it is found that most of the glaciers in the Karakorum region remain stable or are advancing, which is called the ＂Karakorum anomaly＂. Glacier surface velocity is an important parameter for studying glacier dynamics and mass balance. Studying the temporal and spatial variation characteristics of glacier velocity in central Karakorum is significant for understanding the dynamic characteristics of the glacier in this region and its response to climate change. Four pairs of Landsat 7 ETM+ images acquired in 1999 to 2003 (images acquired on 1999.7.16, 2000.6.16, 2001.7.21, 2002.8.9, 2002.4.19, 2003.3.21) were selected; using the panchromatic band with a resolution of 15 m, each pair of images was accurately registered, and then cross-correlation calculations were then performed on each image pair after registration to obtain the surface velocity of the glacier in the central Karakorum region from 1999 to 2003. Due to the lack of velocity observation data in the study area, the accuracy of the ice flow results is estimated using the offset value of the stable region, and the surface velocity error of the glacier is approximately ±7 m/year. The glacier velocity data dates are from 1999 to 2003, with a temporal resolution of one year. They cover the central Karakorum region, with a spatial resolution of 30 m. The data are stored as a GeoTIFF file every year. For details regarding the data, please refer to the data description.
JIANG Liming
The Antarctic ice sheet elevation data were generated from radar altimeter data (Envisat RA-2) and lidar data (ICESat/GLAS). To improve the accuracy of the ICESat/GLAS data, five different quality control indicators were used to process the GLAS data, filtering out 8.36% unqualified data. These five quality control indicators were used to eliminate satellite location error, atmospheric forward scattering, saturation and cloud effects. At the same time, dry and wet tropospheric, correction, solid tide and extreme tide corrections were performed on the Envisat RA-2 data. For the two different elevation data, an elevation relative correction method based on the geometric intersection of Envisat RA-2 and GLAS data spot footprints was proposed, which was used to analyze the point pairs of GLAS footprints and Envisat RA-2 data center points, establish the correlation between the height difference of these intersection points (GLAS-RA-2) and the roughness of the terrain relief, and perform the relative correction of the Envisat RA-2 data to the point pairs with stable correlation. By analyzing the altimetry density in different areas of the Antarctic ice sheet, the final DEM resolution was determined to be 1000 meters. Considering the differences between the Prydz Bay and the inland regions of the Antarctic, the Antarctic ice sheet was divided into 16 sections. The best interpolation model and parameters were determined by semivariogram analysis, and the Antarctic ice sheet elevation data with a resolution of 1000 meters were generated by the Kriging interpolation method. The new Antarctic DEM was verified by two kinds of airborne lidar data and GPS data measured by multiple Antarctic expeditions of China. The results showed that the differences between the new DEM and the measured data ranged from 3.21 to 27.84 meters, and the error distribution was closely related to the slope.
HUANG Huabin
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