Atmospheric water vapor is an important parameter for studying the water cycle. In the context of global warming, in order to better study the impact of atmospheric water vapor on the water cycle, a global daily scale AMSR-E/AMSR2 all-weather atmospheric precipitable water (TPW) dataset with a spatial resolution of 0.25 ° was constructed. In the data set, the TPW over land is mainly obtained by our newly developed 18.7 and 23.8 GHz brightness temperature data inversion algorithm based on AMSR-E and AMSR2; The ocean sky TPW data integrates AMSR-E/AMSR2 official TPW products. As a post-processing, in order to eliminate the systematic deviation between AMSR-E TPW and AMSR2 TPW, using AIRX2RET TPW as the benchmark, the histogram matching method was used to correct the systematic deviation of AMSR-E and AMSR2 TPW data on a global scale, to ensure the continuity of the data, and finally the global daily scale AMSR-E and AMSR2 TPW all-weather data sets were obtained. Among them, the time range of AMSR-E data is from July 8, 2002 to September 27, 2011, and the time range of AMSR-2 data is from January 1, 2013 to August 31, 2017. Each date contains two files: orbit raising and orbit lowering. The data format is Geotiff. The number of data layers is 2. The first layer is TPW data, with the unit of mm. The second layer is time information, which represents the number of seconds elapsed between the pixel observation time with UTC as the time base and 0:00:00 of the current day. The data set has reliable quality. Through verification and analysis with the global SuomiNET GPS TPW, the root mean square error of the data set is 3.5-5.2mm. As atmospheric precipitable water is an important geophysical parameter affecting surface remote sensing and also has an important impact on the earth's climate change, this data can be used for research on the impact of atmospheric water vapor on the water cycle, the assessment of atmospheric water resources and atmospheric correction in the context of climate warming.
JI Dabin, SHI Jiancheng, HUSI Letu, LI Wei , ZHANG Hongxing , SHANG Huazhe
Global solar radiation and diffuse horizontal solar radiation at Dome C (Antarctica) are measured by radiation sensors (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground are obtained from the IPEV/PNRA Project “Routine Meteorological Observation at Station Concordia”, http://www.climantartide.it. This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Lanconelli, C.; Lupi, A.; Driemel, A.; Vitale, V.; Li, K.; Song, T. 2022. Long-Term Variations of Global Solar Radiation and Its Potential Effects at Dome C (Antarctica). Int. J. Environ. Res. Public Health, 19, 3084. https://doi.org/10.3390/ijerph19053084). The observed global solar radiation and meteorological parameters are available at https://doi.org/10.1594/PANGAEA.935421. The data set can be used to study solar radiation and its attenuation at Dome C, Antarctica.
BAI Jianhui
Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.
BAI Jianhui
Precipitation over the Tibetan Plateau (TP) known as Asia's water tower plays a critical role in regional water and energy cycles, largely affecting water availability for downstream countries. Rain gauges are indispensable in precipitation measurement, but are quite limited in the TP that features complex terrain and the harsh environment. Satellite and reanalysis precipitation products can provide complementary information for ground-based measurements, particularly over large poorly gauged areas. Here we optimally merged gauge, satellite, and reanalysis data by determining weights of various data sources using artificial neural networks (ANNs) and environmental variables including elevation, surface pressure, and wind speed. A Multi-Source Precipitation (MSP) data set was generated at a daily timescale and a spatial resolution of 0.1° across the TP for the 1998‒2017 period. The correlation coefficient (CC) of daily precipitation between the MSP and gauge observations was highest (0.74) and the root mean squared error was the second lowest compared with four other satellite products, indicating the quality of the MSP and the effectiveness of the data merging approach. We further evaluated the hydrological utility of different precipitation products using a distributed hydrological model for the poorly gauged headwaters of the Yangtze and Yellow rivers in the TP. The MSP achieved the best Nash-Sutcliffe efficiency coefficient (over 0.8) and CC (over 0.9) for daily streamflow simulations during 2004‒2014. In addition, the MSP performed best over the ungauged western TP based on multiple collocation evaluation. The merging method could be applicable to other data-scarce regions globally to provide high quality precipitation data for hydrological research. The latitude and longitude of the left bottom corner across the TP, the number of rows and columns, and grid cells information are all included in each ASCII file.
