The frozen soil type map of Kazakhstan (1:10,000,000) includes three .shp vector layers: 1, Polyline ranges.shp, indicating the extent of frozen soil; 2, Polygon kaz_perm.shp, frozen soil; 3, An attribute description Word file. The kaz_perm attribute table includes four fields: ID, REGION, SUBREGION, M_RANGE. Comparison of the main attributes: First, Area I. Altai-TienShan Second, Region: High mountains I.1. Altai, I.2. Saur-Tarbagatai, I.3.Dzhungarskyi, I.4. Northern Tien Shan, I.5. Western Tien Shan Intermountain depressions I.6. Zaysanskyi, I.7. Alakulskyi, I.8. Iliyskyi II. Western Siberian Second, Region: Planes II.1. Northern Kazakhstanskyi V. Western Kazakhstanskaya III. Kazakh small hills area IV. Turanskaya: IV.1. Turgayskyi IV.2. Near Aaralskyi IV.3. Chuysko-Syrdaryinskyi IV.4. South-Balkhashskyi V. Western Kazakhstanskaya: V.1. Mugodzhar-Uralskyi V.2. Near Caspian V.3. manghyshlak-Ustyrtskyi Third, Sub-region: I.1.1. Western Altai I.1.2. South Altai I.1.3. Kalbinskyi I.2.1. Tarbagatayskyi I.2.2. Saurskyi I.3.1. Nortern Dzhungarskyi I.3.2. Western Dzhungarskyi I.3.3. Southern Dzhungarskyi I.4.1. Kirgizskyi Alatau I.4.2. Zailiyskyi-Kungeyskyi I.4.3. Ketmenskyi I.4.4. Bayankolskyi I.5.1. Karatauskyi I.5.2. Talaso-Ugamskyi The layer projection information is as follows: GEOGCS["GCS_WGS_1984", DATUM["WGS_1984", SPHEROID["WGS_1984", 6378137.0, 298.257223563]], PRIMEM["Greenwich", 0.0], UNIT["Degree",0.0174532925199433]] Different regions feature different frozen soil attributes, and the specific attribute information can be found in the Word file.
Sergei Marchenko
This product is based on multi-source remote sensing DEM data generation. The steps are as follows: select control points in relatively stable and flat terrain area with Landsat ETM +, SRTM and ICESat remote sensing data as reference. The horizontal coordinates of the control points are obtained with Landsat ETM + l1t panchromatic image as the horizontal reference. The height coordinates of the control points are mainly obtained by ICESat gla14 elevation data, and are supplemented by SRTM elevation data in areas without ICESat distribution. Using the selected control points and automatically generated connection points, the lens distortion and residual deformation are compensated by Brown's physical model, so that the total RMSE of all stereo image pairs in the aerial triangulation results is less than 1 pixel. In order to edit the extracted DEM data to eliminate the obvious elevation abnormal value, DEM Interpolation, DEM filtering and DEM smoothing are used to edit the DEM on the glacier, and kh-9 DEM data in the West Kunlun West and West Kunlun east regions are spliced to form products.
