Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 19 dense soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them), and the widely used soil climate analysis network (SCAN). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of other SMAP products.
ZHAO Tianjie, PENG Zhiqing , YAO Panpan, SHI Jiancheng
The Antarctic McMurdo Dry Valleys ice velocity product is based on the Antarctic Ice Sheet Velocity and Mapping Project (AIV) data product, which is post-processed with advanced algorithms and numerical tools. The product is mapped using Sentinel-1/2/Landsat data and provides uniform, high-resolution (60m) ice velocity results for McMurdo Dry Valleys, covering the period from 2015 to 2020.
JIANG Liming JIANG Liming JIANG Liming
Based on the data of GF-1 and GF-2 in China, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermokast lakes and the distribution data of thermal melting landslides. The dataset can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan-Anduo section of Qinghai Tibet highway is self-made based on the domestic GF-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from GF-2 data; Then, ArcGIS is used for manual editing.
NIU Fujun, LUO Jing LUO Jing
This data set is a code file set of TCA (triple collision analysis) algorithm, which is used to generate the global daily-scale soil moisture fusion dataset from 2011 to 2018.
XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, XIE Qiuxia, JIA Li , HU Guangcheng
Pine Island Glacier, Swett Glacier, etc. are distributed in the basins of the Antarctic Ice Sheet 21 and 22, which is one of the areas with the most severe melting in the Southwest Antarctica. This dataset first uses Cryosat-2 data (August 2010 to October 2018) to establish a plane equation in each regular grid, taking into account terrain items, seasonal fluctuations, backscattering coefficients, wave front width, lifting rails and other factors, and calculates the elevation change of ice cover surface in the grid through least square regression. In addition, we used ICESat-2 data (October 2018 to December 2020) to calculate the surface elevation change during the two periods by obtaining the elevation difference at the intersection of satellite lifting orbits in each regular grid. The spatial resolution of surface elevation change data in two periods is 5km × 5km, the file format is GeoTIFF, the projection coordinate is polar stereo projection (EPSG 3031), and it is named by the name of the satellite altimetry data used. The data can be opened using ArcMap, QGIS and other software. The results show that the average elevation change rate of the region from 2010 to 2018 is -0.34 ± 0.08m/yr, which belongs to the area with severe melting. The annual average elevation change rate from October 2018 to November 2020 is -0.38 ± 0.06m/yr, which is in an intensified state compared with CryoSat-2 calculation results.
YANG Bojin , HUANG Huabing , LIANG Shuang , LI Xinwu
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
This data set includes five periods of lake transparency data, including 1995, 2002, 2005, 2010 and 2015. The data sources are: Landsat 5, Landsat 7 and Landsat 8. Method of use: It is convenient to measure the spectral reflectance. On the basis of analyzing the relationship between the spectral reflectance and the transparency measured synchronously, the semi empirical method is used to select the best band combination, establish the transparency algorithm of Qinghai Tibet Plateau lakes, and obtain the water transparency. The verification of measured points shows that the relative error of water transparency estimation is 35%.
SONG Kaishan
The fractional snow cover (FSC) is the ratio of snow cover area (SCA) to unit pixel area. The data set is made by bv-blrm snow area proportional linear regression empirical model; The source data used are mod09ga 500m global daily surface reflectance products and mod09a1 500m 8-day synthetic global surface reflectance products; The production platform uses Google Earth engine; The data range is global, the data preparation time is from 2000 to 2021, the spatial resolution is 500 meters, and the temporal resolution is year by year. This set of data can provide quantitative information of snow cover distribution for regional climate simulation and hydrological models.
MA Yuan
NDVI reflects the background effects of plant canopy, such as soil, wet ground, snow, dead leaves, roughness, etc., and is related to vegetation cover. It is one of the important parameters to reflect the crop growth and nutrient information. According to this parameter, the N demand of crops in different seasons can be known, which is an important guide to the reasonable application of N fertilizer. Correct NDVI (C-NDVI) is the value of NDVI after excluding the influence of climate elements (temperature, precipitation, etc.) on NDVI. Taking precipitation as an example, studies on the lag effect of precipitation on vegetation growth show that the lag time of precipitation effects varies in different regions due to differences in vegetation composition and soil types. In this study, we post-processed the MODIS NDVI data and firstly correlated the NDVI value of the current month with the precipitation of the current month, the average value of the precipitation of the current month with that of the previous month, and the average value of the precipitation of the current month with that of the previous two months to determine the optimal lag time. The NDVI was regressed on precipitation and air temperature to obtain the correlation coefficients, and then the corrected NDVI values were calculated by the difference between the MODIS NDVI and the NDVI regressed on climate factors. We corrected NDVI using climate data to give reliable vegetation correction indices for the circum-Arctic Circle (range north of 66°N) and the Tibetan Plateau (range 26°N to 39.85°N and 73.45°E to 104.65°E) for 2013 and 2018. The spatial resolution of the data is 0.5 degrees and the temporal resolution is monthly values.
YE Aizhong
We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.
LI Xinwu , LIANG Shuang , YANG Bojin , ZHAO Jingjing
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
Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.
YE Aizhong
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
Based on the data of Gaogao No. 1 and No. 2 in China from 2019 to 2020, the freeze-thaw disaster distribution data of Qinghai Tibet project corridor is produced by using the deep learning classification method and manual visual interpretation and correction. The geographical range of the data is 40km along the Xidatan Anduo section of Qinghai Tibet highway. The data include the distribution data of thermal melting lakes and ponds and the distribution data of thermal melting landslides. The data set can provide data basis for the research of freeze-thaw disaster and engineering disaster prevention and reduction in Qinghai Tibet engineering corridor. The spatial distribution of freezing and thawing disasters within 40km along the Xidatan Anduo section of Qinghai Tibet highway is self-made based on the domestic gaogao-2 image data. Firstly, the deep learning method is used to extract the mud flow terrace block from Gaogao No. 2 data; Then, ArcGIS is used for manual editing. During the production process, the operators are required to strictly abide by the operation specifications, and a special person is responsible for the quality review.
NIU Fujun, LUO Jing LUO Jing
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
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
YAN Dajiang, MA Ning, MA Ning, ZHANG Yinsheng
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
This data is the land cover data at 30m resolution of Southeast Asia in 2015. The data format of the data is NetCDF, and the variable name is "land cover type". The data was obtained by mosaicing and extracting the From-GLC data. Several land cover types, such as snow and ice that do not exist in Southeast Asia were eliminated.The legend were reintegrated to match the new data. The data provide information of 8 land cover types: cropland, forest, grassland, shrub, wetland, water, city and bare land. The overall accuracy of the data is 71% (Gong et al., 2019). The data can provide the land cover information of Southeast Asia for hydrological models and regional climate models.
LIU Junguo
Aiming at the 179000 km2 area of the pan three rivers parallel flow area of the Qinghai Tibet Plateau, InSAR deformation observation is carried out through three kinds of SAR data: sentinel-1 lifting orbit and palsar-1 lifting orbit. According to the obtained InSAR deformation image, it is comprehensively interpreted in combination with geomorphic and optical image features. A total of 949 active landslides below 4000m above sea level were identified. It should be noted that due to the difference of observation angle, sensitivity and observation phase of different SAR data, there are some differences in the interpretation of the same landslide with different data. The scope and boundary of the landslide need to be corrected with the help of ground and optical images. The concept of landslide InSAR recognition scale is different from the traditional spatial resolution and mainly depends on the deformation intensity. Therefore, some landslides with small scale but prominent deformation characteristics and strong integrity compared with the background can also be interpreted (with SAR intensity map, topographic shadow map and optical remote sensing image as ground object reference). The minimum interpretation area can reach several pixels. For example, a highway slope landslide with only 4 pixels is interpreted with reference to the highway along the Nujiang River.
YAO Xin
This data set is hyperspectral observation data of typical vegetation along Sichuan Tibet Railway in September 2019, using the airborne spectrometer of Dajiang M600 resonon imaging system. Including the hyperspectral data observed in the grassland area of Lhasa in 2019, with its own latitude and longitude. The hyperspectral survey was mainly sunny. Before flight, whiteboard calibration was carried out; when data were collected, there was a target (that is, the standard reflective cloth suitable for the grass), which was used for spectral calibration; there were ground mark points (that is, letters with foam plates), and the longitude and latitude coordinates of each mark were recorded for geometric precise calibration. The DN value recorded by Hyperspectral camera of UAV can be converted into reflectivity by using Spectron Pro software. Hyperspectral data is used to extract spectral characteristics of different vegetation types, vegetation classification, inversion of vegetation coverage and so on.
