The data set was obtained from UAV aerial photography during the field investigation of the Qinghai Tibet Plateau in August 2020. The data size is 10.1 GB, including more than 11600 aerial photos. The shooting sites mainly include Lhasa, Shannan, Shigatse and other areas along the road, residential areas and surrounding areas. The aerial photos mainly reflect the local land use / cover type, facility agriculture distribution, grassland coverage and other information. The aerial photos have longitude, latitude and altitude information, which can provide better verification information for land use / cover remote sensing interpretation, and can also be used for vegetation coverage estimation, and provide better reference information for land use research in the study area.
LV Changhe LIU Yaqun
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
MAO Kebiao
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
MAO Kebiao
This dataset contains measurements of L-band brightness temperature by an ELBARA-III microwave radiometer in horizontal and vertical polarization, profile soil moisture and soil temperature, turbulent heat fluxes, and meteorological data from the beginning of 2016 till August 2019, while the experiment is still continuing. Auxiliary vegetation and soil texture information collected in dedicated campaigns are also reported. This dataset can be used to validate the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) satellite based observations and retrievals, verify radiative transfer model assumptions and validate land surface model and reanalysis outputs, retrieve soil properties, as well as to quantify land-atmosphere exchanges of energy, water and carbon and help to reduce discrepancies and uncertainties in current Earth System Models (ESM) parameterizations. ELBARA-III horizontal and vertical brightness temperature are computed from measured radiometer voltages and calibrated internal noise temperatures. The data is reliable, and its quality is evaluated by 1) Perform ‘histogram test’ on the voltage samples (raw-data) of the detector output at sampling frequency of 800 Hz. Statistics of the histogram test showed no non-Gaussian Radio Frequency Interference (RFI) were found when ELBAR-III was operated. 2) Check the voltages at the antenna ports measured during sky measurements. Results showed close values. 3) Check the instrument internal temperature, active cold source temperature and ambient temperature. 3) Analysis the angular behaviour of the processed brightness temperatures. -Temporal resolution: 30 minutes -Spatial resolution: incident angle of observation ranges from 40° to 70° in step of 5°. The area of footprint ranges between 3.31 m^2 and 43.64 m^2 -Accuracy of Measurement: Brightness temperature, 1 K; Soil moisture, 0.001 m^3 m^-3; Soil temperature, 0.1 °C -Unit: Brightness temperature, K; Soil moisture, m^3 m^-3; Soil temperature, °C/K
Bob Su WEN Jun
Snow pits were observed daily at Altay base station（lon：88.07、lat: 44.73） from November 27, 2015 to March 26, 2016. Parameters include: snow stratification, stratification thickness, density, particle size, temperature. The frequency of observation was daily. The particle size was observed by a microscope with camera, the density was observed by snowfork, snow shovel and Snow Cone, and the temperature was automatically observed by temperature sensor. The observation time was 8:00-10:100 am local time. The snow particle size is observed according to the natural stratification of snow. The snow particles of each layer are collected, and at least 2 photos are taken. The long axis and short axis of at least 10 groups of particles are measured by corresponding software. Unit: mm. The density was observed at equal intervals, snowfork every 5 cm, snow shovel every 10 cm, snow cone to observe the density of the whole snow layer, and the density of each layer was observed three times. The unit is g / cm3. The height of temperature observation is 0cm, 5cm, 10cm, 15cm, 25cm, 35cm, 45cm, 55cm. The recording frequency was once every 1 minute. The unit is OC.
DAI Liyun
This dataset is the Fractional Vegetation Cover observation in the artificial oasis experimental region of the middle stream of the Heihe River Basin. The observations lasted for a vegetation growth cycle from May 2012 to September 2012 (UTC+8). Instruments and measurement method: Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. Details are described in the following: 0. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1. For row crop like corn, the plot is set to be 10×10 m2, and for the orchard, plot scale is 30×30 m2. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 2. The photographic method used depends on the species of vegetation and planting pattern: Low crops (<2 m) in rows in a situation with a small field of view (<30 ), rows of more than two cycles should be included in the field of view, and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. 3. High vegetation in rows (>2 m) Through the top-down photography of the low vegetation underneath the crown and the bottom-up photography beneath the tree crown, the FVC within the crown projection area can be obtained by weighting the FVC obtained from the two images. Next, the low vegetation between the trees is photographed, and the FVC that does not lie within the crown projection area is calculated. Finally, the average area of the tree crown is obtained using the tree crown projection method. The ratio of the crown projection area to the area outside the projection is calculated based on row spacing, and the FVC of the quadrat is obtained by weighting. 4. FVC extraction from the classification of digital images. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation.
