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. This dataset is a long-term GIMMS vegetation index dataset of the Qinghai Lake Basin, which includes changes in the vegetation index from 1981 to 2006. The time resolution is 15 days and the spatial resolution is 8 km. GIMMS NDVI data recorded the changes of vegetation in 22a area in the format of satellite data.
National Aeronautics and Space Administration
This is the MODIS data with 499 scenes covering the whole Heihe River basin in 2008 and 2009. The acquisition time is from 2008-04-23 to 2008-09-30 (295 scenes), and from 2009-05-01 to 2009-10-01 (204 scenes). MODIS data products have 36 channels with resolutions of 250m, 500m and 1000m respectively. The data format is pds, unprocessed, and the MODIS processing software is filed together with the original data. MODIS remote sensing data of Heihe Integrated Remote Sensing Joint Test are provided by Gansu Meteorological Bureau.
Gansu meteorological bureau
The dataset of ground truth measurement synchronizing with PROBA CHRIS was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 22, 2008. Observation items included: (1) Albedo by the shortwave radiometer in Huazhaizi desert No. 2 plot. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (2) BRDF of maize in Yingke oasis maize field by ASD (350-2 500 nm) from Beijing University and the observation platform of BNU make. The maximum height of the platform was 5m above the ground with the azimuth 0~360° and the zenith angle -60°~60°; BRDF in Huazhaizi desert No. 2 plot by ASD from Institute of Remote Sensing Applications (CAS) and the observation platform of its own make, whose maximum height was 2m above the ground with the zenith angle -70°~70°. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Atmospheric parameters in Huazhaizi desert No. 2 plot 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 format and can be opened by ASTPWin. ReadMe.txt is attached for detail. Processed data (after retrieval of the raw data) in Excel format 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.
CHEN Ling, GUO Xinping, REN Huazhong, ZOU Jie, LIU Sihan, ZHOU Chunyan, FAN Wenjie, TAO Xin
The dataset of the ground-based microwave radiometers and ground truth observations (multi-frequency, multi-polar multi-angle) for soil freeze/thaw cycle in the A'rou foci experimental area from Oct. 19 to 25, 2007, during the pre-observation period, X-band from Oct. 20 to 25, S-band from Oct. 20 to 25, K-band from Oct. 19 to 24, and Ka-band from Oct. 20 to 24, to be specific. The aims of the measurements were the effects of the soil freeze/thaw status on the microwave brightness temperatures. Those provide reliable ground data for improving and verifying microwave radiative transfer models and parameters retrieval of soil freeze/thaw status. Time-continuous ground observations synchronizing with the ground-based microwave radiometers including self-recording and manual measurements, were carried out in No. 1 quadrate of A'rou with dry natural grassland as the landscape. (1) self-recording observations: the soil temperatures at 0cm, 5cm, 10cm, 15cm and 20cm by the temperature probe from Oct. 21 to 25, 2007, and shallow layer soil moisture at 0-5cm, 5cm, 10cm, 15cm and 20cm by TDR from Oct. 19 to 21 2007. Both time interval of the observations were 5 minutes. (2) manual observations: the surface radiative temperature by the handheld infrared thermometer, the soil temperature at 0cm, 5cm, 10cm, 15cm and 20cm by the glass geothermometer, and the mean soil temperature from 0-5cm by the probe thermometer. The time interval of observations was 30 minutes from Oct. 19-21, 2007.
BAI Yunjie, CAO Yongpan, HAO Xiaohua, LI Hongyi, LI Xin, LI Zhe, QIN Chun, Wang Weizhen
The NDVI data set is the latest release of the long sequence (1981-2015) normalized difference vegetation index product of NOAA Global Inventory Monitoring and Modeling System (GIMMS), version number 3g.v1. The temporal resolution of the product is twice a month, while the spatial resolution is 1/12 of a degree. The temporal coverage is from July 1981 to December 2015. This product is a shared data product and can be downloaded directly from ecocast.arc.nasa.gov. For details, please refer to https://nex.nasa.gov/nex/projects/1349/.
