This biophysical permafrost zonation map was produced using a rule-based GIS model that integrated a new permafrost extent, climate conditions, vegetation structure, soil and topographic conditions, as well as a yedoma map. Different from the previous maps, permafrost in this map is classified into five types: climate-driven, climate-driven/ecosystem-modified, climate-driven/ecosystem protected, ecosystem-driven, and ecosystem-protected. Excluding glaciers and lakes, the areas of these five types in the Northern Hemisphere are 3.66×106 km2, 8.06×106 km2, 0.62×106 km2, 5.79×106 km2, and 1.63×106 km2, respectively. 81% of the permafrost regions in the Northern Hemisphere are modified, driven, or protected by ecosystems, indicating the dominant role of ecosystems in permafrost stability in the Northern Hemisphere. Permafrost driven solely by climate occupies 19% of permafrost regions, mainly in High Arctic and high mountains areas, such as the Qinghai-Tibet Plateau.
RAN Youhua, M. Torre Jorgenson, LI Xin, JIN Huijun, Wu Tonghua, Li Ren, CHENG Guodong
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
The dataset of ground truth measurements for snow synchronizing with EO-1 Hyperion and Landsat TM was obtained in the Binggou watershed foci experimental area on Mar. 17, 2008. Observation items included: (1) Snow parameters as snow depth by the ruler, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer simultaneous with the satellite in BG-A, BG-E, BG-F and BG-H. (2) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork in BG-A, BG-E and BG-H. Besides, 25-hour fixed-point continuous observation was carried out at the Binggou cold region hydrometerological station. (3) The snow spectrum by ASD (Xinjiang Meteorological Administration) (4) Snow albedo by the total radiometer Two files including raw data and preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
The dataset of ground truth measurements for snow synchronizing with Envisat ASAR was obtained in the Binggou watershed foci experimental area on Mar. 15, 2008. The Envisat ASAR data were acquired in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-B, BG-D, BG-E and BG-F; (2) Snow parameters including the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, snow depth by the ruler, and the snow surface temperature synchronizing with ASAR in BG-H, BG-D, BG-E and BG-F; (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) synchronizing with ASAR in BG-H15; the major and minor axis and shape of the snow layer grain through the self-made snow sieve. Two files including raw data and the preprocessed data were archived.
BAI Yanfen, BAI Yunjie, GE Chunmei, HAO Xiaohua, LI Hongyi, LIANG Ji, SHU Lele, WANG Xufeng, XU Zhen, MA Mingguo, QU Wei, REN Jie, CHANG Cun, DOU Yan, MA Zhongguo, LIU Yan, ZHANG Pu
Kilometer-level spatially complete (seamless) land surface temperature products have a wide range of applications needs in climate change and other fields. Satellite retrieved LST has high reliability. Integrating the LST retrieved from thermal infrared and microwave remote sensing observation is an effective way to obtain the SLT with certain accuracy and spatial integrity. Based on this guiding ideology, the author developed a framework for retrieving 1km and seamless LST over China landmass, and generated the LST data set accordingly (2002-2020) Firstly, a look-up table based empirical retrieval algorithm is developed for retrieving microwave LST from AMSR-E/AMSR2 observations. Then, AMSR-E/AMSR2 LST is downscaled by using geographic weighted regression to obtain 1km LST. Finally, the multi-scale Kalman filter is used to fuse AMSR-E/AMSR2 LST and MODIS LST to generate a 1km seamless LST data set. The ground valuation results show that the root mean square error (RMSE) of the 1km seamless LST is about 3K. In addition, the spatial distribution of the 1km seamless LST is consistent with MODIS LST and CLDAS LST.
CHENG Jie, DONG Shengyue, SHI Jiancheng
This data set includes the microwave brightness temperatures obtained by the spaceborne microwave radiometer SSM/I carried by the US Defense Meteorological Satellite Program (DMSP) satellite. It contains the twice daily (ascending and descending) brightness temperatures of seven channels, which are 19H, 19V, 22V, 37H, 37V, 85H, and 85V. The Specialized Microwave Imager (SSM/I) was developed by the Hughes Corporation of the United States. In 1987, it was first carried into the space on the Block 5D-/F8 satellite of the US Defense Meteorological Satellite Program (DMSP) to perform a detection mission. In the 10 years from when the DMSP soared to orbit in 1987 to when the TRMM soared to orbit in 1997, the SSM/I was the world's most advanced spaceborne passive microwave remote sensing detection instrument, having the highest spatial resolution in the world. The DMSP satellite is in a near-polar circular solar synchronous orbit; the elevation is approximately 833 km, the inclination is 98.8 degrees, and the orbital period is 102.2 minutes. It passes through the equator at approximately 6:00 local time and covers the whole world once every 24 hours. The SSM/I consists of seven channels set at four frequencies, and the center frequencies are 19.35, 22.24, 37.05, and 85.50 GHz. The instrument actually comprises seven independent, total-power, balanced-mixing, superheterodyne passive microwave radiometer systems, and it can simultaneously measure microwave radiation from Earth and the atmospheric systems. Except for the 22.24 GHz frequency, all the frequencies have both horizontal and vertical polarization states. Some Eigenvalues of SSM/I Channel Frequency (GHz) Polarization Mode (V/H) Spatial Resolution (km * km) Footprint Size (km) 19V 19.35 V 25×25 56 19H 19.35 H 25×25 56 22V 22.24 V 25×25 45 37V 37.05 V 25×25 33 37H 37.05 H 25×25 33 85V 85.50 V 12.5×12.5 14 85H 85.50 H 12.5×12.5 14 1. File Format and Naming: Each group of data consists of remote sensing data files, .JPG image files and .met auxiliary information files as well as .TIM time information files and the corresponding .met time information auxiliary files. The data file names and naming rules for each group in the SSMI_Grid_China directory are as follows: China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V (remote sensing data); China-EASE-Fnn -ML/HaaaabbbA/D.ccH/V.jpg (image file); China-EASE-Fnn-ML/HaaaabbbA/D.ccH/V.met (auxiliary information document); China-EASE-Fnn-ML/HaaaabbbA/D.TIM (time information file); and China-EASE- Fnn -ML/HaaaabbbA/D.TIM.met (time information auxiliary file). Among them, EASE stands for EASE-Grid projection mode; Fnn represents carrier satellite number (F08, F11, and F13); ML/H represents multichannel low resolution and multichannel high resolution; A/D stands for ascending (A) and descending (D); aaaa represents the year; bbb represents the Julian day of the year; cc represents the channel number (19H, 19V, 22V, 37H, 37V, 85H, and 85V); and H/V represents horizontal polarization (H) and vertical polarization (V). 