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
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 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 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
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
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
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 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
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