The National Center for Atmospheric Research
On 10 July 2012 (UTC+8), TASI sensor carried by the Harbin Y-12 aircraft was used in a visible near Infrared hyperspectral airborne remote sensing experiment, which is located in the observation experimental area (30×30 km), Linze region and Heihe riverway. The relative flight altitude is 2500 meters. The wavelength of TASI is 8-11.5 μm with a spatial resolution of 3 meters. Through the ground sample points and atmospheric data, the data are recorded in surface radiance processed by geometric correction and atmospheric correction.
XIAO Qing, Wen Jianguang
Water scarcity，food crises and ecological deterioration caused by drought disasters are a direct threat to food security and socio-economic development. Improvement of drought disaster risk assessment and emergency management is now urgently required. This article describes major scientific and technological progress in the field of drought disaster risk assessment. Drought is a worldwide natural disaster that has long affected agricultural production as well as social and economic activities. Frequent droughts have been observed in the Belt and Road area, in which much of the agricultural land is concentrated in fragile ecological environment. Soil relative humidity index is one of the indicators to characterize soil drought and can directly reflect the status of crops' available water.
FAN Wenjie
This dataset includes component temperatures measured by the thermal infrared (TIR) radiometers at the Mixed Forest and Sidaoqiao stations between 22 July, 2014 and 19 July, 2016. 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, two TIR radiometers (SI-111, Apogee Instruments Inc., USA) connected to a data logger (CR800, Campbell Scientific Inc., USA) measured component temperatures of the sunlit canopy and shaded canopy. TIR radiometers were mounted horizontally at 5 m height on iron rods just south and north of a tree and pointed to its canopy. The distance from the sensor to the canopy was ~1 m. At the Sidaoqiao station, two SI-111 TIR radiometers connected to a CR800 data logger measured component temperatures of the soil and shrub. The first sensor pointed from 2 m height under a viewing zenith angle of 45° to bare soil; the second sensor was mounted at 1-m height and pointed horizontally into the shrub canopy.
ZHOU Ji, LI Mingsong , MA Jin
The dataset of ground truth measurements synchronizing with ASTER was obtained in the Linze station foci experimental area on May 28, 2008. Observation items included: (1) soil moisture (0-5cm) measured once by the cutting ring method at the corner points of the 40 subplots of the west-east desert transit zone strip once by cutting ring method in the corner points of nine subplots of the north-south desert transit zone, once by the cutting ring method and once by ML2X Soil Moisture Tachometer in the center points of nine subplots of the farmland. The preprocessed soil volumetric moisture data were archived as Excel files. (2) surface radiative temperature measured by the 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), and nine subplots of the north-south desert transit zone strip (repeated 12-30 times). Data were archived as Excel files. (3) BRDF of maize and desert scrub measured by ASD Spectroradiometer (350～2 500 nm) from BNU, the 40% reference board , two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland quadrates and the desert transit zone strips. 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). (4) LAI measured by two methods in the the Wulidun farmland quadrates and Linze station quadrates. One is manual method. The LAI, plant height and the spacing of selected samples were measured by the ruler and the number of the sapmles in the quadrate were counted. Then the LAI can be calculated. The other method is LI-3100. Data were archived as Excel files.
Qian Jinbo, SONG Yi, WANG Zhixia, WANG Yang, PAN Xiaoduo, LI Jing, Li Xiangyun, Qu Yonghua, SUN Qingsong
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 May 25, 2008. Observation items included: (1) soil moisture (0-5cm) measured once by the cutting ring method in the corner points of the 40 subplots of the west-east desert transit zone strip , three times in the corner points of the nine subplots of the north-south desert transit zone, once by the cutting ring and once by ML2X Soil Moisture Tachometer in the center points of nine subplots of the farmland quadrates. The preprocessed soil volumetric moisture data were archived as Excel files. (2) the surface radiative temperature 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) in the west-east and north-south desert transit zone strip (various times synchronizing with the airplane), and Wulidun farmland quadrates (repeated twice at intervals of 15m from east to west). 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) maize BRDF once by ASD Spectroradiometer (350～2 500 nm) from BNU, the reference board (40% before Jun. 15 and 20% hereafter), two observation platforms of BNU make and one of Institute of Remote Sensing Applications make in Wulidun farmland. Raw spectral data were archived as binary files, which were recorded daily in detail, and pre-processed data on reflectance 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.