HONG Zhongkun , LONG Di
The preparation of this data set is based on the proposed downscaling method of all-weather surface temperature data for the glacier area in Southeast Tibet. By analyzing the relationship between all-weather surface temperature and its spatio-temporal influence factor elevation, surface coverage type, vegetation index, snow cover index, surface reflectance and other data, a downscaling model of all-weather surface temperature is constructed, which increase the spatial resolution of all-weather surface temperature products from 1 km to 250 m. The validation results show that the RMSE of downscaling surface temperature at the site is about 2.25 K and 2.16 K in the daytime and at night, respectively, which is about 0.5 K higher than that of the original 1 km surface temperature product. The results of image quality index show that the downscaling surface temperature not only obtains a lot of detailed thermal information, but also maintains a high consistency with the original 1 km surface temperature in spatial pattern and amplitude. This data set has certain significance for high resolution all-weather surface temperature generation and disaster monitoring in glacier area of Southeast Tibet.
ZHOU Ji, HUANG Zhiming , ZHONG Hailing , TANG Wenbin
The data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation and test carried out in Liupanshan area during 2021. Liupanshan scientific research is carried out in Dawan station, Jingyuan station, Liupanshan station, Longde station, etc. Dawan station is mainly equipped with cfl-06 wind profile radar, ht101 cloud radar, mrr-2 micro rain radar, dsg5 raindrop spectrometer, three-dimensional anemometer, C12 laser cloud altimeter. Jingyuan station is mainly equipped with qfw-6000 microwave radiometer, hmb-kps cloud radar, dsg5 raindrop spectrometer Cl51 laser cloud altimeter. Liupanshan station is mainly equipped with ht101 cloud radar, mrr-2 micro rain radar, Ott laser raindrop spectrometer, cloud condensation nodule (CCN) counter, three-dimensional anemometer, FM120 droplet spectrometer and C12 laser cloud altimeter. Longde station is mainly equipped with rpg-hatpro-g4 microwave radiometer, cfl-06 wind profile radar, ht101 Cloud Radar, mrr-2 micro rain radar Ott laser raindrop spectrometer, C12 laser cloud altimeter. Meanwhile automatic weather station, iron tower (Shangpu), X-band all solid-state dual polarization Doppler Weather Radar (Pengyang County), gradient station and other observations were done. It can be used to study the impact of the eastward movement of the plateau system on the downstream, and to reveal the impact of the atmospheric boundary layer and free atmospheric exchange process on aerosols, clouds Fog and precipitation and their interaction.
FU Danhong
The data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation and test carried out in Sanjiangyuan area during 2021. The scientific research of Sanjiangyuan mainly focuses on Advanced Air King aircraft observation. The airborne observation system includes aerosol, cloud particle spectrometer and imager observation. The observation elements include precipitation particle concentration and image of IP probe, cloud particle concentration and image of CIP probe, cloud and aerosol particle data of CAS probe and Hotwire_ LWC probe liquid water data, CAPS Summary aerosol, cloud and precipitation comprehensive data, AIMMS probe conventional meteorological elements, PCASP -100 probe aerosol particle data. Ground observation includes raindrop spectrometer, microwave radiometer and X-band radar. Raindrop spectrometer mainly observes equivalent volume diameter and particle falling speed. Microwave radiometer mainly observes temperature, humidity, water vapor and liquid water. And X-band radar mainly observes intensity, velocity and spectral width. It can provide data support for the study of the impact of westerly monsoon synergy on the cloud precipitation process of Sanjiang source.
FU Danhong
This data set is a sub data set of the comprehensive observation data set of cloud precipitation process, which is derived from the comprehensive investigation test carried out on the South and north slopes of Qilian Mountains during 2020. The air observation is mainly conducted by the king aircraft in the air. The ground investigation includes automatic weather station, raindrop spectrometer, microwave radiometer, Cloud Radar, sounding second data, etc. The observation elements of automatic weather station include air temperature, air pressure, humidity Wind direction, wind speed, precipitation. The observation elements of raindrop spectrometer include particle spectrum, precipitation intensity, etc. The observation elements of microwave radiometer are atmospheric temperature and humidity profiles. The observation elements of cloud Radar are mainly fixed-point vertical observation data. Meanwhile aerosol, rain, hail and soil samples are collected. It can provide data support for revealing the influence of westerly monsoon on cloud precipitation process and atmospheric water cycle in Qilian Mountains.