2. Coordinate System and Projection: The projection method is an equal-area secant cylindrical projection, and the double standard latitude is 30 degrees north and south. For more information on EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection method into a geographic projection method, please refer to the ease2geo.prj file, which reads as follows. Input Projection cylindrical Units meters Parameters 6371228 6371228 1 /* Enter projection type (1, 2, or 3) 0 00 00 /* Longitude of central meridian 30 00 00 /* Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd Parameters End 3. Data Format: Stored as binary integers, Row number: 308 *166,each datum occupies 2 bytes. The data that are actually stored in this data set are the brightness temperatures *10, and after reading the data, they need to be divided by 10 to obtain true brightness temperature. 4. Data Resolution: Spatial resolution: 25 km, 12.5 km (SSM/I 85 GHz); Time resolution: day by day, from 1978 to 2007. 5. The Spatial Coverage: Longitude: 60°-140° east longitude; Latitude: 15°-55° north latitude. 6. Data Reading: Each group of data includes remote sensing image data files, .JPG image files and .met auxiliary information files. The JPG files can be opened with Windows image and fax viewers. The .met auxiliary information files can be opened with notepad, and the remote sensing image data files can be opened in ENVI and ERDAS software.
National Snow and Ice Data Center（NSIDC）
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
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.1 (freeze/thaw status), No. 2 (snow parameters) and No. 3 (freeze/thaw status) quadrates of the A'rou foci experimental areas on Mar. 12, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:29 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Center and corner points of each subsite were chosen for all observations except for the cutting ring measurements which only observed the center points. In No. 1 quadrate, 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 soil volumetric moisture profile (10cm, 20cm, 30cm, 40cm, 60cm and 100cm) by PR2, the mean soil temperature from 0-5cm by the probe thermometer, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). In No. 2 quadrate, simultaneous with ASAR, snow parameters were measured, the snow surface temperature by the thermal infrared probe, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, snow density by the aluminum case, the snow surface temperature and the snow-soil interface temperature by the thermal infrared probe, snow spectrum by ASD, and snow albedo by the total radiometer. In No. 3 quadrate soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity were measured by WET, the mean soil temperature from 0-5cm by the probe thermometer (5# and 7#), the surface radiative temperature by the hand-held infrared thermometer (5#), and 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 verification of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yanfen, CAO Yongpan, GE Chunmei, GU Juan, HAN Xujun, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, QU Wei, CHANG Cun, DOU Yan, MA Zhongguo, YU Meiyan, ZHAO Jin, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu, PATRICK Klenk, YUAN Xiaolong
This dataset uses daily temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015). It is generated by the dual-index (TB, 37v, SG) freeze-thaw discrimination algorithm. The classification results include the frozen surface, the thawed surface, the deserts and water bodies. The data coverage is the main part of China’s mainland, with a spatial resolution of 25.067525 km via the EASE-Grid projection method, and it is stored in ASCIIGRID format. All the ASCII files in this data set can be opened directly with a text program such as Notepad. Except for the head file, the body content is numerically characterized by the freeze/thaw status of the surface soil: 1 for frozen, 2 for thawed, 3 for desert, and 4 for precipitation. If you want to use the icon for display, we recommend using the ArcView + 3D or Spatial Analyst extension module for reading; in the process of reading, a grid format file will be generated, and the displayed grid file is the graphical expression of the ASCII file. The read method comprises the following. [1] Add the 3D or Spatial Analyst extension module to the ArcView software and then create a new View. [2] Activate View, click File menu, and select the Import Data Source option. When the Import Data Source selection box pops up, select ASCII Raster in the Select import file type box. When the dialog box for selecting the source ASCII file automatically pops up, click to find any ASCII file in the data set, and then press OK. [3] Type the name of the Grid file in the Output Grid dialog box (it is recommended that a meaningful file name is used for later viewing) and click the path to store the Grid file, press OK again, and then press Yes (to select integer data) and Yes (to put the generated grid file into the current view). The generated files can be edited according to the Grid file standard. This completes the process of displaying an ASCII file into a Grid file. [4] In the batch processing, the ASCIGRID command of ARCINFO can be used to write AML files, and then use the Run command to complete the process in the Grid module: Usage: ASCIIGRID <in_ascii_file> <out_grid> {INT | FLOAT}. The production of this data is supported by the following Natural Science Foundation Projects: Environmental and Ecological Science Data Center of West China (90502010), Land Data Assimilation System of West China (90202014) and Active and Passive Microwave Radiation Transmission Simulation and Radiation Scattering Characteristics of the Frozen Soil (41071226).
LI Xin
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1, 2 and 3 quadrates of the A'rou foci experimental area on Jul. 14, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:31 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Those provide reliable ground data for retrieval and validation of soil moisture from active remote sensing approaches. Observation items included: (1) soil moisture by POGO soil sensor in No. 1, 2 and 3 quadrates; 25 corner points of each subsite were chosen for the soil temperature, soil volumetric moisture, the loss tangent, soil conductivity, and the real part and the imaginary part of soil complex permittivity; (2) the soil temperature by the handheld infrared thermometer 3# and 5# from BNU in No. 1 quadrate, 1# and 4# in No. 2 quadrate, and 2# and 6# in No. 3 quadrate; 25 corner points of each subsite were measured twice by two groups, and time, the maximum, the minimum and the mean value, and the land cover types were all recorded. (3) spectrum of the grassland, the bare land and the stellera by the thermal infrared spectrometer, 102F. The dataset includes ASAR images, preprocessed data of the thermal infrared spectrometer, 102F, the surface temperature and soil moisture synchronizing with Envisat ASAR.