MU Xihan HUANG Shuai MA Mingguo
The dataset of ground truth measurements for snow synchronizing with the airborne microwave radiometers (K&Ka bands) mission was obtained in the Binggou watershed foci experimental area on Mar. 30, 2008. Those provide reliable data for retrieval of snow parameters and properties, especially for dry and wet snow identification. Observation items included: (1) Snow density, snow complex permittivity, snow volumetric moisture and snow gravimetric moisture by the snowfork in BG-A; (2) Snow parameters including snow depth, the snow surface temperature synchronizing with the airborne microwave radiometers (K&Ka bands), the snow layer temperature, the snow grain size and snow density in BG-A (10 points), BG-B (6 points), BG-F (12 points), BG-H (21 points) and BG-I (20 points); For each snow pit, the snowpack was divided into several layers with 10-cm intervals of snow depth. The layer depth (by the ruler), the snow grain size (by the handheld microscope), snow density (by the cutting ring) and the snow temperature (by the probe thermometer) were obtained at each snow pit. Two files including raw data and the preprocessed data were archived.
GE Chunmei GU Juan HAO Xiaohua MA Mingguo WANG Jianhua WANG Xufeng LI Hua CHE Tao
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.
FAN Wenjie
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 LI Xin WANG Xufeng
This data set includes the continuous observation data set of light temperature and surface temperature and humidity measured by the vehicle borne microwave radiometer from November 10 to 14, 2013 in aroucaochang, arouxiang, Qilian County, Qinghai Province. The surface temperature and humidity include six layers of temperature sensor at the soil depth of 1cm, 3cm, 5cm, 10cm, 15cm, 20cm and six layers of humidity sensor at the soil depth of 0-5cm. The time frequency of routine observation of soil temperature and humidity is 5 minutes. Data details: 1. Time: November 10-14, 2013 2. data: Brightness temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 10.65, 18.7 and 36.5ghz V polarization and H polarization data Soil temperature: use the sensor installed on dt80 and dt85 to measure the soil temperature of 1cm, 5cm, 10cm, 20cm, and 1cm, 3cm, 5cm, 10cm, 15cm, which is measured by the sensor connected to dt80 Soil moisture: use h-probe sensor to measure 0-5cm soil moisture, the probe can measure 0-5cm soil temperature at the same time 3. Data size: 16.7M 4. Data format:. Xls
ZHAO Shaojie KOU Xiaokang YE Qinyu MA Mingguo
The dataset includes the fractional vegetation cover data generated from the stations of crop land, wetland, Gebi desert and desert steppe in Yingke Oasis and biomass data generated from the stations of crop land (corn) and wetland. The observations lasted for a vegetation growth cycle from 19 May, 2012 to 15 September, 2012. 1. Fractional vegetation cover observation 1.1 Observation time 1.1.1 Station of the crop land: The observations lasted from 20 May, 2012 to 15 September, 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the station of crop land (corn) are 2013-5-20, 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.1.2 The other four stations: The observations lasted from 20 May, 2012 to 15 September, 2012 and in ten-day periods for each observation. The observation time for the crop land are 2013-5-20, 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 1.2 method 1.2.1 Instruments and measurement method Digital photography measurement is implemented to measure the FVC. Plot positions, photographic method and data processing method are dedicatedly designed. In field measurements, a long stick with the camera mounted on one end is beneficial to conveniently measure various species of vegetation, enabling a larger area to be photographed with a smaller field of view. The stick can be used to change the camera height; a fixed-focus camera can be placed at the end of the instrument platform at the front end of the support bar, and the camera can be operated by remote control. 