ZHOU Guangsheng, JI Yuhe, LV Xiaomin, SONG Xingyang
This data includes the land cover data of Central Asia, South Asia and Indochina Peninsula in the from 1992 to 2020 with a spatial resolution of 300mLand cover data includes 10 primary categories, which are combined from the secondary categories of the original data. The data source is the surface coverage product CCI-LC of ESA, where the spatial distribution of cropland, built-up land, and water for the land cover data from 1992 to 2020. Combined with the Tsinghua university global land cover data (FROM GLC, 30 m grid), NASA MODIS global land cover data (MCD12Q1, 500 m grid), the United States Geological Survey (USGS global land data (GFSAD30, 30 m), Japanese global forest data (PALSAR/PALSAR - 2, 25 m), the training sample dataset of land cover interpretation were built from the consistent areas of multiple products. The Google Earth Engine and random forest algorithm were used to correct the cropland, built-up land, and water of temporal CCI-LC data. Using the high resolution images in Google Earth at 2019 and 2020, the accuracy of change areas of cropland, built-up land, and water was validated by the stratified random sampling. A total of 3,600 land parcels were selected from 1,200 land parcels of the three land cover types, indicating that the accuracy of our corrected product increased in the range of 11% to 26% for the change areas compared to the CCI-LC product.
XU Erqi
Snow, ice, and glaciers have the highest albedo of any part of Earth's surface. The increase in melting of the polar ice sheet results in a rapid and sequential decrease in albedo and subsequently influences the global energy balance. The hydrological system derived from surface melt and basal meltwater will affect the dynamic stability of ice sheet and therefore mass balance. The dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Antarctic ice sheet surface melt daily product for 1985-1986, 2000-2001, 2015-2016 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. It better reflects the melting gradient of mountainous area, groundline area and ice shelf over time, DSSMIS has a higher accuracy. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Antarctic ice sheet, and the data set is stored as *.nc.
WEI Siyi,
The spectral characteristics of different land use types are mainly determined by spectrograph in the surface spectral data set of Qinghai Tibet Plateau. The measured ground features are mainly divided into woodland, (Alpine) shrub, (Alpine) grassland, wetland, cultivated land and bare land. It includes the field observation points in Lhasa, Linzhi, Shigatse, Ali and Naqu. The spectral characteristics of forests were measured based on the different growth stages of vegetation; The spectral characteristics of grassland were measured based on different coverage; The spectral characteristics of cultivated land were measured based on the main crop types, rape flowers and highland barley; The measurements of wetlands were conducted on the rivers, low-lying valleys and lakes; The measurements of bare lands were conducted on the desert, Gobi and roads, which have no vegetation cover. The measurement conducted from July to August in 2019, and the data is daily observation data. The data set can provide a reference for the field verification of remote sensing interpretation.
FENG Xiaoming
Based on long-term series Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products, daily snow cover products without data gaps at 500 m spatial resolution over the Tibetan Plateau from 2002 to 2021 were generated by employing a Hidden Markov Random Field (HMRF) modeling technique. This HMRF framework optimally integrates spectral, spatiotemporal, and environmental information together, which not only fills data gaps caused by frequent clouds, but also improves the accuracy of the original MODIS snow cover products. In particular, this technology incorporates solar radiation as an environmental contextual information to improve the accuracy of snow identification in mountainous areas. Validation with in situ observations and snow cover derived from Landsat-8 OLI images revealed that these new snow cover products achieved an accuracy of 98.31% and 92.44%, respectively. Specifically, the accuracy of the new snow products is higher during the snow transition period and in complex terrains with higher elevations as well as sunny slopes. These gap-free snow cover products effectively improve the spatiotemporal continuity and the low accuracy in complex terrains of the original MODIS snow products, and is thus the basis for the study of climate change and hydrological cycling in the TP.
HUANG Yan , XU Jianghui
This data is a high-resolution soil freeze/thaw (F/T) dataset in the Qinghai Tibet Engineering Corridor (QTEC) produced by fusing sentinel-1 SAR data, AMSR-2 microwave radiometer data, and MODIS LST products. Based on the newly proposed algorithm, this product provides the detection results of soil F/T state with a spatial resolution of 100 m on a monthly scale. Both meteorological stations and soil temperature stations were used for results evaluation. Based on the ground surface temperature data of four meteorological stations provided by the national meteorological network, the overall accuracy of soil F/T detection products achieved 84.63% and 77.09% for ascending and descending orbits, respectively. Based on the in-situ measured 5 cm soil temperature data near Naqu, the average overall accuracy of ascending and descending orbits are 78.58% and 76.66%. This high spatial resolution F/T product makes up traditional coarse resolution soil F/T products and provides the possibility of high-resolution soil F/T monitoring in the QTEC.
ZHOU Xin , LIU Xiuguo , ZHOU Junxiong , ZHANG Zhengjia , CHEN Qihao , XIE Qinghua
This is the 1976, 1991, 2000, and 2010 vector data set of glaciers and glacial lakes in the Boqu Basin in Central Himalaya based on Landsat satellite images. The data source is from Landsat remote images. 1976: LM21510411975306AAA05, LM21510401976355AAA04 1991: LT41410401991334XXX02, LT41410411991334XXX02 2000: LE71410402000279SGS00, LE71400412000304SGS00, LE71410402000327EDC00, LE71410412000327EDC00 2010: LT51400412009288KHC00, LT51410402009295KHC00, LT51410412009311KHC00, LT51410402011237KHC00. The boundaries of glaciers and glacial lakes are extracted manually from the various remote sensing images. The extraction error of the boundaries of glaciers and glacial lakes is estimated to be 0.5 pixels. Data file: Glacial_1976: Glacier vector data in 1976 Glacial_1991: Glacier vector data in 1991 Glacial_2000: Glacier vector data in 2000 Glacial_2010: Glacier vector data in 2010 Glacial_Lake_1976: Glacial lake vector data in 1976年 Glacial_Lake_1991: Glacial lake vector data in 1991 Glacial_Lake_2000: Glacial lake vector data in 2000 Glacial_Lake_2010: Glacial lake vector data in 2010 The glacial lake vector data fields include Number, name, latitude and longitude, altitude, area, orientation, type of glacial lake, length, width, and distance from the glacier.
WANG Weicai
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 vegetation type map was created by the random forest (RF) classification approach, based on 319 ground-truth samples, combined with a set of input variables derived from the visible, infrared, and thermal Landsat-8 images. According to vegetation characteristics, four types include alpine swamp meadow (ASM), alpine meadow (AM), alpine steppe (AS), and alpine desert (AD) were classified in this map. Based on a spatial resolution of 30 m, the map can provide more detailed vegetation information.
ZHOU Defu, ZOU Defu, ZOU Defu, Zhao Lin, ZHAO Lin, Liu Guangyue, LIU Guangyue, Du Erji, DU Erji, LI Zhibin , LI Zhibin, Wu Tonghua, WU Xiaodong, CHEN Jie CHEN Jie
Surface melting is the primary reason that affects the mass balance of Greenland ice sheet. At the same time, ice and snow have high albedo, and ice sheet surface melting will cause the difference of radiation energy budget, and then affects the energy exchange between sea-land-air. The high-resolution ice sheet surface melting product provides important information support for the study of Greenland ice sheet surface melting and its response to global climate change. This dataset combined microwave radiometer product and optical albedo product, the daily, winter (June-August) averages and July averages of the former are used for layer-stacking, then Gram-Schmidt Spectral Sharpening was adapted to fuse the layer-stacking results with MODIS GLASS albedo product. The spatial resolution of fusion-results has been downscaled from 25 km to 0.05˚. By employing a threshold-based melt detection approach for each fusion-results pixel, Greenland ice sheet surface melt daily product for 1985, 2000, 2015 (DSSMIS) was generated. The spatial resolution of DSSMIS is higher than that of published data sets at home and abroad. Combined with the advantages of radiometer and albedo data, the spatial details characteristics are enhanced and consistent with the extraction range of the original radiometer products, effectively reducing the noise of the radiometer. DSSMIS’s data type is integer, where 1 is melted, 0 is not melted, 255 is masked area besides Greenland ice sheet, and the data set is stored as *.nc.