DING Songchuang, GAO Song, PAN Xiaoduo, Qian Jinbo, WANG Yang, ZHU Shijie, LI Jing, XIAO Zhiqiang
The NDVI data set is the sixth version of the MODIS Normalized Difference Vegetation Index product (2001-2016) jointly released by NASA EOSDIS LP DAAC and the US Geological Survey (USGS EROS). The product has a temporal resolution of 16 days and a spatial resolution of 0.05 degrees. This version is a Climate Modeling Grid (CMG) data product generated from the original NDVI product (MYD13A2) with a resolution of 1 kilometer. Please indicate the source of these data as follows in acknowledgments: The MOD13C NDVI product was retrieved online courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, The [PRODUCT] was (were) retrieved from the online [TOOL], courtesy of the NASA EOSDIS Land Processes Distributed Active Archive Center (LP DAAC), USGS/Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota.
NASA
The data set includes the estimated data of the SOS (start of season) and the EOS (end of season) of vegetation in Sanjiangyuan based on 10-day synthetic NDVI products from the SPOT satellite. Two common phenological estimation methods were adopted: the threshold extraction method based on polynomial fitting (the term “poly” was included in the file names) and the inflection point extraction method based on double logistic function fitting (the term “sig” was included in the file names). These data can be used to analyse the relationship between vegetation phenology and climate change. The temporal coverage is from 1999 to 2013, and the spatial resolution is 1 km.
WANG Xufeng
The dataset of snow spectral reflectance observations was obtained in the Binggou watershed foci experimental area from Dec. 5 to Dec. 15, 2007 during the pre-observation period. The aims of the measurements were to verify feasibility of the predetermined observation schemes and to collect data for retrieval from remote sensing approaches. All data were acquired by ASD spectrometer from Xinjiang Meteorological Administration. Observation items included: (1) Random observations on snow spectrum in the chosen snowpack at the Binggou cold region hydrometeorological station on Dec. 5, 6 and 7, 2007 (2) Snow spectrum observations in BG-A simultaneous with MODIS and Terra MISR on Dec. 10, 2007 (3) The pure and the mixed snow pixel spectrum in BG-A on Dec. 15, 2007 (4) Multi-angle snow spectrum in the chosen snowpack in BG-A on Dec. 15, 2007 Seven subfolders including raw data and pre-processed data are named after the acquisition time, Dec. 5, 2007, Dec. 6, 2007, Dec. 7, 2007, Dec. 10, 2007, Dec. 13, 2007, Dec. 15, 2007 and Dec. 15, 2007, respectively.