1.2.2 Design of the samples Three and two plots with the area of 10×10 m^2 were measured for the station of the crop land and wetland, respectively. One plot with the area of 10×10 m^2 was measured for the other three stations. Shoot 9 times along two perpendicularly crossed rectangular-belt transects. The picture generated of each time is used to calculate a FVC value. “True FVC” of the plot is then acquired as the average of these 9 FVC values. 1.2.3 Photographic method The photographic method used depends on the species of vegetation and planting pattern. A long stick with the camera mounted on one end is used for the stations of crop land and wetland. For the station of the crop land, rows of more than two cycles should be included in the field of view (<30), and the side length of the image should be parallel to the row. If there are no more than two complete cycles, then information regarding row spacing and plant spacing are required. The FVC of the entire cycle, that is, the FVC of the quadrat, can be obtained from the number of rows included in the field of view. For other three stations, the photos of FVC were obtained by directly photographing for the lower heights of the vegetation. 1.2.4 Method for calculating the FVC The FVC calculation was implemented by the Beijing Normal University. The detail method can be found in the reference below. Many methods are available to extract the FVC from digital images, and the degree of automation and the precision of identification are important factors that affect the efficiency of field measurements. This method, which is proposed by the authors, has the advantages of a simple algorithm, a high degree of automation and high precision, as well as ease of operation (see the reference). 2. Biomass observation 2.1. Observation time 2.1.1 Station of the crop land: The observations lasted from 20 May 2012 to 15 September 2012, and in five-day periods for each observation before 31 July and in ten-day periods for each observation after 31 July. The observation time for the crop land are 2013-5-25, 2013-5-30, 2013-6-5, 2013-6-10, 2013-6-16, 2013-6-22, 2013-6-27, 2013-7-2, 2013-7-7, 2013-7-12, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.1.2 The station of wetland: The observations lasted from 20 May 2012 to 15 September 2012, and in ten-day periods for each observation. The observation time for the crop land are 2013-6-5, 2013-6-16, 2013-6-27, 2013-7-7, 2013-7-17, 2013-7-27, 2013-8-3, 2013-8-13, 2013-8-25, 2013-9-5 and 2013-9-15. 2.2. Method Station of the crop land: Three plots were selected and three strains of corn for each observation were random selected for each plot to measure the fresh weight (the aboveground biomass and underground biomass) and dry weight. Per unit biomass can be obtained according to the planting structure. Station of the wetland: Two plots of reed with the area of 0.5 m × 0.5 m were random selected for each observation. The reed of the two plots was cut to measure the fresh weight (the aboveground biomass) and dry weight. 2.3. Instruments Balance (accuracy 0.01 g); drying oven 3. Data storage All observation data were stored in excel. Other data including plant spacing, row spacing, seeding time, irrigation time, the time of cutting male parent and the harvest time of the corn for the station of cropland were also stored in the excel.
GENG Liying Jia Shuzhen Li Yimeng MA Mingguo
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.
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 JIN Rui LI Xin
