The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in No. 1 and 2 quadrates of the E'bao foci experimental area on Oct. 17, 2007 during the pre-observation period The 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, soil volumetric moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by the WET soil moisture tachometer; the surface radiative temperature by the hand-held infrared thermometer; soil gravimetric moisture, volumetric moisture, and soil bulk density by drying soil samples from the cutting ring. Meanwhile, vegetation parameters as height, coverage and water content were also observed. Meanwhile, vegetation parameters as height, coverage and water content were also observed. Those provide reliable ground data for retrieval and verification of soil moisture, soil freeze/thaw status and the microwave radiative transfer model from active remote sensing approaches.
CHAO Zhenhua, CHE Tao, QIN Chun, WU Yueru
The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in No. 2 and 3 quadrates of the A'rou foci experimental area on Mar. 14, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 23:21 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Only the corner points of each subsite were chosen for observations. Those provide reliable ground data for retrieval and verification of soil moisture from active remote sensing approaches. 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".
CAO Yongpan, GU Juan, LI Xin, LI Zhe, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, ZHU Shijie, CHANG Cun
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product in 2015. The data uses the characteristics of China's domestic FY-3 satellite data with high time resolution (1 day) and spatial resolution (1km) to construct a multi angle observation data set. Based on the analysis of the multi-source data set and the existing composite vegetation index products and algorithms A global synthetic vegetation index product algorithm system based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
The 1km / 5day vegetation index (NDVI / EVI) data set of Heihe River basin provides a 5-day resolution NDVI / EVI composite product from 2011 to 2014. The data uses the characteristics of FY-3 data, a domestic satellite, with high time resolution (1 day) and spatial resolution (1km), to construct a multi angle observation data set, which is the basis for analyzing multi-source data sets and existing composite vegetation index products and algorithms On the basis of this, an algorithm system of global composite vegetation index production based on multi-source data set is proposed. The vegetation index synthesis algorithm of MODIS is basically adopted, that is, the algorithm system of BRDF angle normalization method, cv-mvc method and MVC method based on the semi empirical walthal model. Using the algorithm system, the composite vegetation index is calculated for the first level data and the second level data, and the quality is identified. Multi-source data sets can provide more angles and more observations than a single sensor in a limited time. However, due to the difference of on orbit running time and performance of sensors, the observation quality of multi-source data sets is uneven. Therefore, in order to make more effective use of multi-source data sets, the algorithm system first classifies the quality of multi-source data sets, which can be divided into primary data, secondary data and tertiary data according to the observation rationality. The third level data are observations polluted by thin clouds and are not used for calculation. In the middle reaches of Heihe River, the verification results of farmland and forest areas show that the NDVI / EVI composite results of combined multi temporal and multi angle observation data are in good agreement with the ground measured data (RMSE = 0.105). Compared with the time series of MODIS mod13a2 product, it fully shows that when the time resolution is increased from 16 days to 5 days, a stable and high-precision vegetation index can describe the details of vegetation growth in detail. In a word, the NDVI / EVI data set of Heihe River Basin, which is 1km / 5day, comprehensively uses multi temporal and multi angle observation data to improve the estimation accuracy and time resolution of parameter products and better serves the application of remote sensing data products.
LI Jing, LIU Qinhuo, ZHONG Bo, YANG Aixia
30m month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data constructs multi-angle observation data sets by using China's domestic satellite HJ/CCD data with high temporal resolution (2 days after networking) and spatial resolution (30m) , and divides the country into different vegetation divisions and land types. The conversion coefficients of NDVI and FVC are calculated respectively, and use the calculated conversion coefficient lookup table and monthly compositing NDVI to produce the regional monthly compositing FVC products. The 30m month compositing FVC product in the Heihe River Basin can directly obtain the vegetation coverage ratio through high-resolution data, and mitigate the influence of low-resolution data heterogeneity; in addition, selecting the typical period of vegetation growth change, by fitting the vegetation index of each pixel time series to obtain the growth curve parameters that correspond to each pixel; then the land use map and the vegetation classification map are combined to find the representative uniform pixels of the region for training the conversion coefficients of the vegetation index. Compared with the ASTER reference FVC results, the 30m/month compositing FVC product in the Heihe River Basin is slightly higher than the ASTER reference result, but the overall deviation is not large, and the maximum value of the root mean square error (RMSE) of the product and the reference value is less than 0.175. In addition, compared with the ground survey data of Huailai experimental site in Hebei Province, the 30 m/month compositing FVC products generally reflect the seasonal variation of vegetation growth, and the deviation from the ground survey data is less than 0.1. At the same time, compared with the ground measurements of vegetation coverage in many watersheds in Northeast, North China and Southeast China, the overall error between the compositing FVC products and the ground measurements is less than 0.2. In all, the 30m/month compositing FVC data set of Heihe River Basin comprehensively utilizes multi-temporal and multi-angle remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products, so as to better serve the application of remote sensing data products.