WEI Siyi,
This dataset consists of four files including (1) Lake ice thickness of 16 large lakes measured by satellite altimeters for 1992-2019 (Altimetric LIT for 16 large lakes.xlsx); (2) Daily lake ice thickness and lake surface snow depth of 1,313 lakes with an area > 50 km2 in the Northern Hemisphere modeled by a one-dimensional remote sensing lake ice model for 2003-2018 (in NetCDF format); (3) Future lake ice thickness and surface snow depth for 2071-2099 modeled by the lake ice model with a modified ice growth module (table S1.xlsx); (4) A lookup table containing lake IDs, names, locations, and areas. This daily lake ice and snow thickness dataset could provide a benchmark for the estimation of global lake ice and snow mass, thereby improving our understanding of the ecological and economical significance of freshwater ice as well as its response to climate change.
LI Xingdong, LONG Di, HUANG Qi, ZHAO Fanyu
The dataset include ground-based passive microwave brightness temperature, multi-angle brightness temperature, ten-minute 4-component radiation and snow temperature, daily snow pit data and hourly meteorological data observed at Altay base station（lon：88.07、lat: 44.73）from November 27, 2015 to March 26, 2016. Daily snow pit parameters include: snow stratification, stratification thickness, density, particle size, temperature. These data are stored in five NetCDF files: TBdata. nc, TBdata-multiangle. nc, ten-minute 4 component radiation and snow temperature. nc, hourly meteorological and soil data. nc and daily snow pit data.nc. TBdata. nc is brightness temperature at 3 channels for both polarizations automatically collected by a six-channel dual polarized microwave radiometer RPG-6CH-DP. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. TBdata-multiangle.nc is 7 groups of multi-angle brightness temperatures at 3 channels for both polarizations. The contents include Year, month, day, hour, minute, second, Tb1h, Tb1v, Tb18h, Tb18v, Tb36h, Tb36v, incidence angle, azimuth angle. The ten-minute 4 component radiation and snow temperature.nc contains 4 component radiation and layered snow temperatures. The contents include Year, month, day, hour, minute, SR_DOWN, SR_UP, LR_DOWN, LR_UP, T_Sensor, ST_0cm, ST_5cm, ST_15cm, ST_25cm, ST_35cm, ST_45cm, ST_55cm. The hourly meteorological and soil data.nc contains hourly weather data and layered soil data. The contents include Year, month, day, hour, Tair, Wair, Pair, Win, SM_10cm, SM_20cm, Tsoil_5cm, Tsoil_10cm, Tsoil_15 cm, Tsoil_20cm. The daily snow pit data.nc. is manual snow pit data. The observation time was 8:00-10:100 am local time. The contents include Year, month, day, snow depth, thickness_layer1, thickness_layer2, thickness_layer3, thickness_layer4, thickness_layer5, thickness_layer6, Long_layer1, Short_layer1, Long_layer2, Short_layer2, Long_layer3, Short_layer3, Long_layer 4, Short_layer4, Long_layer5, Short_layer5, Long_layer6, Short_layer 6, Stube, Snow shovel_0-10, Snow shovel _10-20, Snow shovel _20-30, Snow shovel _30-40, Snow shovel _40-50, Snow fork_5, Snow fork _10, Snow fork _15, Snow fork_20, Snow fork_25, Snow fork_30, Snow fork_35, Snow fork_40, Snow fork_45, Snow fork_50, shape1, shape2, shape3, shape4, shape5,
DAI Liyun
The Tibetan Plateau Glacial Data -TPG1976 is a glacial coverage data on the Tibetan Plateau in the 1970s. It was generated by manual interpretation from Landsat MSS multispectral image data. The temporal coverage was mainly from 1972 to 1979 by 60 m spatial resolution. It involved 205 scenes of Landsat MSS/TM. There were 189 scenes（92% coverage on TP）in 1972-79，including 116 scenes in 1976/77 (61% of all the collected satellite data).As high quality of MSS data is not accessible due to cloud and snow effects in the South-east Tibetan Plateau, earlier Landsat TM data was collected for usage, including 14 scenes of 1980s（1981,1986-89,which covers 6.5% of TP） and 2 scenes in 1994（by 1.5% coverage on TP）.Among all satellite data，77% was collected in winter with the minimum effects of cloud and seasonal snow. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 1976. Glacier outlines were digitized on-screen manually from the 1976 image mosaic, relying on false-colour image composites (MSS: red, green and blue (RGB) represented by bands 321; TM: RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG1976. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG1976 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 6.4% due to the 60 m spatial resolution images.
YE Qinghua, WU Yuwei
The Tibetan Plateau Glacier Data –TPG2013 is a glacial coverage data on the Tibetan Plateau around 2013. 128 Landsat 8 Operational Land Imager (OLI) images were selected with 30-m spatial resolution, for comparability with previous and current glacier inventories. Besides, about 20 images acquired in 2014 were used to complete the full coverage of the TP. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2013. Glacier outlines were digitized on-screen manually from the 2013 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. [To minimize the effects of snow or cloud cover on glacierized areas, high-resolution (30 m spatial resolution and 4-day repetition cycle) images were also used for reference in glacier delineation from the Chinese satellites HJ-1A and HJ-1B, which were launched on Sep.6th 2008. Both carried as payload two 4-band CCD cameras with swath width 700 km (360 km per camera). All HJ-1A/1B data in 2012, 2013 and 2014 (65 scenes, Fig.S1, Table S1) were from China Centre for Resources Satellite Data and Application (CRESDA; http://www.cresda.com/n16/n92006/n92066/n98627/index.html). Each scene was orthorectified with respect to the 30m-resolution digital elevation model (DEM) of the Shuttle Radar Topography Mission (SRTM) and Landsat images.] The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery and HJ-1A/1B satellite data) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2013. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2013 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
Proba (project for on board autonomy) is the smallest earth observation satellite launched by ESA in 2001. Chris (compact high resolution imaging Spectrometer) is the most important imaging spectrophotometer on the platform of proba. It has five imaging modes. With its excellent spectral spatial resolution and multi angle advantages, it can image land, ocean and inland water respectively for different research purposes. It is the only on-board sensor in the world that can obtain hyperspectral and multi angle data at the same time. It has high spatial resolution, wide spectral range, and can collect rich information in biophysics, biochemistry, etc. At present, there are 23 scenes of proba Chris data in Heihe River Basin. The coverage and acquisition time are as follows: 4 scenes in Arjun dense observation area, 2008-11-18, 2008-12-05, 2009-03-29, 2009-05-22; 1 scene in pingdukou dense observation area, 2009-07-13; 7 scenes in Binggou basin dense observation area, 2008-11-19, 2008-11-26, 2008-12-06, 2009-01-10, 2009-03-04, 2009-03-30, 2009-03-31; dayokou basin dense observation area, 2009-07-13 There are two views in the observation area, 2008-10-23, 2009-06-08; one in Linze area, 2008-06-23; one in Minle area, 2008-10-22; seven in Yingke oasis dense observation area, 2008-04-30, 2008-05-09, 2008-06-04, 2008-07-01, 2008-07-19, 2009-05-31, 2009-08-10. The product level is L1 without geometric correction. Except that there are only four angles for the images of 2009-03-29 and 2009-05-24 in the Arjun encrypted observation area, each image has five different angles. The remote sensing data set of the comprehensive remote sensing joint experiment of Heihe River, proba Chris, was obtained through the "dragon plan" project (Project No.: 5322) (see the data use statement for details).
LI Xin
This dataset includes the Antarctica ice sheet mass balance estimated from satellite gravimetry data, April 2002 to December 2019. The satellite measured gravity data mainly come from the joint NASA/DLR mission, Gravity Recovery And Climate Exepriment (GRACE, April 2002 to June 2017), and its successor, GRACE-FO (June 2018 till present). Considering the ~1-year data gap between GRACE and GRACE-FO, we extra include gravity data estimated from GPS tracking data of ESA's Swarm 3-satellite constellation. The GRACE data used in this study are weighted mean of CSR, GFZ, JPL and OSU produced solutions. The post-processing includes: replacing GRACE degree-1, C20 and C30 spherical harmonic coefficients with SLR estimates, destriping filtering, 300-km Gaussian smoothing, GIA correction using ICE6-G_D (VM5a) model, leakage reduction using forward modeling method and ellipsoidal correction.
C.K. Shum
The Tibetan Plateau Glacier Data –TPG2017 is a glacial coverage data on the Tibetan Plateau from selected 210 scenes of Landsat 8 Operational Land Imager (OLI) images with 30-m spatial resolution from 2013 to 2018, among of which 90% was in 2017 and 85% in winter. Therefore, 2017 was defined as the reference year for the mosaic image. Glacier outlines were digitized on-screen manually from the 2017 image mosaic, relying on false-colour image composites (RGB by bands 654), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2017. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2017 if they were identifiable on images in all other three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.9%.