ZHOU Jianmin
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
The data is the monthly average spatial distribution of frozen soil in Heihe River Basin from 2000 to 2009. Based on the grid temperature data of Heihe River Basin from 2000 to 2009, the freezing and thawing state of surface soil is divided into three kinds: unfreezing state, incomplete freezing state and complete freezing state. Complete freezing means that the soil is completely frozen in the whole month. Incomplete freezing refers to soil freezing days ≤ 30 days but ≥ 1 day in a month, and the soil has freeze-thaw cycle. Non freezing means that the soil will not freeze this month. The data is in the form of grid, which can be opened in ArcGIS. 1 represents unfrozen state, 2 represents unfrozen state, 3 represents fully frozen state
PENG Xiaoqing, ZHANG Tingjun
The Map of Permafrost on the Qinghai-Tibet Plateau (1:3,000,000) (Shude Li and Guodong Cheng, 1996) was made by the State Key Laboratory of Frozen Soil Engineering, LIGG, CAS (currently called the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences). It was based on first-hand information from the study of frozen soil and previous research papers and literature. By detailed study and consultation of aerial photographs, satellite images, the Permafrost Map along the Qinghai-Tibet Highway (1:600,000) (Boliang Tong, et al., 1983), Geomorphological Map of the Qilian Mountains (1:1,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1985), Natural Landscape Map of Qinghai-Tibetan Plateau (1:3,000,000) (Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, 1990), Quaternary Glacial Distribution Map of the Qinghai-Tibetan Plateau (1:3,000,000) (Bingyuan Li and Jijun Li, 1991), Frozen Soil Remote Sensing Map of the Western Channel Project of the South-North Water Diversion in the Region of the Tongtian-Yalong Rivers (1:500,000) (Lanzhou Institute of Glaciology and Cryopedology, Chinese Academy of Sciences, 1995), and Map of Snow, Ice, Frozen Ground in China (1:4,000,000) (Yafeng Shi and Desheng Mi, 1988), with editing on 1,000,000 aerial survey topographic maps, and the 1:3,000,000 Map of Permafrost on the Qinghai-Tibetan Plateau was then generated. It was later digitized by Zhuotong Nan of the Cold and Arid Regions Environmental and Engineering Research Institute, Chinese Academy of Sciences. The data include: 1) Digitized distribution map of frozen soil on the Qinghai-Tibetan Plateau 2) Scanned map of frozen soil map on the Qinghai-Tibetan Plateau The types of frozen soil in the digitized frozen soil map include: 0. Seasonally frozen ground; seasonal frozen soil 1. Permafrost 2. Island permafrost; 3. Continuous permafrost;
CHENG Guodong, LI Shude, NAN Zhuotong, TONG Boliang
The coverage time of microwave scatterometer ice sheet freeze-thaw data is updated to 2015-2019, with a spatial resolution of 4.45km. The time resolution is day by day, and the coverage range is the polar ice sheet. The remote sensing inversion method based on microwave radiometer considers the change of snow cover characteristics in space-time and space. Firstly, the DVPR time series data of scatterometer data is extracted, the high time resolution of scatterometer data is effectively used, and the influence of terrain is removed by channel difference. Then, the variance value of time series at each sampling point is simulated by generalized Gaussian model, so as to make the region. The generalized Gaussian model needs less input parameters than the traditional double Gaussian model, and the obtained threshold is also unique. Finally, the moving window segmentation algorithm is used to accurately find the melting start time, end time and duration of the wet snow point, which can effectively remove the temperature mutation in the melting or non melting period. The impact. The data of long time series microwave scatterometer are from QSCAT and ASCAT. The verification of the measured stations shows that the detection accuracy of ice sheet freezing and thawing is over 70%. The data is stored in a bin file every day. Each file of Antarctic freeze-thaw data based on microwave scatterometer is composed of 810 * 680 grid, and each file of Greenland ice sheet freeze-thaw data is composed of 810 * 680 grid (0 value: non melting area, 1 Value: melting area).
Liang Lei
As an important parameter of permafrost research, the freezing-thawing index is of great significance to the research of permafrost, and it is also an important index for the research of climate change.The cumulative value of daily air temperature or surface soil temperature at a given time. This data is based on the daily surface temperature observation data of 15 regular meteorological stations in the heihe valley of China meteorological administration, and the annual surface freezing-thawing index of each meteorological station from 1960 to 2006 is calculated.
ZHANG Tingjun
1. Data overview: This data set is the data set of frozen depth of permafrost observed artificially in qilian station from January 1, 2012 to December 31, 2012, and observed at 08 o 'clock every day. 2. Data content: The data content is the frozen depth data set of the tundra.The frozen depth (length) of the water in the inner rubber tube is used as a record to determine the freezing level and the upper and lower depth of the frozen layer according to the freezing position and length of the water in the frozen pot.In centimeters (cm), round off the whole number and round off the decimal.Observe once a day at 0:8. 3. Space and time range: Geographical coordinates: longitude: 99° 53’e;Latitude: 38°16 'N;Height: 2981.0 m
CHEN Rensheng, SONG Yaoxuan, HAN Chuntan, LIU Junfeng, YANG Yong
Glaciers are sensitive to climate change and are important indicators and amplifiers of global change. In inland river regions, river runoff mainly comes from mountain ice and snow melt. Glaciers are very important "solid reservoirs" in these regions, and glacial melt water is an important source of supply for the tributaries of the Heihe River. The inventory of glaciers in the Heihe River Basin was completed from 1979 to 1980. For related information, please refer to "Chinese Glacier Inventory-Qilian Mountains" edited by Wang Zongtai and others. In 2004, the relevant results of the "China Glacier Inventory" were systematically digitized and a database was established. The final results were released through the "China Glacier Information System". However, in the process of coordinate restoration, the accuracy of the reference data was poor, and the glaciers in the Heihe River Basin had obvious position shifts. Therefore, we used the Landsat remote sensing image corrected by ortho-geometric correction. The processed Heihe Glacier distribution data is highly consistent with the existing basic geographic information in China in terms of geometric accuracy, and consistent with the first glacier inventory in terms of attributes.