MU Xihan, RUAN Gaiyan, ZHONG Bo, WU Junjun, WU Shanlong, LIU Qinhuo
The dataset of ground-based microwave scatterometer and snow parameter observations was obtained in the Binggou watershed experimental area on Mar. 16, 2008. Observation items included: (1) Snow backscattering coefficient by the scatterometer (2) Snow parameters as the snow surface temperature by the probe thermometer, snow grain size by the handheld microscope, snow density by the snow shovel, the snow surface temperature and the snow-soil interface temperature by the handheld infrared thermometer in BG-I. (3) The snow spectrum by the portable ASD (Xinjiang Meteorological Administration) at the Dadongshu mountain pass; the major and minor axis and shape of the snow layer grain through the snow sieve. (4) Snow albedo by the total radiometer from 10:29 to 15:00 (5) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the Snowfork at the Dadongshu mountain pass Two subfolders including raw data and preprocessed data were archived.
LIU Zengcan, LIU Zengcan, QIN Wei, SHU Lele, WANG Xufeng, XU Zhen, ZHU Shijie, MA Mingguo, CHANG Cun, DOU Yan, MA Zhongguo, ZHANG Pu, JIANG Tenglong
The aim of the simultaneous observation of river surface temperature is obtaining the river surface temperature of different places, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the river surface temperature data will be used for validation of the retrieved river surface temperature from thermal infrared sensor and the analysis of the scale effect of the river surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation sites and other details Ten river sections were chosen to observe surface temperature simultaneously in the midstream of Heihe River Basin on 3 July and 4 July, 2012, including Sunan Bridge, Binhe new area, Heihe Bridge, Railway Bridge, Wujiang Bridge, Gaoya Hydrologic Station, Banqiao, Pingchuan Bridge, Yi’s Village, Liu’s Bridge. Self-recording point thermometers (observed once every 6 seconds) were used in Railway Bridge and Gaoya Hydrologic Station while handheld infrared thermometers (observed once of the river section temperature for every 15 minutes) were used in other eight places. 2. Instrument parameters and calibration The field of view of the self-recording point thermometer and the handheld infrared thermometer are 10 and 1 degree, respectively. The emissivity of the latter was assumed to be 0.95. All instruments were calibrated on 6 July, 2012 using black body during observation. 3. Data storage All the observation data were stored in excel.
HE Xiaobo, Jia Shuzhen
ASAR (Advanced Synthetic Aperture Radar) is a Synthetic Aperture Radar sensor mounted on ENVISAT satellite. It operates in c-band with a wavelength of 5.6 cm and features multi-polarization, variable observation Angle and wide-range imaging. Heihe river basin of ENVISAT ASAR remote sensing data sets mainly through central Europe "dragon plan" project, the data to the Image mode, cross polarization (Alternating Polarisation) model with wide is given priority to, the spatial resolution of 30 meters. ENVISAT ASAR data 404 scenes are currently available in heihe river basin, including 82 scenes in APP mode, 7 scenes in IMP mode and 315 scenes in WSM mode. The acquisition time is: APP can choose the polarization mode, the time range is from 2007-08-15 to 2007-12-23, 2008-01-02 to 2008-12-20, 2009-02-15 to 2009-09-06; IMP imaging mode, time range from 2009-06-19 to 2009-07-12; WSM wide format, time range from 2005-12-05 to 2005-12-31,2006-01-06 to 2006-12-31, 2007-01-01 to 2007-12-30, 2008-01-01 to 2008-12-28, 2009-03-13 to 2009-05-22. Product level is L1B, without geometric correction, is amplitude data.