YE Qinghua
This is the vegetation index (NDVI) for Maduo County in July, August and September of 2016. It is obtained through calculation based on the multispectral data of GF-1. The spatial resolution is 16 m. The GF-1 data are processed by mosaicking, projection coordinating, data subsetting and other methods. The maximum synthesis is then conducted every month in July, August, and September.
LI Fei, Fei Li, Zhijun Zhang
The Tibetan Plateau Glacial Data –TPG2001 is a glacial coverage data on the Tibetan Plateau in around 2000 from 150 scenes of Landsat7 TM/ETM+ images by 30 m spatial resolution. The selected Landsat7 TM/ETM+ images were within the period between 1999 and 2002, including 61 scenes (41%) in 2001 and 47 scenes (31%) in 2000. Among all the images, 71% was taken in winter. The most frequent year in this period was defined as the reference year for the mosaic image: i.e. 2001. Glacier outlines were digitized on-screen manually from the 2001 image mosaic, relying on false-colour image composites (RGB by bands 543), which allowed us to distinguish ice/snow from cloud. Debris-free ice was distinguished from the debris and debris-covered ice by its higher reflectance. Debris-covered ice was not delineated in this data. The delineated glacier outlines were compared with band-ratio (e.g. TM3/TM5) results, and validated by overlapping them onto Google Earth imagery, SRTM DEM, topographic maps and corresponding satellite images. Topographic maps from the 1970s and all available satellite images (including Google EarthTM imagery) were used as base reference data. For areas with mountain shadows and snow cover, they were verified by different methods using data from different seasons. For glaciers in deep shadow, Google EarthTM imagery from different dates was used as the reference for manual delineation. Steep slopes or headwalls were also excluded in the TPG2001. Areas that appeared in any of these sources to have the characteristics of exposed ground/basement/bed rock were manually delineated as non-glacier, and were also cross-checked with CGI-1 and CGI-2. Steep hanging glaciers were included in TPG2001 if they were identifiable on images in all three epochs (i.e. TPG1976, TPG2001, and TPG2013). The accuracy of manual digitization was controlled within one half-pixel. All glacier areas were calculated on the WGS84 spheroid in an Albers equal-area map projection centred at (95°E, 30°N) with standard parallels at 15°N and 65°N. Our results showed that the relative deviation of manual interpretation was less than 3.8%.
YE Qinghua, WU Yuwei
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 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
This dataset includes component temperatures measured by the thermal imager at the Mixed Forest and Sidaoqiao stations between 23 July and 18 August, 2014. The Mixed Forest (101.1335 °E, 41.9903 °N, 874 m.a.s.l.) and Sidaoqiao (101.1374 °E, 42.0012 °N, 873 m.a.s.l.) stations were located in the downstream of the Heihe River basin, Dalaihubu Town, Ejin Banner, Inner Mongolia. At the Mixed Forest station, a Testo 890-2 thermal imager (Testo Inc., Germany) with a resolution of 640 × 480 pixels was employed to acquire brightness temperature images. The imager was manually operated from a 10-m height platform of the tower between 10:00-16:00 (China Standard Time, CST) with an observation interval of 1-h on cloudless days. On August 4th observations were acquired between 11:00 and 17:00 at an interval of 10-min to match observations acquired with an airborne TIR imager. The ground based imager was pointed to five viewing directions (southeast-SE, east-E, northeast-NE, northwest-NW, and southwest-SW) and was inclined 25°–45° below the horizon depending on viewing direction. At Sidaoqiao station, a Testo 875-2i imager (Testo Inc., Germany) with a resolution of 160 × 120 pixels was manually operated from a 10-m high platform to acquire brightness temperature images in directions SW, SE, NE, and NW. Depending on the targets in each viewing direction, the imager was inclined to 30°–45° below the horizon. Observations at Sidaoqiao and Mixed Forest stations were almost synchronous. Furthermore, visible images were taken simultaneously with the aforementioned two TIR imagers (2048 × 1536 pixels for Testo 890-2 and 640 × 480 pixels for Testo 875-2i).
LI Mingsong , MA Jin
The permafrost stability map was created based on the classification system proposed by Guodong Cheng (1984), which mainly depended on the inter-annual variation of deep soil temperature. By using the geographical weighted regression method, many auxiliary data was fusion in the map, such as average soil temperature, snow cover days, GLASS LAI, soil texture and organic from SoilGrids250, soil moisture products from CLDAS of CMA, and FY2/EMSIP precipitation products. The permafrost stability data spatial resolution is 1km and represents the status around 2010. The following table is the permafrost stability classification system. The data format is Arcgis Raster.
RAN Youhua
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
On July 23, 1972, the United States launched the world's first resource satellite "Landsat 1" , and Landsat 2 and Landsat 3 were launched in the following 10 years. These three satellites were the first generation of resource satellites. They were equipped withreturn-beam vidicon cameras and multi-spectral scanners (MSS) with 3 and 4 spectral segments respectively, a resolution of 79m and a width of 185Km. There are 28 scenes of MSS data in Heihe River Basin currently which were obtained on the following dates: 1972-10-14, 1972-10-30, 1973-01-10, 1973-01-31, 1973-02-16, 1973-06-04, 1973. -10-07, 1973-10-28 (2 scenes), 1973-12-22, 1974-01-05, 1975-10-07, 1975-10-09, 1976-07-04, 1976-10-18 , 1976-11-07, 1976-11-27, 1976-12-30, 1977-01-19, 1977-02-07, 1977-04-20, 1977-05-06 (2 scenes), 1977-05 -08, 1977-06-10, 1977-06-29, 1977-07-18, 1978-10-09. Ortho rectification was performed on the images.
LP DAAC User Services
Eo-1 (Earth Observing Mission) is a new Earth Observing satellite developed by NASA to replace Landsat7 in the 21st century. It was launched on November 21, 2000.The orbit of eo-1 satellite is basically the same as that of Landsat7, which is a solar synchronous orbit with an orbital altitude of 705km and an inclination Angle of 98.7°, which is 1min less than that of Landsat7 and crosses the equator.On board of EO 1 3 kinds of sensors, namely, the Advanced Land Imager (ALI (the Advanced Land Imager), atmospheric correction instrument AC (Atmosp heric Corrector) and compose a specular as spectrometer (Hyperion), Hyperion sensor is first spaceborne hyperspectral mapping measurement instrument, the hyperspectral data a total of 242 bands, spectral range is 400 ~ 2500 nm, spectral resolution up to 10 nm, ground resolution of 30 m. Currently, there are 6 scenes of eo-1 Hyperion data in heihe river basin.The coverage and acquisition time were: 4 scenes in the encrypted observation area of zhangye urban area + yingke oasis encrypted observation area (2007-09-10, 2008-05-12, 2008-05-20, 2008-07-15).Two scenes of the iceditch watershed observation area were encrypted, the time was 2008-03-17, 2008-03-22, respectively. Product grade is L1 without geometric correction. The eo-1 Hyperion remote sensing data set of heihe integrated remote sensing joint experiment was acquired by researcher wang jian and Beijing normal university through purchase. (note: "+" represents simultaneous coverage)
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
The spot satellite series in France consists of five stars, of which spot 5 is the best. It was launched in May 2002, with a height of 830km, an orbit inclination of 98.7 degrees, and a sun synchronous quasi regression orbit, with a regression period of 26 days. Linear array sensor (CCD) and push scan scanning technology were used for imaging. SPOT5 satellite carries two high-resolution geometric imagers (HRG), one high-resolution Stereo Imager (HRS) and one wide field vegetation detector (VGT). It has five working bands, multi spectral band spatial resolution is 10m (short wave infrared spatial resolution is 20m), panchromatic band spatial resolution is 2.5m. At present, there are three spots of SPOT5 data in Heihe River Basin. The coverage and acquisition time are respectively: 1 scene in Linze area, including multispectral image with resolution of 10m and panchromatic image with resolution of 2.5m, with time of 2008-07-04; 1 scene in Zhangye City, with resolution of 2.5m, with time of 2008-03-29; 1 scene of multispectral data with resolution of 10m, with time of 2008-08-10. The product level is L1, and the product has undergone rough geometric correction. SPOT5 image is mainly used as the base map of geometric precision correction in Heihe experiment. The spot 5 remote sensing data set of Heihe comprehensive remote sensing joint experiment was purchased by Beijing Normal University.