WANG Zongtai
Snow is a significant component of the ecosystem and water resources in high-mountain Asia (HMA). Therefore, accurate, continuous, and long-term snow monitoring is indispensable for the water resources management and economic development. The present study improves the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra and Aqua satellites 8 d (“d” denotes “day”) composite snow cover Collection 6 (C6) products, named MOD10A2.006 (Terra) and MYD10A2.006 (Aqua), for HMA with a multistep approach. The primary purpose of this study was to reduce uncertainty in the Terra–Aqua MODIS snow cover products and generate a combined snow cover product. For reducing underestimation mainly caused by cloud cover, we used seasonal, temporal, and spatial filters. For reducing overestimation caused by MODIS sensors, we combined Terra and Aqua MODIS snow cover products, considering snow only if a pixel represents snow in both the products; otherwise it is classified as no snow, unlike some previous studies which consider snow if any of the Terra or Aqua product identifies snow. Our methodology generates a new product which removes a significant amount of uncertainty in Terra and Aqua MODIS 8 d composite C6 products comprising 46 % overestimation and 3.66 % underestimation, mainly caused by sensor limitations and cloud cover, respectively. The results were validated using Landsat 8 data, both for winter and summer at 20 well-distributed sites in the study area. Our validated adopted methodology improved accuracy by 10 % on average, compared to Landsat data. The final product covers the period from 2002 to 2018, comprising a combination of snow and glaciers created by merging Randolph Glacier Inventory version 6.0 (RGI 6.0) separated as debris-covered and debris-free with the final snow product MOYDGL06*. We have processed approximately 746 images of both Terra and Aqua MODIS snow containing approximately 100 000 satellite individual images. Furthermore, this product can serve as a valuable input dataset for hydrological and glaciological modelling to assess the melt contribution of snow-covered areas. The data, which can be used in various climatological and water-related studies, are available for end users at https://doi.org/10.1594/PANGAEA.901821 (Muhammad and Thapa, 2019).
SHER Muhammad
The dataset of ground truth measurement synchronizing with ALOS PALSAR was obtained in the Linze grassland foci experimental area on Jun. 10, 2008. The data were in FBS mode and HH/HV polarization combinations, and the overpass time was approximately at 23:39 BJT. Observations were carried out in plots A, B, C, D and E, which were divided into 6×6 subsites, with each one spanning a 120×120 m2 plot. Soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring and the mean soil temperature from 0-5cm by the probe thermometer were measured in A, B and C; the soil temperature, soil moisture, the loss tangent, soil conductivity, the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, and the mean soil temperature from 0-5cm by the probe thermometer in D and E. Data were archived in Excel file. See WATER: Dataset of setting of the sampling plots and stripes in the foci experimental area of Linze station for more information.
BAI Yanfen, CAO Yongpan, GE Chunmei, HU Xiaoli, WANG Shuguo, Wang Weizhen, WU Yueru, ZHU Shijie, FENG Lei
As the main parameter in the land surface energy balance, surface temperature indicates the degree of land-atmosphere energy and water transfer and is widely used in research on climatology, hydrology and ecology. In the study of frozen soil, climate is one of the decisive factors for the existence and development of frozen soil. The surface temperature is the main climatic factor affecting the distribution of frozen soil and affects the occurrence, development and distribution of frozen soil. It is the upper boundary condition for modelling frozen soil and is significant to the study of hydrological processes in cold regions. The data set was based on the DEM and observation station data of the Tibetan Plateau Engineering Corridor and analysed the changing trend of surface temperature on the Tibetan Plateau from 2000 to 2014. Using the surface temperature data products MOD11A1/A2 and MYD11A1/A2 of MODIS aboard Terra and Aqua, the surface temperature information under cloud cover was reconstructed based on the spatio-temporal information of the images. The reconstruction information and surface temperature representativeness problems were analysed using information obtained from 8 sites, including the Kunlun Mountains (wetland, grassland), Beiluhe (grassland, meadow), Kaixinling (meadow, grassland), and Tanggula Mountain (meadow, wetland). According to the correlation coefficient (R2), root-mean-square error (RMSE), mean absolute error (MAE) and mean deviation (MBE), the following results were obtained: (1) the reconstruction accuracy of MODIS surface temperature under cloud cover is higher when it is based on spatio-temporal information; (2) the weighted average representation is the best when generalizing four observations of Terra and Aqua. By analysing the reconstruction of MODIS surface temperature information and representativeness problems, the average annual MODIS surface temperature data of the Tibetan Plateau and the engineering corridor from 2000 to 2010 were obtained. According to the data set, the surface temperature from 2000 to 2010 also experienced volatile rising trends from 2000 to 2010, which is basically consistent with the changing trend of the climate change in the permafrost regions of the Tibetan Plateau and the Qinghai-Tibet Engineering Corridor.