European Space Agency
GIMMS (glaobal inventory modelling and mapping studies) NDVI data is the latest global vegetation index change data released by NASA C-J-Tucker and others in November 2003. The dataset includes the global vegetation index changes from 1981 to 2006, the format is ENVI standard format, the projection is ALBERS, and its time resolution is 15 days and its spatial resolution is 8km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data. 1. File format: The GIMMS-NDVI dataset contains all rar compressed files with a 15-day interval from July 1981 to 2006. After decompression, it includes an XML file, an .HDR header file, an .IMG file, and a .JPG image file. 2. File naming: The naming rules for compressed files in the NOAA / AVHRR-NDVI data set are: YYMMM15a (b) .n **-VIg_data_envi.rar, where YY-year, MMM-abbreviated English month letters, 15a-synthesized in the first half of the month, 15b-synthesized in the second half of the month, **-Satellite. After decompression, there are 4 files with the same file name, and the attributes are: XML document, header file (suffix: .HDF), remote sensing image file (suffix: .IMG), and JPEG image file. In this data set, the user uses the remote sensing image file with the suffix .IMG to analyze the vegetation index. Remote sensing image files with suffix of .IMG and .HDF used by users to analyze vegetation indices can be opened in ENVI and ERDAS software. 3. The data header file information is as follows: Coordinate System is: PROJECTION ["Albers_Conic_Equal_Area"], PARAMETER ["standard_parallel_1", 25], PARAMETER ["standard_parallel_2", 47], PARAMETER ["latitude_of_center", 0], PARAMETER ["longitude_of_center", 105], PARAMETER ["false_easting", 0], PARAMETER ["false_northing", 0], UNIT ["Meter", 1]] Pixel Size = (8000.000000000000000, -8000.000000000000000) Corner Coordinates: Upper Left (-3922260.739, 6100362.950) (51d20'23.06 "E, 46d21'21.43" N) Lower Left (-3922260.739, 1540362.950) (71d16'1.22 "E, 8d41'42.21" N) Upper Right (3277739.261, 6100362.950) (151d 8'57.22 "E, 49d 9'35.37" N) Lower Right (3277739.261, 1540362.950) (133d30'58.46 "E, 10d37'13.35" N) Center (-322260.739, 3820362.950) (101d22'21.08 "E, 35d42'18.02" N) Band 1 Block = 900x1 Type = Int16, ColorInterp = Undefined Computed Min / Max = -16066.000,11231.000 4. Conversion relationship between DN value and NDVI NDVI = DN / 1000, divided by 10000 after 2003 The NDVI value should be between [-1,1]. Data outside this interval represent other features, such as water bodies.
Tucker, C.J., J.E.Pinzon, M.E.Brown
The dataset of ground truth measurements synchronizing with Envisat ASAR was obtained in the saline plot B, the alfalfa plot D and the barley plot E of the Linze grassland 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. The quadrate was divided into 6×6 subsites, with each one spanning a 120×120 m2 plot. Corner points were chosen. Simultaneous with the satellite overpass, numerous ground data were collected, soil gravimetric moisture, volumetric moisture, and soil bulk density after drying by the cutting ring (100cm^3), the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot B; soil moisture, soil conductivity, the soil temperature, and the real part of soil complex permittivity by WET, the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot D; 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, the mean soil temperature from 0-5cm by the probe thermometer, and the land surface radiative temperature measured three times by the hand-held infrared thermometer in plot E. Data were archived in Excel file. Those provide reliable ground data for retrieval and validation of soil moisture and alinity content with active microwave remote sensing approaches. See WATER: Dataset of setting of the sampling plots and stripes in the Linze station foci experimental area for more information.
CHAO Zhenhua, HU Xiaoli, LIANG Ji, Wang Weizhen, LIU Zhaoyan, TANG Bohui, HAN Hui, WANG Xiaoping
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
LI Mingsong , MA Jin
The dataset of object spectral was obtained in the Linze station foci experimental area from May 25 to Jul. 11, 2008. The measurement instrument is ASD Spectroradiometer (350～2 500 nm) from BNU and the reference board (40% before Jun. 15 and 20% hereafter). The selected typical objects included maize field, soil, soil with known moisture and desert scrub. The measured quadrates included Wulidun farmland quadrates (May 28 and 30, Jun. 16 and 29 and Jul. 11), the desert transit zone strips (May 28 and 30 and Jun. 16) and Linze station quadrates (May 23 and Jul. 9) Besides, soil samples were collected inside Linze station quadrates on Jun. 24 and 30, 2008. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance and transmittivity were archived as text files (.txt). 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.