Institute of Remote Sensing and Digital earth, Chinese Academy of Sciences
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
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 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 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
ALOS PRISM dataset includes 13 scenes; one covers the A'rou foci experimental area on Mar. 19, 2008, one covers the Haichaoba on Mar. 19, 2008, one covers the Biandukou foci experimental area on Apr. 17, 2008, and one covers the Linze grassland and Linze station foci experimental areas on Apr. 22, 2008. The data version is LB2, which was released after radiometric correction and geometric correction.
Japan Aerospace Exploration Agency
Ⅰ. Overview Landsat5 was launched in March 1984. The Thematic Mapper (TM) sensor on it includes seven bands, except for the 6th band with a resolution of 120 m, the other 6 bands have a resolution of 30 m. This data set was collected in 1990 and 2010. There are 77 scenes of TM data in the upper reaches of the Yellow River. Ⅱ. Data processing description The product level is L1 and has been geometrically corrected. Ⅲ. Data content description The naming method is LT5 line number column number _ column number year month day, such as LT5129032_03220040816. Ⅳ. Data usage description The main applications are soil use / cover and desertification monitoring.
XUE Xian, DU Heqiang
Ⅰ. Overview Landsat5 was launched in April 1999. As a supplement and enhancement to the Landsat series, it carries an EMT+ sensor. The parameters of each band are close to that of Landsat5, but the panchromatic band with a resolution of 15 m is added, and the resolution of thermal infrared band is increased to 60 m.This dataset was collected in 1999-2010. There were 97 scenes of TM data in the upper reaches of the Yellow River. Due to sensor damage, there were bands in the images. Ⅱ. Data processing description Product level is L1 and has been geometrically corrected. Ⅲ. Data content description The naming method is L5 and row number and column number _ column number and date (yyyymmdd), such as L75129032_03220040816. Ⅳ. Data usage description The main applications are soil use/cover and desertification monitoring.
XUE Xian, DU Heqiang
The Landsat TM Mosaic Image of the Heihe River Basin can be effectively applied to monitoring land-use change of the basin, which reflects the current situation of the Heihe River Basin in 2010, and provides a reliable basis for ecological planning and restoration. This mosaic image collected the TM images released by the USGS for free in 2010 (data from July to September 2010, totally 21 scenes, the maximum cloud amount is less than 10%), and the preprocessed images were geometrically registered by topographic maps(polynomial geometry correction method), then a geometrically-corrected digital mosaic map was generated, which was of high quality after a certain accuracy evaluation. The images were stored in ERDAS IMG format, and the most abundant bands 5, 4 and 3 combination, with three colors: red, green, and blue were selected to generate a color composite image. The combined composite image not only is similar to natural color, which is more in accordance with people's visual habits, but also can fully display the differences in image features because of the rich amount of information.
LP DAAC User Services
Terra (EOS am-1), the flagship of the EOS earth observation series, was the first satellite to be launched on December 18, 1999.ASTER is primarily used for high-resolution observations of surface radiation balance. Compared with Landsat series satellites, ASTER has improved spectral and spatial resolution, and significantly increased short-wave infrared and thermal infrared bands.ASTER has a total of 14 wavebands, including 3 visible and near-infrared wavebands, 5 short-wave infrared wavebands and 5 thermal infrared wavebands. The resolution is 15m, 30m and 90m respectively, and the scanning width is 60km, 30m and 90m respectively.Heihe river basin ASTER remote sensing image data set through the international cooperation data from NASA's web site (https://wist.echo.nasa.gov/). Data naming rules as follows: assuming that the name of the ASTER image for "ASTL1B0103190215190103290064", then ASTL1B said ASTER L1B products, 003 on behalf of the version number namely VersionID, (010319) represents the next 6 digits observation date will be March 19, 2001, followed by six digits (021519) represents the observation time (02:15:19), followed by the last six digits (010329) representing the processing date is March 29, 2001, the last four digits (0064) representing the four-digit sequence code. At present, there are 258 scents of ASTER data in heihe river basin.The acquisition time is:2000-04-25, 2000-04-27 (2 scenes), 2000-05-04, 2000-05-15 (4 scenes), 2000-05-20 (9 scenes), 2000-05-29 (3 scenes), 2000-05-31 (2 scenes), 2000-06-12, 2000-06-14 (5 scenes), 2000-06-21 (3 scenes), 2000-06-30 (8 scenes), 2000-07-18, 2000-07-23 (3 scenes), 2000-08-03 (4 scenes),2000-08-08 (9 scenes), 2000-08-17 (7 scenes), 2000-08-19 (4 scenes), 2000-08-26 (3 scenes), 2000-09-02 (4 scenes), 2000-10-02 (7 scenes), 2000-10-04 (6 scenes), 2000-10-29 (3 scenes), 2000-11-21, 2001-02-18 (2 scenes), 2001-02-25, 2001-03-11 (5 scenes), 2001-03-22 (4 scenes),2001-03-27 (4 scenes), 2001-03-29 (9 scenes), 2001-04-07 (2 scenes), 2001-04-12 (2 scenes), 2001-04-14 (6 scenes), 2001-07-10, 2001-07-12 (8 scenes), 2001-07-21 (8 scenes), 2001-08-13 (8 scenes), 2001-08-20 (7 scenes), 2001-08-22, 2001-08-27 (2 scenes), 2001-08-29,2001-09-03 (2 scenes), 2001-11-15 (7 scenes), 2002-02-01, 2002-03-30 (2 scenes), 2002-04-17 (2 scenes), 2002-05-24, 2002-06-04 (6 scenes), 2002-06-09, 2002-06-13, 2002-06-25, 2002-08-14 (3 scenes), 2002-09-29, 2002-10-19 (2 scenes), 2002-11-11 (2 scenes),2002-12-29 (4 scenes), 2003-04-18, 2003-05-24 (2 scenes), 2003-07-25, 2003-07-30, 2003-8-10 (5 scenes), 2003-08-12, 2003-08-17, 2003-09-09 (11 scenes), 2003-09-13 (4 scenes), 2003-10-15, 2003-10-18, 2003-10-29 (9 scenes), 2003-11-30, 2004-03-14, 2005-03-20,2005-06-05, 2005-08-11, 2007-10-22, 2007-11-14, 2007-11-23, 2007-12-04, 2008-01-28, 2008-02-13, 2008-05-03 (4 scenes), 2008-05-05, 2008-05-17, 2008-06-04 (2 scenes), 2008-06-13.
National Aeronautics and Space Administration
Landsat 5 was launched in March 1984 and has been in orbit for 16 years. The thematic mapper (TM) sensor on Landsat 5 consists of seven bands, all of which have a resolution of 30m except for band 6, which has a resolution of 120m. Currently, there are 23 TM data sets in heihe river basin.The obtained time was 1987-08-15, 1987-09-14, 1987-10-09, 1988-06-28, 1989-05-09, 1990-07-30, 1990-08-21 (2 scenes), 1990-08-28, 1990-08-30, 1990-09-15 (2 scenes), 1991-09-02, 1995-08-19, 1995-08-21, 2002-06-13,2003-09-12, 2007-09-23, 2008-03-17, 2008-07-07, 2008-07-23. The product is class L1 and has been geometrically corrected.
LP DAAC User Services
In April 1999, Landsat 7 was launched. As a supplement and enhancement to the Landsat series, the sensor it carried was ETM+. The parameters of each band were close to those of Landsat 5, but the resolution of panchromatic band with a resolution of 15m was added, and the resolution of thermal infrared band was improved to 60m. At present, there are 85 ETM + data scenes in heihe river basin.Data acquisition time is 1999-07-07, 1999-09-23 (2 scenes), 1999-10-18, 1999-11-26, 2000-01-20, 2000-04-20, 2000-05-06 (2 scenes), 2000-05-20, 2000-06-14 (2 scenes), 2000-07-07 (2 scenes), 2000-07-08, 2000-08-10, 2000-10-02, 2000-10-11,2000-10-13, 2001-05-25, 2001-07-03, 2001-08-20 (2 king), 2001-10-23, 2002-05-03, 2002-05-28, 2002-06-13, 2002-06-29, 2002-07-24, 2004-12-11, 2005-07-23, 2005-09-09, 2005-10-09, 2006-05-07,2006-05-21, 2006-06-24, 2006-07-26, 2006-08-25, 2006-12-01, 2007-08-12, 2008-01-05, 2008-02-06, 2008-03-25, 2008-05-10, 2008-05-19, 2008-05-28, 2008-06-04, 2008-07-15 (2 scenes), 2008-07-22, 2008-08-16 (4 scenes),2008-08-30, 2008-09-08, 2008-09-15, 2008-09-17, 2008-10-01, 2008-10-10 (2 scenes), 2008-10-19 (3 scenes), 2008-10-26 (3 scenes), 2008-11-02, 2008-11-04 (4 scenes), 2008-11-18, 2008-11-20 (4 scenes), 2008-11-27 (3 scenes), 2008-12-04, 2008-12-062008-12-13 (3 scenes).