The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze station-Linze grassland flight zone on Jun. 15, 2008. Data after radiometric correction and calibration and geometric approximate correction were released. The flying time of each route was as follows: {| ! id ! flight ! file ! starttime ! lat ! long ! alt ! image liange ! endtime ! lat ! long ! lat |- | 1 || reservoir 1 || 2008-06-15_11-55-28_DATA.BSQ || 12:12:48 || 39.013 || 100.236 || -1.0 || 2540 || 12:15:37 || 39.085 || 100.150 || -1.0 |- | 2 || 1-13 || 2008-06-15_12-15-51_DATA.BSQ || 12:20:47 || 39.172 || 100.048 || 2867.7 || 5572 || 12:26:58 || 39.359 || 100.190 || 2867.8 |- | 3 || 1-12 || 2008-06-15_12-27-13_DATA.BSQ || 12:31:59 || 39.366 || 100.188 || 2846.6 || 5067 || 12:37:37 || 39.185 || 100.051 || 2867.8 |- | 4 || 1-11 || 2008-06-15_12-37-51_DATA.BSQ || 12:42:52 || 39.179 || 100.039 || 2878.8 || 5542 || 12:49:02 || 39.363 || 100.179 || 2884.8 |- | 5 || 1-10 || 2008-06-15_12-49-16_DATA.BSQ || 12:54:29 || 39.373 || 100.179 || 2909.9 || 5116 || 13:00:10 || 39.187 || 100.039 || 2897.3 |- | 6 || 1-9 || 2008-06-15_13-00-24_DATA.BSQ || 13:05:30 || 39.182 || 100.028 || 2864.2 || 5498 || 13:11:37 || 39.366 || 100.167 || 2859.7 |- | 7 || 1-8 || 2008-06-15_13-11-51_DATA.BSQ || 13:17:22 || 39.377 || 100.169 || 2846.8 || 5114 || 13:23:02 || 39.191 || 100.029 || 2862.3 |- | 8 || 1-7 || 2008-06-15_13-23-17_DATA.BSQ || 13:28:06 || 39.187 || 100.0187 || 2857.1 || 5497 || 13:34:13 || 39.372 || 100.158 || 2842.5 |- | 9 || 1-6 || 2008-06-15_13-34-27_DATA.BSQ || 13:39:10 || 39.380 || 100.158 || 2909.7 || 5184 || 13:44:55 || 39.197 || 100.019 || 2861.8 |- | 10 || 1-5 || 2008-06-15_13-45-10_DATA.BSQ || 13:50:09 || -1.000 || -1.000 || -1.0 || 5488 || 13:56:09 || -1.000 || -1.000 || -1.0 |- | 11 || 1-4 || 2008-06-15_13-56-23_DATA.BSQ || 14:01:20 || -1.000 || -1.000 || -1.0 || 5353 || 14:07:18 || -1.000 || -1.000 || -1.0 |- | 12 || 1-3 || 2008-06-15_14-07-32_DATA.BSQ || 14:12:36 || -1.000 || -1.000 || -1.0 || 5350 || 14:18:30 || -1.000 || -1.000 || -1.0 |- | 13 || 1-2 || 2008-06-15_14-18-46_DATA.BSQ || 14:22:48 || -1.000 || -1.000 || -1.0 || 5236 || 14:28:31 || -1.000 || -1.000 || -1.0 |- | 14 || 1-1 || 2008-06-15_14-28-49_DATA.BSQ || 14:34:02 || -1.000 || -1.000 || -1.0 || 5964 || 14:40:11 || -1.000 || -1.000 || -1.0 |- | 15 || reservoir 2 || 2008-06-15_14-40-51_DATA.BSQ || 14:51:05 || -1.000 || -1.000 || -1.0 || 6846 || 14:58:35 || -1.000 || -1.000 || -1.0 |}
Liu Liangyun LI Xin MA Mingguo
The dataset of airborne WiDAS mission was obtained in the Linze station-Linze grassland flight zone on Jun. 29, 2008. Data available for general users include Level-2C data (after geometric, radiometric and atmospheric corrections), Level-1B browse image (after intra-band matchingintra-band) and Level-2B browse image (intra-bandafter registration). The raw data, Level-1A, and data processing parameters were filed; applications would be evaluated prior to access. Data processing started in Aug. 2008 and ended in Apr. 2009, and in Nov. 2009, CCD data were reprocessed to adjust radiometric calibration. The flying time of each route was as follows: {| ! id ! flight ! relative height ! starttime ! endtime ! data size ! data state ! data quality ! ground targets |- | 1 || 1#13 || 1500m || 11:44:35 || 11:50:31 || 90 || processed;complete || good || Pingchuan reservoir |- | 2 || 1#11 || 1500m || 11:55:55 || 12:01:55 || 91 || processed;complete || good || Linze grass station |- | 3 || 1#9_1 || 1500m || 12:06:27 || 12:12:27 || 91 || incomplete || incomplete || Pingchuan reservoir |- | 4 || 1#9_2 || 1500m || 13:01:35 || 13:07:43 || 93 || processed;complete || good || Pingchuan reservoir |- | 5 || 1#7 || 1500m || 12:17:59 || 12:23:59 || 91 || processed;complete || good || desert transit plot |- | 6 || 1#5 || 1500m || 12:28:35 || 12:34:31 || 90 || processed;complete || good || North-south desert strip |- | 7 || 1#3 || 1500m || 12:39:11 || 12:45:03 || 89 || processed;complete || good || Pingchuan reservoir |- | 8 || 1#1 || 1500m || 12:50:55 || 12:56:51 || 90 || processed;complete || good || Linze station |}
Liu Qiang XIAO Qing Wen Jianguang WANG Heshun LI Xin MA Mingguo