LI Jing, Li Xiangyun, Qu Yonghua, SUN Qingsong, XIAO Zhiqiang, YU Yingjie, LIU Sihan, BAI Yanfen, WANG Yang, CHEN Shaohui, JIANG Hao, LI Shihua
This dataset includes 44 scenes, covering the whole Heihe River Basin, which were acquired on (yy-mm-dd) 2012-08-25, 2012-09-03, 2012-09-08, 2012-09-13, 2012-09-18, 2012-09-23, 2012-09-28, 2012-10-03, 2012-10-13, 2012-10-18, 2012-10-22, 2012-11-01, 2012-11-11, 2012-11-21. The data are of multi-spectral bands with data product of Level 1. The spatial resolution is 1 m. ZY-3 dataset was acquired from purchase.
China Centre for Resources Satellite Data and Application
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
The dataset of ground truth measurements synchronizing with the airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze grassland foci experimental area on Jun. 15, 2008. The observation item was mainly the land surface temperature by the hand-held infrared thermometer in the reed plot A, the saline plots B and C, and the alfalfa plot D, the maximum of which were 120m×120m and the minimum were 30m×30m. 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.
CHAO Zhenhua, GE Chunmei, HUANG Chunlin, FENG Lei, YU Fan
The dataset of ground truth measurements synchronizing with EO-1 Hyperion was obtained in the Yingke oasis foci experimental area from Sep. 5 to Sep. 10, 2007 during the pre-observation period. It was carried out by the 3rd and 2nd sub-projects of CAS’s West Action Plan along Zhangye city-Yingke oasis-Huazhaizi, and on the very day of 10, one scene of Hyperion was captured. sampling plot time north latitude east longitude elevation notes 1 9:58 38°53′53.2″ 100°26′09.7″ 1500 cauliflower land east to the road 2 10:51 38°52′39.8″ 100°25′33.1″ 1510 cabbage land east to the road 3 11:35 38°52′39.0″ 100°25′34.6″ 1510 east to No. 2 sampling plot, maize and intercropping wheat reaped 4 12:24 38°51′53.0″ 100°25′08.0″ 1510 maize seed 5 13:08 38°51′54.2″ 100°25′09.5″ 1520 north to No. 4 sampling plot, maize and intercropping wheat reaped 6 14:40 38°51′23.5″ 100°24′45.0″ 1510 west to the road, maize seed, serious blights (red spider) 7 15:40 38°49′26.6″ 100°23′23.7″ 1540 intercrop land of sea buckthorn and beet 8 16:18 38°49′06.9″ 100°23′30.5″ 1540 tomato land, rich of amaranth weeds 9 16:18 38°49′06.4″ 100°23′30.8″ 1540 beet land 10 16:18 38°49′06.9″ 100°23′30.5″ 1540 tomato land with less weeds 11 10:30 38°48′28.3″ 100°24′11.4″ 1540 sea buckthorn seedling land west to the road 12 11:24 38°48′09.3″ 100°24′10.1″ 1550 sun flower land east to the road, intercropping wheat reaped 13 12:38 38°46′16.3″ 100°23′14.2″ 1600 dry rice land 14 12:45 38°46′16.2″ 100°23′14.0″ 1600 rape land 15 12:54 38°46′15.6″ 100°23′13.8″ 1600 buckwheat land 16 14:52 38°45′55.5″ 100°23′00.1″ 1610 maize (without intercrop) 17 15:28 38°45′57.5″ 100°22′28.3″ 1630 maize (without intercrop) 18 16:20 38°43′17.3″ 100°22′53.4″ 1730 gobi (Bassia dasyphylla and margarite dominate) 19 17:40 38°42′31.8″ 100°22′56.8″ 1780 gobi (Bassia dasyphylla and Sympegma regelii dominate) 20 10:27 38°36′25.1″ 100°20′33.2″ 2260 wheatgrass dominates 21 11:10 38°36′24.4″ 100°20′38.1″ 2260 abandoned composite land 22 11:30 2260 near site 22, wheatgrass and composite cenosis 23 bare land 24 13:09 38°38′46.3″ 100°23′08.5″ 2030 alfalfa land 25 14:39 38°44′30.8″ 100°22′41.0″ 1660 poplar 26 9:47 38°58′11.4″ 100°26′18.3″ 1460 rice land Observation items included: (1) quadrat surveys (2) LAI by LAI-2000 (3) ground object reflectance spectra by ASD FieldSpec Pro (350-2500nm)from Gansu Meteorological Administration (4) the land surface temperature and the canopy radiative temperature by the hand-held thermal infrared sensor (5) the photosynthesis rate by LI-6400 (6) the radiative temperature by ThermaCAM SC2000 (7) Atmospheric parameters by CE318 to retrieve the total optical depth, aerosol optical depth, Rayleigh scattering coefficient, column water vapor in 936 nm, and various parameters at 550nm to obtain horizontal visibility with the help of MODTRAN or 6S codes (8) chlorophyll consistency by portable SPAD Those provide reliable ground data for developing and validating retrieval meathods of biophysical parameters from EO-1 Hyperion images.