LP DAAC User Services
This dataset includes: remote sensing data _ETM around 2000 in Western China; Data attributes: Pixel Size: 15-meter panchromatic: Band 8 30-meter: Bands 1-5 and Band 7 60-meter: Bands 6H and 6L Resampling Method: Cubic Convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) The data was downloaded from USGS: http://glovis.usgs.gov/ImgViewer/Java2ImgViewer.html?lat=38.3&lon=78.9&mission=LANDSAT&sensor=ETM. Part of the remote sensing images collected from various research projects. The folder contains ETM 8 band images (* .tif) and header files (* .met). The naming format of image files is row and column number _ETM image logo (7k, 7x, 7t), image acquisition time _ image 6 degree band number _ band number. The data also includes an image index map in shp format.
EROS DATA CENTER
Snow cover dataset is produced by snow and cloud identification method based on optical instrument observation data, covering the time from 1989 to 2018 (two periods, from January to April and from October to December) and the region of Qinghai-Tibet Plateau (17°N-41°N, 65°E-106°E) with daily product, which takes equal latitude and longitude projection with 0.01°×0.01° spatial resolution, and characterizes whether the ground under clear sky or transparent thin cloud is covered by snow. The input data sources include AVHRR L1 data of NOAA and MetOp serials of satellites, and L1 data corresponding to AVHRR channels taken from TERRA/MODIS. Decision Tree algorithm (DT) with dynamic thresholds is employed independent of cloud mask and its cloud detection emphasizes on reserving snow, particularly under transparency cirrus. It considers a variety of methods for different situations, such as ice-cloud over the water-cloud, snow in forest and sand, thin snow or melting snow, etc. Besides those, setting dynamic threshold based on land-surface type, DEM and season variation, deleting false snow in low latitude forest covered by heavy aerosol or soot, referring to maximum monthly snowlines and minimum snow surface brightness temperature, and optimizing discrimination program, these techniques all contribute to DT. DT discriminates most snow and cloud under normal circumstances, but underestimates snow on the Qinghai-Tibet Plateau in October. Daily product achieves about 95% average coincidence rate of snow and non-snow identification compared to ground-based snow depth observation in years. The dataset is stored in the standard HDF4 files each having two SDSs of snow cover and quality code with the dimensions of 4100-column and 2400-line. Complete attribute descriptions is written in them.
ZHENG Zhaojun, CHU Duo
This dataset includes five scenes, covering the artificial oasis eco-hydrology experimental area of the Heihe River Basin, which were acquired on (yy-mm-dd) 2012-04-05, 2012-04-21, 2012-05-07, 2012-06-24, 2012-07-10. The data were all acquired around 11:50 (BJT) with data product of Level 2. Landsat ETM+ dataset was downloaded from http://glovis.usgs.gov/.
United States Geological Survey (USGS) UitedStateGeologicalSurvey UitedStateGeologicalSurvey
Under the background of global warming, mountain glaciers worldwide are facing strong ablation and retreat, but from existing field observations, it is found that most of the glaciers in the Karakorum region remain stable or are advancing, which is called the ＂Karakorum anomaly＂. Glacier surface velocity is an important parameter for studying glacier dynamics and mass balance. Studying the temporal and spatial variation characteristics of glacier velocity in central Karakorum is significant for understanding the dynamic characteristics of the glacier in this region and its response to climate change. Four pairs of Landsat 7 ETM+ images acquired in 1999 to 2003 (images acquired on 1999.7.16, 2000.6.16, 2001.7.21, 2002.8.9, 2002.4.19, 2003.3.21) were selected; using the panchromatic band with a resolution of 15 m, each pair of images was accurately registered, and then cross-correlation calculations were then performed on each image pair after registration to obtain the surface velocity of the glacier in the central Karakorum region from 1999 to 2003. Due to the lack of velocity observation data in the study area, the accuracy of the ice flow results is estimated using the offset value of the stable region, and the surface velocity error of the glacier is approximately ±7 m/year. The glacier velocity data dates are from 1999 to 2003, with a temporal resolution of one year. They cover the central Karakorum region, with a spatial resolution of 30 m. The data are stored as a GeoTIFF file every year. For details regarding the data, please refer to the data description.
JIANG Liming
The dataset covers western China. MSS remote sensing images Dataset properties: Pixel Size: 60m Reflective bands 4-7 (Landsat 1-3) and Bands 1-4 (Landsat 4-5) Output Format: GeoTIFF Resampling method: cubic convolution (CC) Map Projection: UTM – WGS 84 Polar Stereographic for the continent of Antarctica. Image Orientation: Map (North Up) Data sources were partially downloaded from http://eros.usgs.gov/ and some were collected from various projects. The data folder is named the row and column number where the image is located. The folder contains the MSS 4 band images (* .tif), header files (* .met, * .hdr), and thumbnails (jpg). The naming format of image files is row and column number_TM image mark (2m), and image acquisition time_band number. It is mainly used for thematic analysis and compilation of different scale thematic maps on agriculture, forestry, water, soil, geology, geography, geography, surveying and mapping, regional planning, and environmental monitoring.
EROS DATA CENTER
QuickBird satellite was launched by Digital Globe corporation on October 18, 2001. It has 4 multi-spectral bands and 1 panchromatic band, with a spatial resolution of 0.61m for panchromatic bands and a spatial resolution of 2.5m for multi-spectral bands and a width of 16.5 * 16.5 km. There are two QuickBird remote sensing images in heihe river basin.The acquisition time and coverage were: 2004-03-23, covering zhangye area;2004-08-08, covering danokou and drainage ditch drainage basin. The product level is level L2 and has been geometrically corrected by the system.
LI Xin, GUO Jianwen
On August 2, 2012, airborne ground synchronous observation was carried out in plmr quadrats of Yingke oasis and huazhaizi desert. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the transition zone between the southern edge of Zhangye Oasis and anyangtan desert, on the west side of Zhangye Daman highway, and across the trunk canal of Longqu in the north and the south, which is divided into two parts. In the southwest, there is a 1 km × 1 km desert quadrat. Because the desert is relatively homogeneous, here 1 The soil moisture of 5 points (1 point and center point around each side, and several more points can be measured during walking along the road in the actual measurement process) is collected in KM quadrat. The four corner points are 600 m apart from each other except the diagonal direction. The southwest corner point is huazhaizi desert station, which is convenient to compare with the data of meteorological station. On the northeast side, a large sample with an area of 1.6km × 1.6km was selected to carry out synchronous observation on the underlying surface of oasis. The selection of quadrat is mainly based on the consideration of the representativeness of surface coverage, avoiding residential buildings and greenhouses as much as possible, crossing oasis farmland and some deserts in the south, accessibility, and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 11 splines (east-west distribution) were collected at the interval of 160 m in the east-west direction. Each line has 21 points (north-south direction) at the interval of 80 M. four hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 230 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. No synchronous vegetation sampling was carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
On July 3, 2012, airborne ground synchronous observation was carried out in plmr sample belt near Linze station. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: According to the typical ground surface type represented by three points near Linze station and taking part of neutron tube observation into account, the three routes from northwest to southeast are designed, with an interval of 200 m, a design altitude of about 300 m and a plmr ground resolution of 100 m. According to the observation characteristics of the route and plmr, three observation transects are designed on both sides of the route, each of which is about 6 km long. From west to East are L1, L2 and L3 respectively. Among them, L1 and L2 are centered on the middle route, 80 m apart; L2 and L3 are 200 m apart. Four hydroprobe data acquisition systems (HDAS, ref. 2) were used to measure at the same time. Measurement content: About 4500 points on the sample belt were obtained, each point was observed twice, that is to say, in each sampling point, once in the film (marked as a in the data record) and once out of the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and virtual part of soil complex dielectric are observed. Vegetation parameter observation was carried out in some representative soil water sampling points, and the measurement of plant height and biomass (vegetation water content) was completed. Note: the observation date coincides with the irrigation of large area of farmland in this area, which makes it difficult for the observer to move forward, the field block is difficult to enter, and the observation point position deviates from the preset point position. Data: This data set includes two parts: soil moisture observation and vegetation observation. The former saves the data format as a vector file, the spatial location is the location of each sampling point (WGS84 + UTM 47N), and the measurement information of soil moisture is recorded in the attribute file; the vegetation sampling information is recorded in the excel table.