ZHANG Pu, LIU Yan
The dataset of ground truth measurement synchronizing with Envisat ASAR and MODIS was obtained in the arid region hydrological experimental area on May 24, 2008. The Envisat ASAR data were in AP mode and VV/VH polarization combinations, and the overpass time was approximately at 11:34 BJT. Observation items included: (1) The radiative temperature of Reaumuria soongorica and the bare soil in Huazhaizi desert No. 2 plot (HZZHMYD2)was collected using ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°), along the diagonal (NW-SE). The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived as Excel files). (2) The radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 0.95), were measured at nadir with time intervals of one second. Raw data, blackbody calibrated data and processed data were all archived as Excel files. (3) The radiative temperature in Huazhaizi desert No. 2 plot by the handheld infrared thermometer (which belongs to BNU) along the diagonal (NW-SE). Raw data (.doc), blackbody calibrated data and processed data (in Excel format) were all archived. (4) Soil moisture (0-40cm) by the cutting ring and the soil temperature by the thermocouple thermometer in Yingke oasis and Huazhaizi foci experimental area. Besides, (a) roughness of No. 1 and 2 Huazhizi desert plots was also measured by self-made instruments . Sample points were selected every 30m along the diagonal of each plot. (b) soil profile moisture (0-100cm) and the temperature in the maize field of Yingke oasis. (c) soil profile moisture (0-100cm) and the temperature in one orchard of Yingke Oasis. Data were all archived as Excel files. (5) the photosynthetic rate of alfalfa and barley at Linze grass station by LI-6400. Raw data were archived in the user-defined format (by notepat.exe) and processed data were as Excel files. (6) ground object reflectance spectra of new-born rape and the bare land in Biandukou foci experimental area by ASD FieldSpec (350～2500 nm) from Institute of Remote Sensing Applications (CAS). Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (7) LAI by the measuring tape and the ruler in the alfalfa field of Linze grass station. The maximum length and width of alfalfa leaves and barley were measured. Data were archived as Excel files. (8) surface roughness in Huazhaizi desert No. 2 plot with the self-made roughness board (Cold and Arid Regions Environmental and Engineering Research Institute, CAS), the digital camera and the compass. Sample points were selected at equal intervals along the diagonals and marked in the photos.
CHEN Ling, KANG Guoting, QIAN Yonggang, REN Huazhong, WANG Haoxing, WANG Jindi, YAN Guangkuo, GE Yingchun, SHU Lele, WANG Jianhua, XU Zhen, GUANG Jie, LI Li, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, TAO Xin, YAN Binyan, YAO Yanjuan, CHENG Zhanhui, YANG Tianfu
The vegetation phenology data set of Heihe River basin provides remote sensing phenology products from 2012 to 2015. The spatial resolution is 1km and the projection type is sinusoidal. MODIS Lai product mod15a2 is used as the phenological remote sensing monitoring data source, and MODIS land cover classification product mcd12q1 is used as the auxiliary data set for extraction. The product algorithm first uses the time series data reconstruction method (bise method) to control the data quality of the input time series; then uses the main algorithm (logistic function fitting method) and the backup algorithm (piecewise linear fitting method) to extract the vegetation phenological parameters, realizes the complementary calculation method, guarantees the accuracy and improves the inversion rate. The algorithm can extract up to three growth cycles in a year, each growth cycle contains six data sets, including the start point of vegetation growth, the start point of growth peak, the end point of growth peak, the end point of growth, the fastest growth and the fastest decline. At the same time, it records the growth cycle type, growth season length, quality identification, etc., a total of 25 data sets. The phenology product reduces the missing rate of inversion, improves the stability of the product, and the data set is relatively reliable with rich information.
LI Jing
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature of different kinds of underlying surface, including greenhouse film, the roof, road, ditch, concrete floor and so on, while the sensor of thermal infrared go into the experimental areas of artificial oases eco-hydrology on the middle stream. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal infrared sensor and the analysis of the scale effect of the land surface temperature, and finally serve for the validation of the plausibility checks of the surface temperature product from remote sensing. 1. Observation time and other details On 25 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers recorded. On 26 June, 2012, ditch and asphalt road surface temperatures were observed once every five minutes using handheld infrared thermometers while greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 29 June, 2012, concrete floor surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 30 June, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed continuously using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every one second using self-recording point thermometer. On 10 July, 2012, asphalt road, ditch, bare soil, melonry and ridge of field surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of TASI go into the region. At the same time, concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 26 July, 2012, asphalt road, concrete floor, bare soil and melonry surface temperatures were observed once every one minute using handheld infrared thermometers during the sensor of WiDAS go into the region. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. On 2 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, twelve sites were selected according to the flight strip of the WiDAS sensor, and for each site one plot surface temperatures were recorded continuously during the sensor of WiDAS go into the region. On 3 August, 2012, corn field and concrete floor surface temperatures were observed using handheld infrared thermometers. At the same time, greenhouse film and concrete floor surface temperatures were observed once every six second using self-recording point thermometer. For corn field, fourteen sites were selected according to the flight strip of the WiDAS sensor, and for each site three plots surface temperatures were recorded continuously during the sensor of WiDAS go into the region. 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. The observation heights of the self-recording point thermometer for the greenhouse film and the concrete floor were 0.5 m and 1 m, respectively. All instruments were calibrated three times (on 6 July, 5 August and 20 September, 2012) using black body during observation. 3. Data storage All the observation data were stored in excel.