MA Mingguo, LI Xin, SU Peixi, DING Songchuang, GAO Song, YAN Qiaodi, ZHANG Lingmei, WANG Xufeng, Qian Jinbo, BAI Yunjie, HAO Xiaohua, Liu Qiang, Wen Jianguang, XIN Xiaozhou, WANG Xiaoping, HAN Hui
The dataset of ground truth measurement synchronizing with the airborne microwave radiometers (L&K bands) mission was obtained in the Linze station foci experimental area on Jul. 8, 2008. Observation items included: (1) soil moisture (0-5cm) measured by the cutting ring method (50cm^3) in P1 to P6 strips (17 sample points each). Photos were taken. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by three handheld infrared thermometer (5# and 6# from Cold and Arid Regions Environmental and Engineering Research Institute, and one from Institute of Geographic Sciences and Natural Resources, which were all calibrated) from P1 to P6 strips. 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. 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.
XIE Tingting, JIANG Hao, SONG Yi, BAI Yanfen, GAO Song, Qian Jinbo, SHU Lele, SONG Yi, XU Zhen, XIE Tingting, JIANG Hao, LI Shihua
The dataset of ground truth measurement synchronizing with Envisat ASAR was obtained in No. 1, 2 and 3 quadrates of the A'rou foci experimental area on Jul. 5 and Jul. 6, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:14 BJT. The quadrates were divided into 4×4 subsites, with each one spanning a 30×30 m2 plot. Observation items included: (1) the quadrate investigation in No. 2 and 3 quadrates: GPS by GARMIN GPS 76, plant species by manual cognition, the plant number by manual work, the height by the measuring tape repeated 4-5 times, phenology by manual work, the coverage by manual work (compartmentalizing 0.5m×0.5m into 100 to see the percentage the stellera takes) and the chlorophyll content by SPAD 502. (2) spectrum of stellera and pasture by ASD FieldSpec (350～2 500 nm), with 20% reference board. The preprocessed canopy spectrum was archived. (3) BRDF by ASD FieldSpec (350～2 500 nm), with 20% reference board. The processed reflectance and transmittivity were archived as .txt files. (4) photosynthesis of stellera and pasture by LI-6400. The data were archived in Excel format. (5) soil moisture by WET soil moisture tachometer. Acquisition time, soil moisture (%vol), Ecp (ms/m), Tmp Eb and Ecb (ms/m) of 25 corner points were archived. (6) the soil temperature by the handheld infrared thermometer. Acquisition time, the soil temperature measured three times and the land cover types were archived. The data included the canopy reflectance on Jul. 5 and 6, photosynthesis on Jul. 5 and 6, BRDF on Jul. 5, photos on Jul. 5, the infrared land surface temperature and soil moisture by WET on Jul. 5, biomass on Jul. 5 and the surface temperature along No. 3 flight on Jul. 6.
DING Songchuang, GE Yingchun, LI Hongyi, MA Mingguo, Qian Jinbo, WANG Yang, YU Yingjie, LIU Sihan
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
In 2007, 2008 and 2009, ENVISAT ASAR data 179 scenes, covering the whole Heihe River Basin. Among them, there were 63 in 2007, 71 in 2008 and 45 in 2009. Imaging mode and acquisition time are respectively: app can select polarization mode from August 15, 2007 to December 23, 2007, from January 02, 2008 to December 202009-02-15, 2008 to September 06, 2009; imp imaging mode from June 19, 2009 to July 12, 2009; WSM wide mode from January 1, 2007 to December 302008-01-01, 2007 to November 28, 2008, from March 13, 2009 to May 22, 2009. The product level is L1B, which is amplitude data without geometric correction. The ENVISAT ASAR remote sensing data set of Heihe comprehensive remote sensing joint experiment is mainly obtained through the China EU "dragon plan" project (Project No.: 5322 and 5344); the WSM wide model data in 2007 and January 2008 are obtained from Professor Bob Su of ITC; the 8-view app can be purchased from the earth observation and digital earth center of Chinese Academy of Sciences.
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
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