WANG Shuguo, MA Mingguo, LI Xin
During lidar and widas flight in summer 2012, the ground synchronously carried out the continuous observation of differential GPS of ground base station, and obtained the synchronous GPS static observation data, which is used to support the synchronous solution of aviation flight data. Measuring instrument: Two sets of triple R8 GNSS system. Zgp8001 sets Time and place of measurement: On July 19, 2012, EC matrix lidar flew and observed at mjwxb (northwest of Maojiawan) and sbmz (shibamin) two base stations at the same time On July 25, 2012, lidar of hulugou small watershed and tianmuchi small watershed in the upper reaches flew, observed in XT Xiatang, lidar of Zhangye City calibration field in the middle reaches, and observed in mjwxb (northwest of Maojiawan) On July 26, 2012, lidar flight of hulugou small watershed and tianmuchi small watershed in the upper reaches was observed in XT Xiatang, lidar flight of Zhangye City calibration field in the middle reaches was observed in HCZ (railway station) On August 1, 2012, the upper east and West branches of widas flew and observed in yng (yeniugou) On August 2, 2012, the midstream EC matrix test area widas flew and observed in HCZ (railway station) On August 3, 2012, the midstream EC matrix test area widas flew and observed in mjwxb (northwest Maojiawan) Data format: Original data format before differential preprocessing.
LIU Xiangfeng, MA Mingguo
This data includes the coverage data set of vegetation in one growth cycle in five stations of Daman super station, wetland, desert, desert and Gobi, and the biomass data set of maize and wetland reed in one growth cycle in Daman super station. The observation time starts from May 10, 2014 and ends on September 11, 2014. 1 coverage observation 1.1 observation time 1.1.1 super station: the observation period is from May 10 to September 11, 2014. Before July 20, the observation is once every five days. After July 20, the observation is once every 10 days. A total of 17 observations are made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 1.1.2 other four stations: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; Other four stations: May 10, 2014, May 20, 2014, May 30, 2014, June 10, 2014, June 20, 2014, June 30, July 10, 2014, July 20, August 5, 2014, August 17, 2014, September 11, 2014 1.2 observation method 1.2.1 measuring instruments and principles: The digital camera is placed on the instrument platform at the front end of the simple support pole to keep the shooting vertical and downward and remotely control the camera measurement data. The observation frame can be used to change the shooting height of the camera and realize targeted measurement for different types of vegetation. 1.2.2 design of sample Super station: take 3 plots in total, the sample size of each plot is 10 × 10 meters, take photos along two diagonal lines in turn each time, take 9-10 photos in total; Wetland station: take 2 sample plots, each plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 3 other stations: select 1 sample plot, each sample plot is 10 × 10 meters in size, and take 9-10 photos for each survey; 1.2.3 shooting method For the super station corn and wetland station reed, the observation frame is directly used to ensure that the camera on the observation frame is far higher than the vegetation crown height. Samples are taken along the diagonal in the square quadrat, and then the arithmetic average is made. In the case of a small field angle (< 30 °), the field of view includes more than 2 ridges with a full cycle, and the side length of the photo is parallel to the ridge; in the other three sites, due to the relatively low vegetation, the camera is directly used to take pictures vertically downward (without using the bracket). 1.2.4 coverage calculation The coverage calculation is completed by Beijing Normal University, and an automatic classification method is adopted. For details, see article 1 of "recommended references". By transforming RGB color space to lab space which is easier to distinguish green vegetation, the histogram of green component A is clustered to separate green vegetation and non green background, and the vegetation coverage of a single photo is obtained. The advantage of this method lies in its simple algorithm, easy to implement and high degree of automation and precision. In the future, more rapid, automatic and accurate classification methods are needed to maximize the advantages of digital camera methods. 2 biomass observation 2.1 observation time 2.1.1 corn: the observation period is from May 10 to September 11, 2014, once every 5 days before July 20, and once every 10 days after July 20. A total of 17 observations have been made. The specific observation time is as follows:; Super stations: May 10, 15, 20, 25, 30, 10, 15, 20, 20, 30, 30, 30, 30, 30, 7, 10, 10, 10, 10, 10, 15 2.1.2 Reed: the observation period is from May 20 to September 15, 2014, once every 10 days, and 11 observations have been made in total. The specific observation time is as follows:; 2014-5-10、2014-5-20、2014-5-30、2014-6-10、2014-6-20、2014-6-30、2014-7-10、2014-7-20、2014-8-5、2014-8-17、2014-9-11 2.2 observation method Corn: select three sample plots, and select three corn plants that represent the average level of each sample plot for each observation, respectively weigh the fresh weight (aboveground biomass + underground biomass) and the corresponding dry weight (85 ℃ constant temperature drying), and calculate the biomass of unit area corn according to the plant spacing and row spacing; Reed: set two 0.5m × 0.5m quadrats, cut them in the same place, and weigh the fresh weight (stem and leaf) and dry weight (constant temperature drying at 85 ℃) of reed respectively. 2.3 observation instruments Balance (accuracy 0.01g), oven. 3 data storage All the observation data were recorded in the excel table first, and then stored in the excel table. At the same time, the data of corn planting structure was sorted out, including the plant spacing, row spacing, planting time, irrigation time, except for the parent time, harvesting time and other relevant information.
YU Wenping, GENG Liying, Li Yimeng, TAN Junlei, MA Mingguo
This data set includes the continuous observation data set of soil texture, roughness and surface temperature measured by the vehicle borne microwave radiometer on November 15-16, 2013 in the farmland of jiushe, Kangning, Zhangye City, Gansu Province. The surface temperature includes the soil temperature data observed by the temperature sensor at the soil depth of 0 cm, 1 cm, 3 cm, 5 cm and 10 cm. The time frequency of conventional observation of soil temperature is 5 minutes. Data details: 1. Time: November 15-16, 2013 2. data: Bright temperature: observed by vehicle mounted multi frequency passive microwave radiometer, including 6.925, 18.7 and 36.5ghz v-polarization and H-polarization data (10.65ghz band instrument damaged) Soil temperature: use the sensor installed on dt85 to measure the soil temperature of 0cm, 1cm, 3cm, 5cm and 10cm Soil texture: soil samples measured in Beijing Normal University Soil roughness: measured by roughness meter provided by northeast geography 3. Data size: 4.8m 4. Data format:. Xls
ZHAO Shaojie, KOU Xiaokang, YE Qinyu, MA Mingguo
On July 26, 2012, the airborne ground synchronous observation was carried out in the plmr quadrat in the dense observation area of Daman. Plmr (polarimetric L-band multibeam radiometer) is a dual polarized (H / V) L-band microwave radiometer, with a center frequency of 1.413 GHz, a bandwidth of 24 MHz, a resolution of 1 km (relative altitude of 3 km), six beam simultaneous observations, an incidence angle of ± 7 °, ± 21.5 °, ± 38.5 °, and a sensitivity of < 1K. The flight mainly covers the middle reaches of the artificial oasis eco hydrological experimental area. The local synchronous data set can provide the basic ground data set for the development and verification of passive microwave remote sensing soil moisture inversion algorithm. Quadrat and sampling strategy: The observation area is located in the matrix of the dense observation area of Daman, and the detailed plan with an area of 3.0KM × 2.4km is selected to carry out synchronous observation on the underlying surface of oasis. The selection of the sample is mainly based on the representativeness of the surface coverage, accessibility and observation (road consumption) time, so as to obtain the comparison of brightness and temperature with plmr observation. Considering the resolution of plmr observation, 5 splines (east-west distribution) were collected at an interval of 450 m in the east-west direction. Each line has 31 points (north-south direction) at an interval of 100 m, and 5 hydraprobe data acquisition systems (HDAS, reference 2) were used for simultaneous measurement. Measurement content: About 150 points on the quadrat were obtained, each point was observed twice, that is to say, two times were observed at each sampling point, one time was inside the film (marked as a in the data record) and one time was outside the film (marked as B in the data record). As the HDAS system uses pogo portable soil sensor, the soil temperature, soil moisture (volume moisture content), loss tangent, soil conductivity, real part and imaginary part of soil complex dielectric are observed. Because the vegetation in this area has been sampled and observed once every five days, no special vegetation synchronous sampling has been carried out on that day. Data: This data set consists of two parts: soil moisture observation and vegetation observation. The former saves data in vector file format, and the spatial location is the location of each sampling point (WGS84 + UTM 47N). Soil moisture and other measurement information are recorded in attribute file.