GENG Liying, Jia Shuzhen, WANG Haibo, PENG Li, Dong Cunhui
The dataset focuses on the distribution of sampling plots and stripes in the Yingke oasis and Huazhaizi desert steppe foci experimental areas. (1) YKLZYMD-the maize field plot (180m×180m) at Yingke Weather Station It matches No. 10 flight route. Five subplots were selected, including three maize subplots and 2 wheat subplots. The maize subplots, labeled as YKLZYMD01, YKLZYMD02 and YKLZYMD03, were planted in different directions with a ridge sturctrue, which was composed of single row of maizes and bare soils. The distance of adjacent maize rows, as well as the width of bare soil was 0.5m . YKLZYMD05 (2.46m×1m, along the ridge) was located in the northwest of the plot and interplanted with wheat and soy bean. YKLZYMDD06 was exclusively wheat, and 10 rows (1.5m) vertical to the ridge and 1m along the ridge were measured. This is a key experimental area for canopy spectrum, component reflectance spectra, BRDF, albedo, the photosynthetic rate, FPAR, structural parameters, vegetation coverage, the radiative temperature, surface emissivity, atmospheric parameters and soil moisture. (2) YKXMD-Yingke wheat plot (180m×170m) It matches No. 11 flight route. Wheat and maize were interplanted. Three subplots with the same size (3.4m * 3.4m) were selected for the measurement of vegetaion structural parameters, BRDF, the radiative temperature, vegetation coverage and soil moisture. (3) HZZHMZYMD-Huazhaizi maize plot (240m×240m) It is located between No. 9 and No. 10 flight routes. The maize seed dominates, and wheat, alfalfa and tomatoes were planted. 4 maize subplots and one wheat subplot were chosen to collect the canopy temperature, spectrum, structural parameters and vegetation coverage. (4) HZZHMYD1-Huazhaizi desert No. 1 plot (240m×240m) It is located within No. 4 flight route. 3 subplots (30m×30m) were chosen for reflectance spectra, BRDF, vegetation coverage, emissivity, the radiative temperature, soil moisture, atmospheric parameters by sunphotometer CE318 and surface roughness. In cooperation with experiments in Huazhaizi desert plots and Yingke weather station, simultaneous airborne multiangular thermal infrared camera&CCD-ground observations, simultaneous airborne hyperspectral imager (OMIS)-ground observations, simultaneous OMIS/TM/ASTER/Hyperion/CHRIS/ASAR-ground observations were all accomplished. (5) HZZHMYD2-Huazhaizi desert No. 2 plot It matches No. 5 flight route. Three subplots (10m×10m) for coverage and the radiative temperature and one (30m×30m) for simultaneous temperature and spectrum were chosen. (6) HZZHMYD3-Huazhaizi desert No. 3 plot (30m×30m) It is an intensive plot without simultaneous airporne or spaceborne measurement. (7) DJCYMYD-the maize field at the resort It is an intensive plot (30m×30m) with the maize seeds, mainly for the measurement of radiative temperature and soil moisture. (8) DJCDMD-the barley field at the resort It is mainly for radiative temperature data. (9) DJCDBC-the calibration field at the resort It is located at the ICBC resort. The reflectance spectra of the basketball court, the pool and the vegetation were collected used for radiative calibration of CCD camera in visible and near infrared spectra range. The dataset also includes geographic infomation of each sample point.