The dataset of airborne Polarimetric L-band Multibeam Radiometers (PLMR) was acquired on 1 August, 2012, located in the upper reaches of the Heihe River Basin. The aircraft took off at 8:30 am (UTC+8) from Zhangye airport and landed at 12:30 pm, with the flight time of 4 hours. The flight was performed in the altitude of about 1000 m and at the speed of about 220-250 km during the observation, corresponding to an expected ground resolution of about 300 m. The PLMR instrument flown on a small aircraft operates at 1.413 GHz (L-band), with both H- and V-polarizations at incidence angles of ±7.5°, ±21.5° and ±38.5°. PLMR ‘warm’ and ‘cold’ calibrations were performed before and after each flight. The processed PLMR data include 2 DAT files (v-pol and h-pol separately) and 1 KMZ file for each flying day. The DAT file contains all the TB values together with their corresponding beam ID, incidence angle, location, time stamp (in UTC) and other flight attitude information as per headings. The KMZ file shows the gridded 1-km TB values corrected to 38.5 degrees together with flight lines. Cautions should be taken when using these data, as the RFI contaminations are often higher than expected at v-polarization.
CHE Tao Gao Ying LI Xin
The dataset of diurnal FPAR change observations was obtained in the Yingke oasis foci experimental areas. Observation items included: (1) Maize canopy reflectance spectra by ASD and 50% grey board, leaf SPAD by the chlorophyll meter and leaf photosynthesis by LI-6400 in Yingke oasis maize field on Jul. 5, 2008 (fixed point observations from 10:00-20:00 at intervals of one hour, and half an hour from 16:00) Besides, Photo: photosynthetic rate (µmol CO2 m-2 s-1), Cond: stomatal conductance (mol H2O m-2 s-1), Ci: intercellular CO2 viscosity (µmol CO2 mol-1), Trmmol: transpiration rate (mmol H2O m-2 s-1), VpdL: vapor pressure deficiency of leaves (kPa), Tleaf: leaf temperature (°C), ParIn_µm: active radiation of interior photosynthesis (µmol m-2 s-1), and ParOutµm: active radiation of outdoor photosynthesis (µmol m-2 s-1) were all archived. (2) Maize canopy reflectance spectra, leaf photosynthesis and diurnal FPAR change by ASD (Institute of Remote Sensing Applications), 50% grey board (Institute of Remote Sensing Applications), LI-6400 (Institute of Remote Sensing Applications) and SUNSCAN (Beijing academy of Agriculture and Forestry Sciences). Based on calibration lamp data (serial number: 64831), radiance spectrum on Jul. 9 by 1050 spectrometer (Beijing academy of Agriculture and Forestry Sciences) and 50% grey board and 99% white board calibration data, the spectrum data were preprocessed. Calibration was undertaken in accordance with the following precedures: a) The original DN was converted into radiance and further into readable EXCEL format by the spectrometer-matched calibration lamp data and ASD. b) Solar radiance was got by 99% white board radiance. solar radiance=the reference board radiance/the reference board reflectance. c) Spectrum from Agriculture and Forestry Sciences was sampled at an interval of 1.438nm, which was made into data at 1nm intervals by segmentation interpolation. d) Based on b=16.087a (where a is radiance before fitting and b after fitting), radiance data got by 68731 spectrograph were processed. The original maize leaf photosynthesis data (by LI-6400) were introduced into EXCEL format, diurnal changes of each leaf were archived as one single unit according to leaf classification. Maize FPAR (the fraction of photosynthetically active radiation) was got by FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR= FPAR×canopy PAR. The unit for PAR was µmol m-2 s-1. The data included number (the whole leaf), observation time (hh:mm:ss), upper light (µmol m-2 s-1), upper reflectivity (µmol m-2 s-1), lower light (µmol m-2 s-1), lower reflectivity (µmol m-2 s-1) and Spread: variation coefficients of the probe optical intensity.