FU Danhong
Photosynthetically active radiation (PAR) is fundamental physiological variable driving the process of material and energy exchange, and is indispensable for researches in ecological and agricultural fields. In this study, we produced a 35-year (1984-2018) high-resolution (3 h, 10 km) global grided PAR dataset with an effective physical-based PAR model. The main inputs were cloud optical depth from the latest International Satellite Cloud Climatology Project (ISCCP) H-series cloud products, the routine variables (water vapor, surface pressure and ozone) from the ERA5 reanalysis data, aerosol from the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) products and albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) product after 2000 and CLARRA-2 product before 2000. The grided PAR products were evaluated against surface observations measured at seven experimental stations of the SURFace RADiation budget network (SURFRAD), 42 experimental stations of the National Ecological Observatory Network (NEON), and 38 experimental stations of the Chinese Ecosystem Research Network (CERN). The instantaneous PAR was validated at the SURFRAD and NEON, and the mean bias errors (MBEs) and root mean square errors (RMSEs) are 5.6 W m-2 and 44.3 W m-2, and 5.9 W m-2 and 45.5 W m-2, respectively, and correlation coefficients (R) are both 0.94 at 10 km scale. When averaged to 30 km, the errors were obviously reduced with RMSEs decreasing to 36.3 W m-2 and 36.3 W m-2 and R both increasing to 0.96. The daily PAR was validated at the SURFRAD, NEON and CERN, and the RMSEs were 13.2 W m-2, 13.1 W m-2 and 19.6 W m-2, respectively at 10 km scale. The RMSEs were slightly reduced to 11.2 W m-2, 11.6 W m-2, and 18.6 W m-2 when upscaled to 30 km. Comparison with the other well-known global satellite-based PAR product of the Earth's Radiant Energy System (CERES) reveals that our PAR product was a more accurate dataset with higher resolution than the CRERS. Our grided PAR dataset would contribute to the ecological simulation and food yield assessment in the future.
TANG Wenjun
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
The measurement data of the sun spectrophotometer can be directly used to perform inversion on the optical thickness of the non-water vapor channel, Rayleigh scattering, aerosol optical thickness, and moisture content of the atmospheric air column (using the measurement data at 936 nm of the water vapor channel). The aerosol optical property data set of the Tibetan Plateau by ground-based observations was obtained by adopting the Cimel 318 sun photometer, and both the Mt. Qomolangma and Namco stations were involved. The temporal coverage of the data is from 2009 to 2016, and the temporal resolution is one day. The sun photometer has eight observation channels from visible light to near infrared. The center wavelengths are 340, 380, 440, 500, 670, 870, 940 and 1120 nm. The field angle of the instrument is 1.2°, and the sun tracking accuracy is 0.1°. According to the direct solar radiation, the aerosol optical thickness of 6 bands can be obtained, and the estimated accuracy is 0.01 to 0.02. Finally, the AERONET unified inversion algorithm was used to obtain aerosol optical thickness, Angstrom index, particle size spectrum, single scattering albedo, phase function, birefringence index, asymmetry factor, etc.