REN Huazhong, YAN Guangkuo, XIN Xiaozhou, Liu Qiang, WANG Jianhua
Wildfires can strongly affect the frozen soil environment by burning surface vegetation and soil organic matter. Vegetation affected by fire can take many years to return to mature pre-fire levels. In this data set, the effects of fires on vegetation regrowth in a frozen-ground tundra environment in the Anaktuvuk River Basin on the North Slope of Alaska were studied by quantifying changes in C-band and L-band SAR backscatter data over 15 years (2002-2017). After the fire, the C- and L-band backscattering coefficients increased by 5.5 and 4.4 dB, respectively, in the severe fire area compared to the unburned area. Five years after the fire, the difference in C-band backscattering between the fire zone and the unburned zone decreased, indicating that the post-fire vegetation level had recovered to the level of the unburned zone. This long recovery time is longer than the 3-year recovery estimated from visible wavelength-based NDVI observations. In addition, after 10 years of vegetation recovery, the backscattering of the L-band in the severe fire zone remains approximately 2 dB higher than that of the unburned zone. This continued difference may be caused by an increase in surface roughness. Our analysis shows that long-term SAR backscattering data sets can quantify vegetation recovery after fire in an Arctic tundra environment and can also be used to supplement visible-wavelength observations. The temporal coverage of the backscattering data is from 2002 to 2017, with a time resolution of one month, and the data cover the Anaktuvuk River area on the North Slope of Alaska. The spatial resolution is 30~100 m, the C- and L-band data are separated, and a GeoTIFF file is stored every month. For details on the data, see SAR Backscattering Data of the Anaktuvuk River Basin on the North Slope of Alaska - Data Description.
JIANG Liming
This dataset contains the spectra of white cloth and black cloth obtained in the simultaneous time during the airborn remote sensing which supports the airboren data preprocessing as CASI, SASI and TASI , and the spetra of the typical targets in the middle reaches of the Heihe River Basin. Instruments: SVC-HR1024 from IRSA, ASD Field Spec 3 from CEODE, Reference board Measurement method: the spectra radiance of the targets are vertically measured by the SVC or ASD; before and after the target, the spectra radiance of the reference board is measured as the reference. This dataset contains the spectra recorded by the SVC-HR1024 ( in the format of .sig which can be opened by the SVC-HR1024 software or by the notepad ) and the ASD (in the format of .asd), the observation log (in the format of word or excel), and the photos of the measured targets. Observation time: 15-6-2012, the spectra of typical targets in the EC matrix using SVC 16-6-2012, the spectra of typical targets in the wetland by SVC 29-6-2012, the spectra of typical vegetation and soil in Daman site and Gobi site by ASD 29-6-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 30-6-2012, the spectra of vegetation and soil in the desert by ASD 5-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 7-7-2012, the spectra of corn in the Daman site for the research of daily speral variation. 8-7-2012, the spectra of white cloth and black cloth by ASD which is simultaneous with the airborne CASI data 8-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 9-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 10-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation 11-7-2012, the spectra of corn in the Daman site by ASD for the research of daily speral variation. The time used in this dataset is in UTC+8 Time.
XIAO Qing, MA Mingguo
The dataset of ground truth measurement synchronizing with Landsat TM was obtained in the A'rou foci experimental area from Jul. 10 to Jul. 12, 2008. The stellera and the whin coverage were mainly measured. Photos were taken in No. 2 quadrate of A'rou and an optional stellera land for coverage mesurement from Jul. 10 to 11, shooting straight downwards at the height of 1.5 m. The fisheye camera was Nikon D80 with a lens of Sigma 8mm F3.5 EX DG CIRCULAR FISHEYE. The vegetation height was measured on Jul. 12. One grid of 5m×5m was chosen in each of the eight quadrates (60m×60m or 120m×120m) and compartmentalized into 2.5m×2.5m, in which GPS positions by GARMIN GPS 76, species, the plant number and height were measured. Four files were included, the quadrates coordinates, stellera observations in No. 2 quadrate, the stellera quadrat investigation and TM quadrate investigation.
BAI Yanfen, Qian Jinbo, GAO Song, HAO Xiaohua, SHU Lele
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