GAO Hongchun, LI Hongxing, LIU Chao, RAN Youhua, REN Huazhong, YU Yingjie
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 dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No.2 quadrate of the A'rou foci experimental area on Oct. 17, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:04 BJT. The quadrate was divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture by ML2X; soil volumetric moisture, soil conductivity, soil temperature, and the real part of soil complex permittivity by WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The dataset of chlorophyll content observations was obtained in the Yingke oasis and Linze grassland foci experimental areas. Observation items included: (1) Chlorophyll content synchronizing with TM in Yingke oasis No. 1, 4 and 5 maize plots on May 20, 2008. (2) Chlorophyll content synchronizing with ASTER and MODIS in Linze grassland foci experimental areas on May 24, 2008. (3) Chlorophyll content synchronizing with ASTER and MODIS in Yingke oasis maize field on May 28, 2008. (4) Chlorophyll content synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner) in Yingke oasis maize field on May 30, 2008. (5) Chlorophyll content synchronizing with OMIS-II in Yingke oasis maize field on Jun. 16, 2008. (6) Chlorophyll content synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner) in Yingke oasis maize field on Jun. 29, 2008. (7) Chlorophyll content synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner) and TM in Yingke oasis maize field on Jul. 7, 2008. (8) Chlorophyll content synchronizing with WiDAS (Wide-angle Infrared Dual-mode line/area Array Scanner) in Yingke oasis maize field on Jul. 11, 2008.
LI Li, XIN Xiaozhou, ZHANG Yang, ZHOU Mengwei
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the A'rou foci experimental area on Oct. 18, 2007 during the pre-observation period. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:17 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners of each subsites. Simultaneous with the satellite overpass, numerous ground data were collected, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture sensor; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Surface roughness was detailed in the "WATER: Surface roughness dataset in the A'rou foci experimental area". Those provide reliable ground data for retrieval and validation of soil moisture and freeze/thaw status from active remote sensing approaches.
BAI Yunjie, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the Biandukou foci experimental area on Oct. 17, 2007 during the pre-observation period. The ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:04 BJT. Both the quadrates were divided into 3×3 subsites, with each one spanning a 30×30 m2 plot. 25 sampling points were chosen, including centers and corners. Simultaneous with the satellite overpass, numerous ground data were collected: the soil temperature , volumetric soil moisture (cm^3/cm^3), soil salinity (s/m), soil conductivity (s/m) by the Hydra probe, the surface radiative temperature by the handheld infrared thermometer, gravimetric soil moisture, volumetric soil moisture, and soil bulk density by drying soil samples from the cutting ring (100cm^3). Meanwhile, vegetation parameters as height, coverage and water content were also observed. Those provide reliable ground data for the development and validation of soil moisture, soil freeze/thaw algorithms and the forward model from active remote sensing approaches.
BAI Yunjie, CAO Yongpan, LI Xin, Wang Weizhen, WANG Xufeng
The dataset of ground truth measurements synchronizing with ALOS PALSAR was obtained in the Linze station foci experimental area on Jul. 10, 2008. The ALOS PALSAR data were in FBS mode and HH polarization combinations, and the overpass time was approximately at 23:39 BJT. Soil moisture (0-5cm) data were measured by the cutting ring method (50cm^3) in LY07 and LY08 quadrates (repeated nine times). The quadrate location information was listed in coordinates.xls and 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.
PAN Xiaoduo, SONG Yi
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in C1, W2 and B2 of the Biandukou foci experimental area on Mar. 14, 2008, from 23:30 on 14 to 1:00 on 15, to be specific. The ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:21 BJT. The wheat stubble land, the deep plowed land and the rape stubble land were chosen for measurements. (1) The surface radiative temperature and the physical temperature were measured by the handheld infrared thermometer. Besides, the land cover type was also recorded. The data can be opened by Microsoft Office. (2) The gravimetric soil moisture (samples from 0-1cm, 1-3cm, 3-5cm, 5-10cm and 10-20cm) was measured by the microwave drying method. (3) The frost depth by the chopstick and the ruler. The soil was considered frozen when it was hard and with ice crystal. The data can be opened by Microsoft Office. Four data files were included, ASAR data, C1, W2 and B2 data.
CHANG Sheng, Fang Qian, QU Ying, LIANG Xingtao, LIU Zhigang, PAN Jinmei, PENG Danqing, REN Huazhong, ZHANG Yongpan, ZHANG Zhiyu, ZHAO Shaojie, Zhao Tianjie, ZHENG Yue, Zhou Ji, LIU Chenzhou, YIN Xiaojun, ZHANG Zhiyu
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng
The dataset of ground truth measurements synchronizing with Terra MISR and MODIS was obtained in sampling plot BG-A of the Binggou watershed foci experimental area on Dec. 10 and Dec. 11, 2007 during the pre-observation period. Observation items included: (1) Snow parameters including the snow surface temperature, the snow-soil interface temperature, the land surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, snow depth by the ruler and the snow grain size by the handheld microscope. (2) Snow density in "WATER: Dataset of snow density measurements in the Binggou watershed foci experimental area on Dec. 6 and Dec. 10, 2007 during the pre-observation period" (3) Snow properties in "WATER: Dataset of snow properties measured by the Snowfork in the Binggou watershed foci experimental area during the pre-observation period" Raw data and pre-processed data including snow parameters synchronizing with Terra MISR and MODIS and the temperature synchronizing with MODIS were archived herein.