Liu Liangyun
The dataset of ground truth measurement synchronizing with EO-1 Hyperion was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on May 25, 2008. Observation items included: (1) Atmospheric parameters on the ICBC resort office roof 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. (2) Ground object reflectance spectra f new-born rape and the bare land in Biandukou foci experimental area by ASD FieldSpec (350~2500 nm) from BNU. Raw data were binary files direct from ASD (by ViewSpecPro), and pre-processed data on reflectance were in Excel format. (3) Soil moisture (0-40cm) by the cutting ring and the soil temperature (0-40cm) by the thermocouple in Huazhaizi desert No. 1 plot and the windbreak forest; and soil moisture and the soil temperature (0-100cm) in Yingke oasis maize field. Data were archived in Excel format. (4) LAI. The maximum leaf length and width of each alfalfa and barley were measured. Data were archived in Excel format. (5) Coverage of maize and wheat in Yingke oasis maize field, of vegetation (Reaumuria soongorica) in Huazhaizi desert No. 1 and 2 plots by the self-made coverage instrument and the camera (2.5m-3.5m above the ground). Based on the length of the measuring tape and the bamboo pole, the size of the photo can be decided GPS date were also collected and the technology LAB was applied to retrieve the coverage of the green vegetation. Besides, such related information as surroundings environment was also recorded. Data included the primarily measured image and final fraction of vegetation coverage.
Gai Yingchun WANG Jianhua GUANG Jie XIN Xiaozhou ZHANG Yang
The dataset of sun photometer observations was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas. 24 times observations were carried out by CE318 from BNU (at 1020nm, 936nm, 870nm, 670nm and 440nm, and column water vapor by 936 nm data) and from Institute of Remote Sensing Applications, CAS (at 1640nm, 1020nm, 936nm, 870nm, 670nm, 550nm, 440nm, 380nm and 340nm, and column water vapor by 936 nm data) on May 20, 23, 25 and 27, Jun. 4, 6, 16, 20, 22, 23, 27 and 29, Jul. 1, 7 and 11, 2008. Those atmospheric measurements synchronized with airborne (i.e. WiDAS, OMIS) and spaceborne sensors (i.e. TM, ASTER,CHRIS and Hyperion) Accuracy of CE318 could be influenced by local air pressure, instrument calibration parameters, and convertion factors. (1) Most air pressure was derived from elevation-related empiricism, which was not reliable. For more accurate result, simultaneous data from the weather station are needed. (2) Errors from instrument calibration parameters. Field calibration based on Langly or interior instrument calibrationcin the standard light is required. (3) Convertion factors for retrieval of aerosol optical depth and the water vapor of the water vapor channel were also from empiricism, and need further checking. Raw data were archived in k7 format and can be opened by ASTPWin. ReadMe.txt is attached for details. Preprocessed 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. Langley was used for the instrument calibration. Two parts are included in CE318 result data (see Geometric Positions and the Total Optical Depth of Each Channel and Rayleigh Scattering and Aerosol Optical Depth of Each Channel).
GUANG Jie
The dataset of airborne WiDAS mission was obtained in the national observatory on climatology at Zhangye-Zhangye flight zone on Jun. 29, 2008. Intra-band data available for general users include Level-2C data (after geometric, radiometric and atmospheric corrections), Level-1B browse image (after intra-band matching) and Level-2B browse image (after registration). The raw data, Level-1A, and data processing parameters were filed; applications would be evaluated prior to access. Data processing started in Aug. 2008 and ended in Apr. 2009, and in Nov. 2009, CCD data were reprocessed to adjust radiometric calibration. The flying time of each route was as follows: {| ! id ! flight ! relative height ! starttime ! endtime ! data size ! data state ! data quality ! ground targets |- | 1 || 2#5 || 1500m || 13:14:39 || 13:22:43 || 122 || processed;complete || good || National observatory on climatology at Zhangye;Gulou in Zhangye |- | 2 || 2#7 || 1500m || 13:28:23 || 13:35:31 || 108 || processed;complete || good |- | 3 || 2#9 || 1500m || 13:41:11 || 13:49:03 || 119 || processed;complete || good || wetland park in Zhangye |}
Liu Qiang XIAO Qing Wang Heshun LI Xin MA Mingguo