NIU Fujun, YIN Guoan
This glacial lake inventory receives joint support from International Centre for Integrated Mountain Development (ICIMOD) and United Nations Environment Programme/Regional Resource Centre, Asia and the Pacific (UNEP/RRC-AP). 1. This glacial lake inventory referred to Landsat 4/5 (MSS, TM/1984/1999), Landsat 7 (TM & ETM+), IRS-1C, LISS-III (1995 IRS-1C), (1997 IRS-1D) and other remote sensing data. It reflects the current situation of glacial lakes with areas larger than 0.01 km2 in 2000. 2. Glacial Lake Inventory Coverage: Tista Basin, Sikkim Region 3. Glacial Lake Inventory includes: glacial lake inventory, glacial lake type, glacial lake orientation, glacial lake width, glacial lake area, glacial lake depth, glacial lake length and other attributes. 4. Projection parameter: Projection: Lambert conformal conic Ellipsoid: Everest (India 1956) Datum: India (India, Sikkim) False easting: 2743196.40 False northing: 914398.80 Central meridian: 90°00’00” E Central parallel: 26°00’00” N Scale factor: 0.998786 Standard parallel 1: 23°09’28.17” N Standard parallel 2: 28°49’8.18” N Minimum X Value: 2545172 Maximum X Value: 2645240 Minimum Y Value: 1026436 Maximum Y Value: 1163523 For a detailed data description, please refer to the data file and report.
Samjwal Ratna Bajracharya Samjwal Ratna Bajracharya
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in in No. 2 and 3 quadrates of the A'rou foci experimental areas on Mar. 15, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:35 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Only corner points of each subsite were chosen for observations. In No. 2 quadrate, simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, the mean soil temperature from 0-5cm by the probe thermometer, the surface radiative temperature measured three times by the hand-held infrared thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). In No. 3 quadrate, simultaneous with the satellite overpass, numerous ground data were collected, the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity by the POGO soil sensor, soil volumetric moisture by ML2X, the mean soil temperature from 0-5cm by the probe thermometer, the surface radiative temperature measured three times by the hand-held infrared thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Besides, GPR (Ground Penetration Radar) observations were also carried out in No. 1 quadrate of A'rou. Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
CAO Yongpan, GU Juan, HAN Xujun, LI Zhe, Wang Weizhen, WU Yueru, LI Hua, YU Meiyan, ZHAO Jin, PATRICK Klenk, YUAN Xiaolong
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
Due to the short snow duration and thin snow layer on the Tibetan Plateau, dynamic monitoring data for daily fractional snow cover are urgently needed in order to better understand water cycling and other processes. This data set is based on MODIS Snow Cover Daily L3 Global 500 m Grid data and includes the Normalized Difference Snow Index (NDSI) data product generated from MODIS/Terra data (MOD10A1) and MODIS/Aqua data (MYD10A1). The data are in the .hdf format. The projection method is sinusoidal map projection. Combining the advantages of 90 m SRTM terrain data and fractional snow cover estimation algorithms under multiple cloud coverage types, the fractional snow cover under different cloud coverage conditions can be re-estimated to meet the production requirements of the daily less cloud (< 10%) data products in High Asia. On the basis of this method, the MODIS daily fractional snow cover data set over High Asia (2002-2016) was constructed. By taking the binary snow product under cloudless conditions as a reference, the spatial and temporal comparisons between snow distribution and snow coverage show that the spatio-temporal characteristics of the product and the binary products are highly consistent. Taking the winter of 2013 as an example, when the fractional snow cover is greater than 50%, the correlation can reach 0.8628. This data set provides daily fractional snow cover data for use in studying snow dynamics, the climate and environment, hydrology, energy balance, and disaster assessment in High Asia.