CONG Zhiyuan
This dataset includes the emissivity spectrum (8-14 µm) of typical ground objects in Zhangye City, Zhangye airport, desert and farmland at Wuxing experiment area. The data was measured by the BOMEM MR304 FTIR (Fourier Transform Infrared Spectrometer). A. Objective The objective of the thermal infrared (TIR) spectrum measurement lies in: Radiometric calibration for the airborne TIR sensor, land surface emissivity products validation and collecting typical surface spectrum working as priori knowledge in land surface temperature inversion and ecological and hydrological models. B. Instruments and theory Instruments: BOMEM MR304 FTIR, Mikron M340 blackbody, BODACH BDB blackbody, diffused golden plate, Fluke 50-series II thermometer Measurement theory: The target radiance is directly measured by the MR304 FTIR under clear-sky condition while the atmospheric downward radiance is obtained through a diffused golden plate, and emissivity is retrieved by the Iterative Spectrally Smooth Temperature and Emissivity Separation (ISSTES) algorithm C. Experiment site and targets 29-5-2012: Stone bricks, grassland and asphalt, etc at square of Zhangye. 20-6-2012: Roof of the building in Zhangye, water and sand sample collected from the desert, etc. 30-6-2012: Cement road at Zhangye airport, desert around the Zhangye airport. 3-7-2012: Corn leaves, soil and road in the farmland at Wuxing village, Zhangye City. 4-7-2012: Corn leaves, wheat canopy at Xiaoman town, Zhangye City. 10-7-2012: Bricks of Runquanhu park, Zhangye City. 13-7-2012: Corn leaves and other plants at Wuxing village, Zhangye City. D. Data processing The original data collected by BOMEM FTIR is firstly calibrated using the calibration data and get the radiance spectrum of the targets and sky (*.rad), then, the radiance data is converted to the easy readably text file (ASCII format). The time used in this dataset is in UTC+8 Time.
MA Mingguo, XIAO Qing
This dataset includes the emissivity spectrum of typical ground objects in middle researches of the Heihe river basin. This dataset was acquired in oasis, desert, Gobi and wetland of experiment area. Time range starts from 2012-05-25 to 2012-07-18 (UTC+8). Instrument: MODEL 102F PORTABLE FTIR (Fourier Transform Infrared Spectrometer), Handheld infrared thermometer. Measurement methods: at the first step, measure the thermal radiance of cold blackbody, warm blackbody, sample and gold plate (Downwelling Radiance). The radiance of cold blackbody and warm blackbody was used to calibrate the instrument, and eliminate the “noise” caused by the device itself. The retrieval of emissivity and temperature was then performed using iterative spectrally smooth temperature-emissivity separation (ISSTES) algorithm. The retrieved emissivity spectrum range from 8 to 14 μm, with spectral resolution of 4cm-1. Dataset contains the original recorded spectra (in ASCII format) and the log files (in doc format). The processed data are emissivity curves (ASCII) that ranged from 8 to 14 μm, and the temperatures of samples. Thermal photos of the sample, digital photo of the scene and the object are recorded in some cases.
MA Mingguo
The object of this dataset is to support the atmospheric correction data for the satellite and airborne remote-sensing. It provides the atmospheric aerosol and the column content of water vapor. The dataset is sectioned into two parts: the conventional observations data and the observations data synchronized with the airborne experiments. The instrument was on the roof of the 7# in the Wuxing Jiayuan community from 1 to 24 in June. After 25 June, it was moved to the ditch in the south of the Supperstaiton 15. The dataset provide the raw observations data and the retrieval data which contains the atmosphere aerosol optical depth (AOD) of the wavebands at the center of 1640 nm, 1020 nm, 936 nm, 870 nm, 670 nm, 500 nm, 440 nm, 380 nm and 340 nm, respectively, and the water vapor content is retrieved from the band data with a centroid wavelength of 936 nm. The continuous data was obtained from the 1 June to 20 September in 2012 with a one minute temporal resolution. The time used in this dataset is in UTC+8 Time. Instrument: The sun photometer is employed to measure the character of atmosphere. In HiWATER, the CE318-NE was used.
YU Wenping, WANG Zengyan, MA Mingguo
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
This data set is typical specific emissivity data set of Heihe River Basin. Data observation is from March 25, 2014 to June 30, 2015. Instrument: Portable Fourier transform infrared spectrometer (102f), hand-held infrared thermometer Measurement method: 102f was used to measure the radiation values of cold blackbody, warm blackbody, observation target and gold plate. Using the radiation value of the cold and warm blackbody, the 102f is calibrated to eliminate the influence of the instrument's own emission. By using the iterative inversion algorithm based on smoothness, the specific emissivity and the object temperature are inversed. The specific emissivity range is 8-14 μ m, and the resolution is 4cm-1. This data set contains the original radiation curves (in ASCII format) and recording files of cold blackbody, warm blackbody, measured target and gold plate obtained by 102f.
YU Wenping, REN Zhiguo, TAN Junlei, Li Yimeng, WANG Haibo, MA Mingguo
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).