LI Xin, WANG Jian, MA Mingguo, Wang Weizhen, CHE Tao, HAO Xiaohua, LI Hongyi, LIANG Ji, BAI Yunjie, WANG Xufeng, WU Yueru, WANG Yang, LUO Lihui, ZHANG Pu, LIU Yan
The data set provided the cloudless Fractional Snow Cover area (FSC) time-series product basing on the MODIS data and covered the Heihe River Basin from January 2010 to December 2013. They also provide the high spatial (500 m) and temporal (1 day) resolution. Firstly, the end-member were automatically extracted by the fast autonomous spectral end-member determination (N-FINDR) maximizing volume iteration algorithm. Combining N-FINDR with the orthogonal subspace projection (OSP) approach, we propose an improved end-member extraction algorithm using a maximizing, volume-based iterative method. All the 6 end-members were extracted including snow, soil, water, bare land, vegetation, and cloud, respectively. Then, the 10-day spectral library time series based on prior knowledge of Heihe basin are built for 2009. The primary data were produced using the fully constrained least squares (FCLS) linear spectral mixture analysis method by the spectral library. Finally，the cubic spline interpolation algorithm were used to the eliminate the cloud pixels completely and obtain the data set. The data are validated by the fractional snow cover derived from Landsat imagery and the results indicate that the improved algorithm can obtain the end-member information accurately, and the retrieved fractional snow cover has better accuracy than the MODIS fractional snow-cover product (MOD10A1). So the data set can provide more accurate input for the hydrology and climate model.
HUANG Xiaodong, ZHANG Ying, TANG Zhiguang, LI Xin
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
The “Long-term series of daily global snow depth” was produced using the passive microwave remote sensing data. The temporal range is 1980~2018, and the coverage is the global land. The spatial resolutions is 25,067.53 m and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file is a 1383*586 snow depth matrix, and each snow depth represents a 25,067.53m* 25,067.53m grid. The projection of this data is EASE-Grid, and following is the file header which describes the projection detail. File header: ncols 1383 nrows 586 xllcorner -17334193.54 yllcorner -7344787.75 cellsize 25,067.53 NODATA_value -1
CHE Tao, LI Xin, DAI Liyun
This data set uses SMMR (1979-1987), SSM / I (1987-2009) and ssmis (2009-2015) daily brightness temperature data, which is generated by double index (TB V, SG) freeze-thaw discrimination algorithm. The classification results include four types: frozen surface, melted surface, desert and water body. The data covers the source area of three rivers, with a spatial resolution of 25.067525 km. It is stored in geotif format in the form of ease grid projection. Pixel values represent the state of freezing and thawing: 1 for freezing, 2 for thawing, 3 for deserts, 4 for water bodies. Because all TIF files in the dataset describe the scope of Sanjiangyuan National Park, the row and column number information of these files is unchanged, and the excerpt is as follows (where the unit of cellsize is m): ncols 52 nrows 28 cellsize 25067.525 nodata_value 0
High Asia is very sensitive to climate change, and is a hot area of global change research. The changes of temperature and precipitation will be reflected in the freezing and thawing time of ice and snow. Satellite microwave remote sensing can provide continuous monitoring ability of ice and snow surface state in time and space. When a small part of ice and snow begins to melt, micro liquid water will also be reflected in active and passive microwave remote sensing signals. In the microwave band, the dielectric constant of ice and liquid water is very different, so it provides a basic theory for the microwave remote sensing monitoring of ice and snow melting. In the case of passive microwave, when ice and snow begin to melt and liquid water appears, its absorption and emissivity increase rapidly, so its emissivity, brightness temperature and backscatter coefficient will also change rapidly. This data set is the initial time of ice and snow melting in the high Asia region retrieved by using the satellite microwave radiometer and scatterometer observations from 1979 to 2018. The passive microwave remote sensing data are SMMR on satellite (1979-1987) and SSM / i-ssmis radiometer on DMSP (1988 present). The active microwave remote sensing data is the QuikSCAT satellite scatterometer (2000-2009).
Xiong Chuan, SHI Jiancheng, YAO Ruzhen, LEI Yonghui, PAN Jinmei
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
This dataset was derived from long-term daily snow depth in China based on the boundary of the three-river-source area. The snow depth ranges from 0 to 100 cm, and the temporal coverage is from January 1 1980 to December 31 2020. The spatial and temporal resolutions are 0.25o and daily, respectively. Snow depth was produced from satellite passive microwave remote sensing data which came from three different sensors that are SMMR, SSM/I and SSMI/S. Considering the systematic bias among these sensors, the inter-sensor calibrations were performed to obtain temporal consistent passive microwave remote sensing data. And the long-term daily snow depth in China were produced from this consistent data based on the spectral gradient method.For header file information, refer to the data set header.txt.