QIU Yubao
The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in the arid region hydrological experimental area on Sep. 19, 2007 during the pre-observation period. One scene of Envisat ASAR image was captured on Sep. 19. The data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:29 BJT. Those provide reliable ground data for remote sensing retrieval and validation of soil moisture from Envisat ASAR image. Observation items included: (1) soil moisture measured by the cutting ring method in Linze reed land, Zhangye farmland, Zhangye gobi, Linze maize land, Linze alfalfa land, Zhangye weather station, and Linze wetland. (2) GPS measured by GARMIN GPS 76 (3) vegetation measurements including the vegetation height, the green weight, the dry weight, the sampling method, and descriptions on the land type, uniformity and dry and wet conditions (4) atmospheric parameters at Daman Water Management office measured by CE318 (produced by CIMEL in France). The total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, particle size spectrum and phase function were then retrieved from these observations. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318. Those data include the raw data in .k7 and can be opened by ASTPWin. ReadMetext files (.txt) is attached for detail. Processed data (after retrieval of the raw data) archived as Excel files are on optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, the near surface air temperature, the solar azimuth, zenith, solar distance correlation factors, and air column mass number. (5) roughness measured by the roughness plate together with the digital camera. The coordinates of the sample would be got with the help of ArcView; and after geometric correction, surface height standard deviation (cm) and correlation length (cm) could be acquired based on the formula listed on pages 234-236, Microwave Remote Sensing (Vol. II). The roughness data were initialized by the sample name, which was followed by the serial number, the name of the file, standard deviation and correlation length. Each text files (.txt) file is matched with one sample photo and standard deviation and correlation length represent the roughness. In addition, the length of 101 radius is also included for further checking.
CHE Tao, LI Xin, BAI Yunjie, DING Songchuang, GAO Song, HAN Xujun, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, PAN Xiaoduo, QIN Chun, RAN Youhua, WANG Xufeng, WU Yueru, YAN Qiaodi, ZHANG Lingmei, FANG Li, LI Hua, Liu Qiang, Wen Jianguang, MA Hongwei, YAN Yeqing, YUAN Xiaolong
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 dataset of ground truth measurements synchronizing with Envisat ASAR and ALOS PALSAR was obtained in the Linze station foci experimental area on May 24, 2008. The data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) soil moisture (0-5cm) measured once by cutting ring method at corner points of the 40 subplots of the west-east desert transit zone strip, one time by cutting ring method in nine subplots of the north-south desert transit zone, strip and once by the cutting ring and three times by ML2X Soil Moisture Tachometer in the center points of nine subplots of Wulidun farmland quadrates . The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature by measured two handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute which were both calibrated) in 40 subplots of the west-east desert transit zone strip (repeated 14-30 times each), and nine subplots of the north-south desert transit zone strip (repeated 12-30 times). There are 34 sample points in total and each was repeated three times synchronizing with the airplane. Photos were taken. Data were archived as Excel files. (3) LAI, the plant height and the spacing measured by the ruler and the set square in Wulidun farmland quadrates and Linze station quadrates. Part of the samples were also measured by LI-3100. Data were archived as Excel files. See the metadata record “WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area” for more information of the quadrate locations.
BAI Yanfen, DING Songchuang, PAN Xiaoduo, WANG Yang, ZHU Shijie, LI Jing, XIAO Zhiqiang, SUN Jinxia
Because of its unique natural conditions and geographical location, the Arctic region plays a very important role in global change. Polar sea ice, as an important influencing factor of climate change, is a sensitive instrument of global climate change. The Yellow River Station, one of China's research stations in the Arctic, focuses on supporting the three scientific fields of global change and its regional response, the polar space environment and space climate, and the life characteristics and processes in the polar environment, providing an important platform for China's in-depth scientific research activities in the Arctic. Therefore, the product data set of data validation for key areas of Arctic sea ice in recent years is constructed to monitor the key areas of Arctic sea ice.
Chen Fu, QIU Yubao
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