REN Huazhong, YAN Guangkuo, GUANG Jie, SU Gaoli, WANG Ying, ZHOU Chunyan
The dataset of sun photometer observations was obtained in Linze grassland station, the reed plot A, the saline plot B, the barley plot E, the observation stationof the Linze grassland foci experimental areaand Jingdu hotel of Zhangye city. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired by CE318 from May 30 to Jun. 11, 2008. And from Jun. 15 to Jul.11, the data of 1640nm, 1020nm, 936nm, 870nm, 670nm, 550nm, 440nm, 380nm and 340nm were acquired. Both measurements were carried out at intervals of 1 minute. Optical depth, rayleigh scattering, aerosol optical depth, the horizontal visibility, air temperature and pressure near land surface, the solar azimuth and zenith could all be further retrieved. Readme file was attached for detail.
LIANG Ji, WANG Xufeng
The dataset of ground truth measurement synchronizing with the airborne WiDAS mission was obtained in the Yingke oasis and Huazhaizi desert steppe foci experimental areas on Jun. 1, 2008. WiDAS, composed of four CCD cameras, one mid-infrared thermal imager (AGEMA 550), and one infrared thermal imager (S60), can acquire CCD, MIR and TIR band data. The simultaneous ground data included: (1) The radiative temperature of maize, wheat and the bare land in Yingke oasis maize field and Huazhaizi desert No. 1 plot by ThermaCAM SC2000 (1.2m above the ground, FOV = 24°×18°). The data included raw data (read by ThermaCAM Researcher 2001), recorded data and the blackbody calibrated data (archived in Excel format). (2) The radiative temperature by the automatic thermometer (FOV: 10°; emissivity: 1.0; from Institute of Remote Sensing Applications), observing straight downwards at intervals of 1s in Yingke oasis maize field. Raw data, blackbody calibrated data and processed data were all archived in Excel format. (3) FPAR (Fraction of Photosynthetically Active Radiation) of maize and wheat by SUNSACN and the digital camera in Yingke oasis maize field. FPAR= (canopyPAR－surface transmissionPAR－canopy reflection PAR+surface reflectionPAR) /canopy PAR; APAR=FPAR* canopy PAR. Data were archived in Excel format. (4) The reflectance spectra by ASD in Yingke oasis maize field (350-2500nm , from BNU, the vertical canopy observation and the transect observation), and Huazhaizi desert No. 1 plot (350-2500nm , from Cold and Arid Regions Environmental and Engineering Research Institute, CAS, the NE-SW diagonal observation at intervals of 30m). The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (5) Maize albedo by the shortwave radiometer in Yingke oasis maize field. R =10H (R for FOV radius; H for the probe height). Data were archived in Excel format. (6) The radiative temperature by the handheld radiometer in Yingke oasis maize field (from BNU, the vertical canopy observation, the transect observation and the diagonal observation), Yingke oasis wheat field (only for the transect temperature), and Huazhaizi desert No. 1 plot (the NE-SW diagonal observation). Besides, the maize radiative temperature and the physical temperature were also measured both by the handheld radiometer and the probe thermometer in the maize plot of 30m near the resort. The data included raw data (in .doc format), recorded data and the blackbody calibrated data (in Excel format). (7) Atmospheric parameters on the playroom roof at the resort by CE318 (produced by CIMEL in France). The underlying surface was mainly composed of crops and the forest (1526m high). 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. (8) Narrow channel emissivity of the bare land and vegetation by the W-shaped determinator in Huazhaizi desert No. 1 plot. Four circumstances should be considered for emissivity, with the lid plus the au-plating board, the au-plating board only, the lid only and without both. Data were archived in Word.
CHEN Ling, HE Tao, REN Huazhong, REN Zhixing, YAN Guangkuo, ZHANG Wuming, XU Zhen, LI Xin, GE Yingchun, SHU Lele, JIANG Xi, HUANG Chunlin, GUANG Jie, LI Li, LIU Sihan, WANG Ying, XIN Xiaozhou, ZHANG Yang, ZHOU Chunyan, LIU Xiaocheng, TAO Xin, CHEN Shaohui, LIANG Wenguang, LI Xiaoyu, CHENG Zhanhui, Liu Liangyun, YANG Tianfu
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