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
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
The microwave radiometer data set comprises brightness temperature data from SMMR (1978-1987), SSM/I (1987-2009) and SSMIS (2009-2015), with temporal coverage from 1978 to 2015 and a spatial resolution of 25 km. Each Antarctic data file consists of 316*332 grids, and each Arctic freeze-thaw data file consists of 304*448 grids. The microwave scatterometer data set comprises backscattering data from QScat (2000-2009) and ASCAT (2009-2015), with a temporal coverage from 2000 to 2015 and a spatial resolution of 4.45 km. Each Antarctic data file consists of 1940*1940 grids, and each Arctic data file consists of 810*680 grids. The temporal resolution of the data set is one day, and the data cover both Antarctica and Arctic ice sheets.
Li Xinwu, Liang Lei
Using the Modis1B data of 11 scenes from 2003 to 2013 (the ice shelf Modis1B data published on the NSIDC website), the surface velocity of the Antarctic Amery Ice Shelf was extracted by the subpixel cross-correlation method, the ice velocity was extracted by the COSI-Corr software, and then the time sequence of annual average velocities for nearly ten years was obtained. Due to the lack of field observations in the study area, the accuracy of the ice flow results was estimated by using the offset value of the stable region, and the ice flow error was approximately ±50 m/year. The ice velocity data date from 2003 to 2013, the temporal resolution is one year, and the data cover the Amery area with a spatial resolution of 500 m. A GeoTIFF file of velocity data is stored every year. For details regarding the data, please refer to the Amery Ice Flow Field - Data Description.
JIANG Liming
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
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
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
This dataset mainly includes the passive microwave brightness temperature obtained from the Scanning Multichannel Microwave Radiometer (SMMR) carried by the Nimbus-7 satellite, including 06H, 06V, 10H, 10V, 18H, 18V, 21H, 21V, 37H, 37V, a total of ten microwave channels with two transits (ascending & descending) brightness temperature per day from October 25, 1978 to August 20, 1987, where H represents horizontal polarization and V represents vertical polarization. Nimbus-7, launched in October 1978, is a solar-synchronous polar-orbiting satellite. The microwave sensor SMMR is a dual-polarization microwave radiometer that measures the brightness temperature of five frequencies (6.6GHz, 10.69GHz, 18.0GHz, 21.0GHz, 37.0GHz) on the surface. It scans the surface at a fixed incident angle of about 50.3 °, with a width of 780 km, and passes through the equator at noon 12:00 (ascending orbit) and 24:00 (descending orbit). The time resolution of SMMR is daily, but due to the wide distance between swaths, the same surface will be revisited every 5-6 days. 1. File format and naming: Each set of data is composed of remote sensing data files. The name and naming rules of each group of data files in the SMMR_Grid_China directory are as follows: SMMR-MLyyyydddA / D.subset.ccH / V (remote sensing data) Among them: SMMR stands for SMMR sensor; ML stands for multi-channel low resolution; yyyy stands for year; ddd stands for Julian Day of the year (1-365 / 366); A / D stands for ascending (A) and derailing (D ); subset represents the brightness temperature data in China; cc represents the frequency (6.6GHz, 10.69GHz, 18.0GHz, 21.0GHz, 37.0GHz); 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 parallels are 30 degrees 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, 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. Spatial resolution: 25km; Time resolution: daily, from 1978 to 1987. 4. Spatial range: Longitude: 60.1 ° -140.0 ° East longitude; Latitude: 14.9 ° -55.0 ° north latitude. 5. Data reading Remote sensing image data files for each set of data can be opened in ENVI and ERDAS software.
NSIDC
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）
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 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 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 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
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 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 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 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
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 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 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
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