DAI Liyun
The global Cryosat-2 GDR dataset is generated by the European Space Agency (ESA); it has a temporal coverage from 2010 to 2016 and covers the globe. On April 8, 2010, the ESA launched the Cryosat-2 high-tilt polar orbit satellite. The satellite is equipped with an SAR Interferometer Radar Altimeter (SIRAL), which is mainly used to monitor polar ice thickness and sea ice thickness changes, and, furthermore, to study the effects of melting polar ice on global sea level rise and that of global climate change on Antarctic ice thickness. The altimeter operates in the Ku-band and at a frequency of 13.575 GHz, it includes three measurement modes. One is a low-resolution altimeter measurement mode (LRM) that points to the subsatellite point to obtain all surface observations for land, sea, and ice sheets; its processing is similar to ENVISAT/RA-2, with an orbital resolution of 5 to 7 km. The second is the Synthetic Aperture Radar (SAR) measurement mode, which is mainly used to improve the accuracy and resolution of sea ice observations; it can make the resolution along the orbit reach approximately 250 m. The third is the Interferometric Synthetic Aperture Radar (InSAR), which is mainly used to improve the accuracy of areas with complex terrain such as the edges of ice sheets or ice shelves. The CryoSat -2/SIRAL data products mainly include 0-level data, 1b-level data, 2-level data and high-level data. The Cryosat-2/SIRAL products consist of two files: an XML head file (.HDR) and a data product file (.DBL). The HDR file is an auxiliary ASCII file for fast identification and retrieval of the data files. 1b-level products are stored separately according to the measurement modes, and the data recording formats of different modes are also different. Each waveform in LRM mode and SAR mode has 128 sampling points, while that in SARIn mode has 512 sampling points. 2-level GDR products are available for most scientific applications, including measurement time, geographic location, altitude, and more. In addition, the altitude information in GDR products has been obtained through instrumental calibration, transmission delay corrections, geometric corrections, and geophysical corrections (such as atmospheric corrections and tidal corrections). The GDR products are single global full-track data, that is, the measurement results of the three modes. After different processing, they are combined in chronological order; thereby, the data recording formats are unified. The data in the three modes use different waveform retracking algorithms to obtain altitude values. In the latest updated Baseline C data, the LRM mode data use three algorithms: Refined CFI, UCL and Refined OCOG.
SHEN Guozhuang, FU Wenxue
Microwave emissivity of the surface characterization of the object to launch the ability of microwave radiation, spaceborne passive microwave emissivity can on macro, large scale integral expression of epicontinental microwave radiation is a passive microwave surface parameters in quantitative inversion experience for one of the important basic data, is also on the large scale understand epicontinental microwave radiation in a way.This data set is considered to carry on the Aqua satellite advanced microwave scanning radiometer (amsr-e) and moderate resolution imaging spectroradiometer (MODIS) synchronous observation characteristics, using the MODIS land surface temperature and atmospheric water vapor data as input, by considering the effects of atmospheric emissivity estimation model, produced a global sky conditions during the running of amsr-e sensor (June 2002 ~ October 2011) of the epicontinental multichannel bipolar microwave instantaneous emission rate.Through product low-frequency radio signal, data alignment, statistic analysis, the different emissivity characteristics of surface coverage condition, frequency dependence and correlation studies conducted confirmatory analysis, the results show that the instantaneous dynamic details of emissivity is rich, standard deviation within 0.02 month daily variation, the change of time and space, frequency dependent on and related to the understanding of the natural physical process. This data set includes amsr-e global land surface daily, daily, daily, monthly and monthly products in the whole life cycle, which can be used to carry out satellite based passive microwave remote sensing simulation, land surface model, and inversion research of land surface temperature, snow cover, atmospheric precipitation/moisture/precipitation.The projection coordinates of the data adopt the standard EASE-GRID projection, and the data storage method is binary floating point lattice (the size of the matrix is 1383*586). After the data is obtained, ENVI/IDL and other software or the corresponding program code can be read in the form of binary files. All land surface emissivity data produced are named according to the following rules: RADI_AMSRE_EM # # # # _yyymmdd_EG_V. Bin For example, file name: RADI_AMSRE_EM01_20060101_EG_V# EM##: 01 means daily, 05 means 5 days, 10 means ten days, HM means half a month, MO means a month Yyyymmdd: yyyy means year, mm means month, and dd means date V##: version number, such as 0.1, 1.0, etc., the units digit is the official version RADI: institute of remote sensing and digital earth, Chinese academy of sciences AMSRE: advanced microwave scanning radiometer
QIU Yubao
The parameter inversion study project of soil moisture and snow water equivalent on the Tibetan Plateau in the past 20 years is part of the key research plan of Environmental and Ecological Science for West China of the National Natural Science Foundation of China. The person in charge is Jiancheng Shi, a researcher at the Institute of Remote Sensing Applications of the Chinese Academy of Sciences. The project ran from January 2004 to December 2007. The data collection of the project: the Monthly MODIS Snow Cover Product of Tibetan Plateau (2001-2005). Based on the image data acquired by MODIS, combined with ASTER image data, the data set carried out snow cover area classification and change analysis at a subpixel level on the Tibetan Plateau. The research mainly focused on studying the subpixel snow cover area classification algorithm, including the statistical regression method and the mixed-pixel decomposition method using the normalized snow index. In the mixed-pixel decomposition, a linear mixed model was adopted, and snow and non-snow end members were automatically extracted using the normalized snow index and the normalized vegetation index. On the basis of the subpixel snow cover area classification algorithm, the snow cover area variation on the Tibetan Plateau was analyzed. Using the method of establishing a decision tree, clouds and snow were detected, cloud-removal was performed, and the subpixel of the Tibetan Plateau was formed by synthesis and mosaicking of the time series images. The snow cover area classification database analyzes and describes the spatial distribution and variation characteristics of the snow cover area of the Tibetan Plateau.
SHI Jiancheng, XU Lina
Snow duration on the Tibetan Plateau changes relatively quickly, and the mountainous areas around the plateau are characterized by abundant snow and ice resources and active atmospheric convection. Optical remote sensing is often affected by clouds. Snow cover monitoring needs to consider the cloud-removal problem on a daily time scale. Taking full account of the terrain of the Tibetan Plateau and the characteristics of snow on the mountains, this data set adopted a combination of various cloud-removing processes and steps to gradually remove the daily snow cover by maintaining the cloud-classify accuracy of the snow cover. In addition, a step-by-step comprehensive classification algorithm was formed, and the “MODIS daily cloud-free snow cover product over the Tibetan Plateau (2002-2015)” was completed. Two snow seasons from October 1, 2009, to April 30, 2011, were selected as test data for algorithm research and accuracy verification, and the snow depth data provided by 145 ground stations in the study area were used as a ground reference. The results showed that in the plateau region, when the snow depth exceeds 3 cm, the total classification accuracy of the cloud-free snow cover products is 96.6%, and the snow cover classification accuracy is 89.0%. The whole algorithm procedure, based on WGS84 projected MODIS snow products (MOD10A1 and MYD10A1) with medium resolution, results in a small loss of cloud-removal accuracy, which made the data highly reliable.
