Surface cover map in the Antarctic (1999-2003)

A high-resolution remote sensing image mosaic of the entire Antarctic was generated by synthesizing the 1073 images taken by American Landsat 7 during 1999 to 2003 and the medium-resolution MODIS image (taken in 2005) covering south of 82.5°southern latitude. Based on the mosaic, combined with the needs of Antarctic scientific research, Antarctica land cover was divided into six types using the combination method of computer automatic interpretation and artificial assistance. They were blue ice, fissures, bare rocks, water bodies, moraines and firns, and the areas and proportions of the above types were 225,207.29 square kilometers (1.651%), 7153.36 square kilometers (0.052%), 72,958.04 square kilometers (0.535%), 189.43 square kilometers (0.001%), 310.76 square kilometers (0.003%), and 13337392.66 square kilometers (97.758%), respectively. The map is a satellite image map of approximate true color synthesis, and the regions of various cover types are represented by different color blocks. The map mainly provides a reference for popular scientific research, geography education and science popularization.

0 2020-01-19

Material balance data on the surface of the West Antarctic ice sheet (1800-2000)

1 High resolution gridded West Antarctic surface mass balance dataset, its project is Polar Stereographic Projection 2. The kriging like interpolation method is used to reconstruct the high‐spatial resolution surface mass balance (SMB) over the West Antarctic Ice Sheet (WAIS) from 1800 to 2010, based on ice core records, the outputs of the European Centre for Medium‐Range Weather Forecasts “Interim” reanalysis (ERA‐Interim) as well as the latest polar version of the Regional Atmospheric Climate Model (RACMO2.3p2). 3. Its accuracy is higher than reanalysis data. 4. Temporal resolution: 1800-2010; Temporal resolution: 1 year; Spatial coverage : the whole West Antarctic Ice Sheet, Spatial resolution: 25km х 25km

0 2020-01-19

SAR ice sheet freezing-thawing data for Antarctica and Greenland V1.0 (2015-2019)

At present, based on the proposed SAR ice sheet freeze-thaw detection algorithm using change detection and decision tree algorithm, the monthly average ice sheet freeze-thaw is detected using sentinel-1 EW SAR data. At the same time, using the developed production module of freeze-thaw products based on big data platform, the international first production of Antarctic ice sheet and Greenland ice sheet freeze-thaw products. Through the development of automatic weather station temperature data, the ice sheet freeze-thaw detection accuracy reaches 90%. At present, the acquisition time of data products is mainly the summer of the north and south poles, among which the Antarctic ice sheet products are January, February, March, October, November, December and Greenland products are may, June, July, August, September and October.

0 2020-01-19

Radiometer ice sheet freezing-thawing data for Antarctica and Greenland V1.0 (2016-2019)

The coverage time of microwave radiometer ice sheet freeze-thaw data set is updated to 2016-2019, with a spatial resolution of 25 km; the remote sensing inversion method based on microwave radiometer adopts the improved wavelet based ice sheet freeze-thaw detection algorithm, which takes into account the change of ice sheet freeze-thaw brightness temperature characteristics in time. First, the long-time brightness temperature data of all ice sheet areas in Greenland is small by using wavelet transform. The multi-scale decomposition of wave is used to analyze the edge information at different scales. Thirdly, the edge information of ice sheet melting and refreezing is separated from the noise by ANOVA. Based on the extracted edge information of long-term brightness and temperature change of ice sheet, the optimal edge threshold of dry snow and wet snow classification is determined by using the generalized Gaussian model, so as to detect the melting area of Greenland ice sheet. Finally, based on the principle of space automatic error correction, the error results caused by noise are detected by using the space neighborhood error correction operator, and the error is corrected manually. The brightness and temperature data of passive microwave in long time series come from SMMR, SSM / I and SSMI / s sensors. To ensure simultaneous interpreting of the brightness temperature of different sensors, simultaneous interpreting of different sensor brightness temperatures is made before freezing and thawing. Through the verification of the actual measurement site, it shows that the detection accuracy of Greenland ice sheet freeze-thaw is more than 70%.

0 2020-01-19

Radiometer and scatterometer data for Antarctica and the Arctic ice sheet (1978-2015)

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.

0 2020-01-19

Prokaryotic distribution over the Arctic, Antarctic and Tibetan glaciers V1.0 (2010-2018)

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.

0 2020-01-19

Law Dome area Methane concentration (1010-1980)

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

0 2020-01-18

Ice velocity of the Amery ice shelf in the Antarctic (Version 1.0) (2003-2013)

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.

0 2020-01-18

Ice elevation changes for typical glaciers on the QTP V1.0 (2000-2013)

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.

0 2020-01-18

Ice crack dataset of Antarctican and Greenland V1.0 (2015-2019)

Based on the sentinel-1 hyperspectral wide-band SAR data, using the proposed u-net ice fissure detection method, the ice fissure elevation data of the north and south polar ice sheet are formed. Firstly, the data preprocessing of sentinel-1 hyperspectral wide-band SAR includes radiometric calibration, ice cover range determination and speckle noise removal. In order to suppress the speckle noise of SAR data, and to ensure the ice fracture characteristics, we use ppb method to remove multiplicative noise. This method can not only effectively remove spots, but also retain the characteristics of ice cracks. Secondly, we use the u-net based ice crack detection algorithm to extract ice cracks. In order to obtain the correct ice fracture SAR data samples, we select the SAR samples by comparing the high-resolution optical data of ice fracture to form the ice fracture SAR data samples. Based on the SAR data of ice fracture area and non ice fracture area, we use u-net method to extract ice fracture. Finally, we geocode the detected ice fracture data to form the ice fracture products of the north and south polar.

0 2020-01-18