QIU Yubao
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 long-term evolution of lakes on the Tibetan Plateau (TP) could be observed from Landsat series of satellite data since the 1970s. However, the seasonal cycles of lakes on the TP have received little attention due to high cloud contamination of the commonly-used optical images. In this study, for the first time, the seasonal cycle of lakes on the TP were detected using Sentinel-1 Synthetic Aperture Radar (SAR) data with a high repeat cycle. A total of approximately 6000 Level-1 scenes were obtained that covered all large lakes (> 50 km2) in the study area. The images were extracted from stripmap (SM) and interferometric wide swath (IW) modes that had a pixel spacing of 40 m in the range and azimuth directions. The lake boundaries extracted from Sentinel-1 data using the algorithm developed in this study were in good agreement with in-situ measurements of lake shoreline, lake outlines delineated from the corresponding Landsat images in 2015 and lake levels for Qinghai Lake. Upon analysis, it was found that the seasonal cycles of lakes exhibited drastically different patterns across the TP. For example, large size lakes (> 100 km2) reached their peaks in August−September while lakes with areas of 50−100 km2 reached their peaks in early June−July. The peaks of seasonal cycles for endorheic lakes were more pronounced than those for exorheic lakes with flat peaks, and glacier-fed lakes with additional supplies of water exhibited delayed peaks in their seasonal cycles relative to those of non-glacier-fed lakes. Large-scale atmospheric circulation systems, such as the westerlies, Indian summer monsoon, transition in between, and East Asian summer monsoon, were also found to affect the seasonal cycles of lakes. The results of this study suggest that Sentinel-1 SAR data are a powerful tool that can be used to fill gaps in intra-annual lake observations.
ZHANG Yu, ZHANG Guoqing
The proportion data set of daily cloudless MODIS snow cover area in babaohe river basin (2008.1.1-2014.6.1) was obtained after cloud removal processing using a cloud removal algorithm based on cubic spline function interpolation on the basis of daily cloudless MODIS snow cover product-mod10a1 (tang zhiguang, 2013). This data set adopts the projection method of UTM (horizontal axis isometric cutting cylinder), with a spatial resolution of 500m, and provides Daily Snow Albedo daily-sad results for the babao river basin.The data set is a daily file from January 1, 2008 to June 1, 2014.Each file is the snow albedo result of the day, with a value of 0-100 (%), is the ENVI standard file, and the naming rule is: mod10a1.ayyyyddd_h25v05_snow_sad_grid_2d_reproj_babaohe_nocloud.img, where YYYY represents the year, DDD stands for Julian day (001-365/366).The file can be opened directly with ENVI or ARCMAP software. The original MODIS snow cover data products processed by declouding are derived from MOD10A1 products processed by the us national snow and ice data center (NSIDC). This data set is in HDF format and USES sinusoidal projection. The attributes of the cloud-free MODIS albedo data set (2008.1.1-2014.1.1) in babaohe river basin are composed of the spatial and temporal resolution, projection information and data format of the dataset.
WANG Jian, PAN Haizhu
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
Surface soil moisture (SSM) is a crucial parameter for understanding the hydrological process of our earth surface. Passive microwave (PM) technique has long been the primary choice for estimating SSM at satellite remote sensing scales, while on the other hand, the coarse resolution (usually >~10 km) of PM observations hampers its applications at finer scales. Although quantitative studies have been proposed for downscaling satellite PM-based SSM, very few products have been available to public that meet the qualification of 1-km resolution and daily revisit cycles under all-weather conditions. In this study, therefore, we have developed one such SSM product in China with all these characteristics. The product was generated through downscaling of AMSR-E and AMSR-2 based SSM at 36-km, covering all on-orbit time of the two radiometers during 2003-2019. MODIS optical reflectance data and daily thermal infrared land surface temperature (LST) that have been gap-filled for cloudy conditions were the primary data inputs of the downscaling model, in order to achieve the “all-weather” quality for the SSM downscaling outcome. Daily images from this developed SSM product have achieved quasi-complete coverage over the country during April-September. For other months, the national coverage percentage of the developed product is also greatly improved against the original daily PM observations. We evaluated the product against in situ soil moisture measurements from over 2000 professional meteorological and soil moisture observation stations, and found the accuracy of the product is stable for all weathers from clear sky to cloudy conditions, with station averages of the unbiased RMSE ranging from 0.053 vol to 0.056 vol. Moreover, the evaluation results also show that the developed product distinctly outperforms the widely known SMAP-Sentinel (Active-Passive microwave) combined SSM product at 1-km resolution. This indicates potential important benefits that can be brought by our developed product, on improvement of futural investigations related to hydrological processes, agricultural industry, water resource and environment management.
SONG Peilin, ZHANG Yongqiang
The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E, AMSR2 passive microwave brightness temperature data and MODIS optical remote sensing data. The freeze-thaw discriminant function algorithm and downscaling algorithm are used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.05° from 2002 to 2017. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.
Zhao Tianjie, ZHANG Ziqian
The freeze/thaw status of the near-surface soil is the water-ice phase transition that occurred at the top soil layer. It is an important indicator as a giant on-off “switch” of the land surface processes including water, energy, and carbon exchanges between the land surface and atmosphere. The freeze/thaw status is an essential variable for understanding how the ecosystem responds to and affects global changes. This dataset is based on the AMSR-E and AMSR2 passive microwave brightness temperature data, and the freeze-thaw discriminant function algorithm is used to generate the global near-surface soil freeze-thaw status with a spatial resolution of grids at 0.25° from 2002 to 2019. The dataset can be used for the analysis of the spatial distribution and trend changes of global freeze-thaw cycles, such as the freeze/thaw onset dates and duration. It provides data support for understanding the interaction mechanism between the land surface freeze-thaw cycle and the land-atmosphere exchanges under the context of global changes.
Zhao Tianjie
This dataset is the snow cover dataset based on the MODIS fractional snow cover mapping algorithm Coupled Regional Approach (CRA). The CRA algorithm mainly consists of three parts. (1) First, the N-FINDR (Volume Iterative Approach) and OSP (Orthogonal Subspace Projection) are used to automatically extract the endmember according to the settings (extracting 30 end endmembers). (2) On the basis of automatic extraction, combined with the IGBG land cover type map, six types of endmembers of snow, vegetation, cloud, soil, rock and water are selected by the manual screening method, and an annual spectrum database is established according to the 2009 image. There are 3 spectra in the early, middle and late months and 36 spectra a year. (3) The established spectral database is used as a priori knowledge, and based on prior knowledge, the fully constrained linear unmixing method (FCLS) for subpixel decomposition is used to obtain the fractional snow cover products. The NDSI ratio algorithm with improved topographic effect is used to obtain the snow cover area, the spatiotemporal data are then interpolated, and, finally, the multisource data fusion with the AMSR-E microwave snow depth product is undertaken. The dataset adopts a latitude and longitude (Geographic) projection method. The datum is WGS84, and the spatial resolution is 0.005°. It provides the daily cloudless snow cover area map of the Tibetan Plateau from 2008 to 2010. The data set is stored by year and consists of 3 folders from 2008 to 2010. Each folder contains the classification results of the daily snow cover of the current year. It is a tif file with the naming rule YYYY***.tif, in which YYYY represents the year (2008-2010), and *** represents the day (001~365/ 366). It can be opened directly with ARCGIS or ENVI.
HAO Xiaohua
The “long-term series of daily snow depth in Eurasia” was produced using the passive microwave remote sensing data. The temporal range is 1980~2016, and the coverage is the Eurasia continent. The spatial resolutions is 0.25° and the temporal resolution is daily. A dynamic brightness temperature gradient algorithm was used to derive snow depth. In this algorithm, the spatial and temporal variations of snow characteristics were considered and the spatial and seasonal dynamic relationships between the temperature difference between 18 GHz and 36 GHz and the measured snow depth were established. The long-term sequence of satellite-borne passive microwave brightness temperature data used to derive snow depth came from three sensors (SMMR, SSM/I and SSMI/S), and there is a certain system inconsistency among them. So, the inter-sensor calibration was performed to improve the temporal consistency of these brightness temperature data before snow depth derivation. The accuracy analysis shows that the relative deviation of Eurasia snow depth data is within 30%. The data are stored as a txt file every day, each file includes a file header (projection mode) and a 720*332 snow depth matrix, and each snow depth represents a 0.25°*0.25° grid. For details of the data, please refer to data specification “Snow depth dataset of Eurasian (Version 1.0) (1980-2016).doc”
CHE Tao, DAI Liyun
The High Asia region is an area sensitive to global changes in mid-latitude regions and is a hotspot for research. The lakes in the territory are scattered, and the lake freeze-thaw process is one of the key factors sensitive to global change. Due to the large difference in the dielectric constant between ice and water, satellite-borne passive microwave remote sensing is weather insensitive and has a high revisiting rate; thus, it can achieve rapid monitoring of the freeze-thaw state of lakes. According to the area ratio of the lake and the land surface in the sub-pixels of passive microwave radiometer data, this data set represents the lake brightness temperature information of the pixel (sub-pixel level) by applying the hybrid pixel decomposition method in order to monitor the lake freeze-thaw process in the High Asia region. Thus, by adopting a variety of passive microwave data, time series of lake brightness temperature and freeze-thaw status were obtained for a total of 51 medium to large lakes from 2002 to 2016 in the High Asia region. Using cloudless MODIS optical products as validation data, three lakes of different sizes in different regions of High Asia, i.e., Hoh Xil Lake, Dagze Co Lake, and Kusai Lake, were selected for freeze-thaw detection validation. The results indicated that the lake freeze-thaw parameters obtained by microwave and optical remote sensing were highly consistent, and the correlation coefficients reached 0.968 and 0.987. This data set contained the time series brightness temperature of lakes and the freeze-thaw parameters of lake ice, which could be used to further invert the characteristic parameters of lakes and enhance the understanding of lake ice freezing and thawing in the High Asia region. This database will be useful in the assessment of climatic and environmental changes in the High Asia region and in global climatic change response models. The data set consists of two parts: the passive microwave remote sensing brightness temperature data set of 51 lakes in the High Asia region from 2002 to 2016, with an observation interval of 1 to 2 days, and the lake ice freeze-thaw data set obtained by estimation of the lake brightness temperature. The files are the lake brightness temperature data via the nearest neighbour method and pixel decomposition in the form of a .zip file (12 MB) and the lake freeze-thaw data set for 51 lakes in the High Asia region from 2002 to 2016 in the form of an .xls file (0.1 MB).
QIU Yubao
This data set provides daily snow thickness distribution data of China from October 24, 1978 to December 31, 2012, with a spatial resolution of 25km.The original data used for the inversion of the snow depth data set came from SMMR (1978-1987), SSM/I (1987-2008) and amsr-e (2002-2012) daily passive microwave bright temperature data processed by the national snow and ice data center (NSIDC).As the three sensors are mounted on different platforms, there is a certain system inconsistency in the obtained data.The time consistency of bright temperature data is improved by cross calibration of bright temperature of different sensors.Then, based on Chang algorithm, Dr. Che tao is used to carry out snow depth inversion.Refer to the data description document for specific inversion methods.
CHE Tao, LI Xin, DAI Liyun
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
This dataset mainly includes the twice a day (ascending-descending orbit) brightness temperature (K) of the space-borne microwave radiometers SSM / I and SSMIS carried by the US Defense Meteorological Satellite Program satellites (DMSP-F08, DMSP-F11, DMSP-F13, and DMSP-F17), time coverage from September 15, 1987 to December 31, 2015. The SSM/I brightness temperature of DMSP-F08, DMSP-F11 and DMSP-F13 include 7 channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 85.50H and 85.50V; The SSMIS brightness temperature observation of DMSP-F17 consists of seven channels: 19.35H, 19.35V, 22.24V, 37.05H, 37.05V, 91.66H and 91.66v. Among them, DMSP-F08 satellite brightness temperature coverage time is from September 15, 1987 to December 31, 1991; DMSP-F11 satellite brightness temperature coverage time is from January 1, 1992 to December 31, 1995; The coverage time of DMSP-F13 satellite brightness temperature is from January 1, 1996 to April 29, 2009; The coverage time of DMSP-F17 satellite brightness temperature is from January 1, 2009 to December 31, 2015. 1. File format and naming: The brightness temperature is stored separately in units of years, and each directory is composed of remote sensing data files of each frequency, and the SSMIS data also contains the .TIM time information file. The data file names and their naming rules are as follows: EASE-Fnn-ML / HyyyydddA / D.subset.ccH / V (remote sensing data) EASE-Fnn-ML / HyyyydddA / D.subset.TIM (time information file) Among them: EASE stands for EASE-Grid projection method; Fnn stands for satellite number (F08, F11, F13, F17); ML / H stands for multi-channel low-resolution and multi-channel high-resolution respectively; yyyy represents the year; ddd represents Julian Day of the year (1-365 / 366); A / D stands for ascending (A) and descending (D) respectively; subset represents brightness temperature data in China; cc represents frequency (19.35GHz, 22.24 GHz, 37.05GHz, (85.50GHz, 91.66GHz); H / V stands for horizontal polarization (H) and vertical polarization (V), respectively. 2. Coordinate system and projection: The projection method of this data set is EASE-Grid, which is an equal area secant cylindrical projection, and the double standard parallels are 30 ° north and south. For more information about EASE-GRID, please refer to http://www.ncgia.ucsb.edu/globalgrids-book/ease_grid/. If you need to convert the EASE-Grid projection to Geographic projection, please refer to the ease2geo.prj file, the content is as follows: Input projection cylindrical units meters parameters 6371228 6371228 1 / * Enter projection type (1, 2, or 3) 0 00 00 / * Longitude of central meridian 30 00 00 / * Latitude of standard parallel Output Projection GEOGRAPHIC Spheroid KRASovsky Units dd parameters end 3. Data format: Stored as integer binary, Row number: 308 *166，each data occupies 2 bytes. The actual data stored in this dataset is the brightness temperature * 10. After reading the data, you need to divide by 10 to get the real brightness temperature. 4. Data resolution: Spatial resolution: 25.067525km, 12.5km (SSM / I 85GHz, SSMIS 91GHz) Time resolution: daily, from 1978 to 2015. 5. Spatial range: Longitude: 60.1 ° -140.0 ° east longitude; Latitude: 14.9 ° -55.0 ° north latitude. 6. Data reading: Remote sensing image data files in each set of data can be opened in ArcMap, ENVI and ERDAS software.
National Snow and Ice Data Center（NSIDC）
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
River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.
QIU Yubao
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
On the basis of RGI6.0, we use remote sensing and geographic information system technology to update the glacier inventory data in Alaska. The updated glacier inventory uses a data source for 2018 Landsat OLI spatial resolution 15m remote sensing image, and the method used is manual interpretation. The results show that the Alaska Glacier inventory includes 27043 glaciers with a total area of 81285km2. The uncertiany of this data is 4.3%. The data will provide important data support for the study of glacier change in Alaska and the regional and global impact of glacier change in the context of global change.
SHANGGUAN Donghui,
There are many lakes in the Qinghai Tibet Plateau. The glacial phenology and duration of lakes in this region are very sensitive to regional and global climate change, so they are used as the key indicators of climate change research, especially the comparative study of the three polar environmental changes of the earth. However, due to its poor natural environment and sparse population, there is a lack of conventional field measurement of lake ice phenology. The lake ice was monitored with a resolution of 500 meters by using the normalized difference snow index (NDSI) data of MODIS. The traditional snow map algorithm is used to detect the lake daily ice amount and coverage under the condition of sunny days, and the lake daily ice amount and coverage under the condition of cloud cover are re determined through a series of steps based on the spatiotemporal continuity of the lake surface conditions. Through time series analysis, 308 lakes larger than 3km2 are identified as effective records of lake ice range and coverage, forming a daily lake ice range and coverage data set, including 216 lakes.
QIU Yubao
The variation in the duration of snow on the Tibetan Plateau is relatively great, and the high mountainous areas around the plateau are rich in snow and ice resources. Taking full account of the terrain of the Tibetan Plateau and the snow characteristics in the mountains, the data set adopted AVHRR data to gradually realize generating data products for daily, ten-day, and monthly snow cover areas while maintaining the snow classification accuracy. These data included the daily/10-day/monthly snow cover area data for the Tibetan Plateau from 2007 to 2015, the average accuracy of which is 0.92. It can provide reliable data for snow changes during the historical periods of the Tibetan Plateau.
QIU Yubao
The extraction of glacier surface movement is of great significance in the study of glacier dynamics and material balance changes. In view of the shortcomings of the current application of autonomous remote sensing satellite data in glacier movement monitoring in China, the SAR data covering typical glaciers in alpine areas of the Qinghai Tibet Plateau from 2019 to 2020 obtained under the GF-3 satellite FSI mode was used to obtain the glacier surface velocity distribution in the study area with the help of a parallel offset tracking algorithm. With its good spatial resolution, GF-3 image has significant advantages in extracting glacier movement with small scale and slow movement, and can better reflect the details and differences of glacier movement. This study is helpful to analyze the movement law and spatio-temporal evolution characteristics of glaciers in the Qinghai Tibet Plateau under the background of climate change.
YAN Shiyong
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