WANG Shuguo, MA Mingguo, LI Xin
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
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
This dataset contains three basic remote sensing data of digital topography (DEM), TM remote sensing image and NDVI vegetation index of badan jilin desert. 1. DEM, digital terrain data, from the SRTM1 data set released by NASA in the United States, was cropped in the desert area.The resolution is 30 m.The data is stored in the DEM folder, and the dm.ovr file can be opened by ArcGIS. 2. TM image data.The composite data of Landsat TM/ETM + 543 band released by NASA were cropped in the desert lake group distribution area.The resolution is 30 m.From 1990 to 2010, one scene was selected in summer and one scene in autumn every five years to analyze the long-term changes of the lake.In 2002, there was a scene for each quarter to analyze the changes of the lake during the year.The data is stored in TM folder, TIFF format, can be opened by ArcGIS or ENVI software.The file naming rule is yyyymm.tif, where yyyy refers to the year and mm to the month. For example, 199009 refers to the time corresponding to the impact data of September 1990. 3. NDVI, vegetation index.The modis-ndvi product MOD13Q1, released by NASA, was cropped in desert areas.The NDVI data of every ten days of the growing season (June, July, August and September) from 2000 to 2012 are included. The spatial resolution is 250 m and the temporal resolution is 16 days.Stored in NDVI folder, TIFF format, can be opened by ArcGIS or ENVI software.Mosaic_tmp_yyyyddd.hdfout.250m_16_days_ndvi_roi.tif, Where yyyy represents the year and DDD represents the day of DDD of the year.
JIN Xiaomei, HU Xiaonong
This data set was acquired by L & K band airborne microwave radiometer on July 8, 2008 in Linze flight area. The frequency of L-band is 1.4GHz, and the backsight is 35 degrees to obtain dual polarization (H and V) information; the frequency of K-band is 18.7ghz, and there is no polarization information. The plane took off from Zhangye airport at 10:00 (Beijing time, the same below) and landed at 13:38. 10: At 23-13:10, the flight altitude was about 1900m and the flight speed was about 230-250km / hr. Among them, 12:21-12:27 low flying Linze reservoir line 1-6 has a relative altitude of 100m and a flight speed of 190km / hr. 12: 56-13:08 low flying desert marking twice (line 1-7, first North to south, then south to North). The original data is divided into two parts: microwave radiometer data and GPS data. The L and K bands of microwave radiometer are non imaging observations. The digital values obtained from the instantaneous observation are recorded in the text file, and the longitude and latitude as well as the aircraft attitude parameters are recorded in the GPS data. When using microwave radiometer to observe data, it is necessary to convert the digital value recorded into the bright temperature value according to the calibration coefficient (the calibration coefficient file is filed with the original observation data). At the same time, through the clock records of microwave radiometer and GPS, we can connect the microwave observation with GPS record and match the geographic coordinate information for the microwave observation. Due to the coarse observation resolution of microwave radiometer, the effects of aircraft yaw, roll and pitch are generally ignored in data processing. According to the target and flight relative altitude (H), after calibration and coordinate matching, the observation information can also be gridded. The resolution (x) of L band and K band is consistent with that of observation footprint. The reference resolution is: L band, x = 0.3H; K band, x = 0.24h. After the above steps, we can get the products that users can use directly.
WANG Shuguo, HAO Xiaohua, CHE Tao, XIAO Qing, Liu Qiang, ZHAO Kai, JIN Jinan
This data set was acquired by K & Ka band airborne microwave radiometer on March 29, 2008, in the Binggou watershed flight zone. Among them, K-band frequency is 18.7ghz, zenith angle observation, no polarization information; Ka band frequency is 36.0ghz, scanning imaging, scanning range ± 12 °, vertical polarization observation. The plane took off from Zhangye airport at 8:49 (Beijing time, the same below) and landed at 12:54. 9: At 25-12:08, 18 routes were flown according to the scheduled design, with a flight altitude of about 5000m and a flight speed of about 220-250km / hr. The original data is divided into two parts: microwave radiometer data and GPS data. The K-band of microwave radiometer belongs to non imaging observation, and the digital value obtained from instantaneous observation is recorded in the text file. Ka band belongs to imaging observation, which is different from L band and K band data. The original record of Ka band is hexadecimal text file. In data processing, the hexadecimal file needs to be converted to decimal system first, and then 112 data (the angle difference of each two data points is 24 / 112 = 0.214 degrees) are collected uniformly within the scanning range of 24 degrees. GPS data record the latitude and longitude of the flight and the aircraft attitude parameters. When using microwave radiometer to observe data, it is necessary to convert the digital value recorded into the bright temperature value according to the calibration coefficient (the calibration coefficient file is filed with the original observation data). At the same time, through the clock records of microwave radiometer and GPS, microwave observation and GPS record can be linked to match the geographical coordinate information for microwave observation. When processing Ka band data, the angle scanning effect should also be considered, and 112 data in the scanning period should be given geographical coordinate information respectively. Due to the coarse observation resolution of microwave radiometer, the effects of aircraft yaw, roll and pitch are generally ignored in data processing. According to the target and flight relative altitude (H), after calibration and coordinate matching, the observation information can also be gridded. The resolution (x) of K-band is consistent with that of observation footprint. The reference resolution is: x = 0.24h; the resolution of Ka band is 39m. After the above steps, we can get the products that users can use directly.
WANG Shuguo, WANG Xufeng, CHE Tao, ZHAO Kai, JIN Jinan, XIAO Qing, Liu Qiang
This data set was acquired by L & K band airborne microwave radiometer on July 4, 2008, in the Biandukou-Linze flight zone. The frequency of L-band is 1.4GHz, and the backsight is 35 degrees to obtain dual polarization (H and V) information; the frequency of K-band is 18.7ghz, and there is no polarization information. The plane took off from Zhangye airport at 9:48 (Beijing time, the same below) and landed at 14:14. 10: At 16-11:40, the flight altitude was 3100-3500m and the flight speed was about 230-250km / hr. 12: 16-12:18 low flying Linze reservoir line 1-6, relative altitude 100m, flight speed 190km / hr. 12: At 26-13:42, he worked in Linze photography area, with a flight altitude of about 2000m and a flight speed of about 250km / hr. 13: 49-13:51 fly low again to Linze reservoir line 1-6. The original data is divided into two parts: microwave radiometer data and GPS data. The L and K bands of microwave radiometer are non imaging observations. The digital values obtained from the instantaneous observation are recorded in the text file, and the longitude and latitude as well as the aircraft attitude parameters are recorded in the GPS data. When using microwave radiometer to observe data, it is necessary to convert the digital value recorded into the bright temperature value according to the calibration coefficient (the calibration coefficient file is filed with the original observation data). At the same time, through the clock records of microwave radiometer and GPS, we can connect the microwave observation with GPS record and match the geographic coordinate information for the microwave observation. Due to the coarse observation resolution of microwave radiometer, the effects of aircraft yaw, roll and pitch are generally ignored in data processing. According to the target and flight relative altitude (H), after calibration and coordinate matching, the observation information can also be gridded. The resolution (x) of L band and K band is consistent with that of observation footprint. The reference resolution is: L band, x = 0.3H; K band, x = 0.24h. After the above steps, we can get the products that users can use directly.
LI Xin, WANG Shuguo, CHE Tao, XIAO Qing, Liu Qiang, ZHAO Kai, JIN Jinan
This data set was acquired by the L & K band airborne microwave radiometer on the morning of April 1, 2008, in the A'rou flight zone. The frequency of L-band is 1.4GHz, and the backsight is 35 degrees to obtain dual polarization (H and V) information; the frequency of K-band is 18.7ghz, and there is no polarization information. The plane took off from Zhangye airport at 8:06 (Beijing time, the same below) and landed at 11:17. 8: 50-10:13 fly from north to south, observe and reserve 10 routes, flight height is about 4100m, flight speed is about 260km / hr. 10: At 20-10:35, Jiafei 6-8 and 6-9 lines completed the observation. The original data is divided into two parts: microwave radiometer data and GPS data. The L and K bands of microwave radiometer are non imaging observations. The digital values obtained from the instantaneous observation are recorded in the text file, and the longitude and latitude as well as the aircraft attitude parameters are recorded in the GPS data. When using microwave radiometer to observe data, it is necessary to convert the digital value recorded into the bright temperature value according to the calibration coefficient (the calibration coefficient file is filed with the original observation data). At the same time, through the clock records of microwave radiometer and GPS, we can connect the microwave observation with GPS record and match the geographic coordinate information for the microwave observation. Due to the coarse observation resolution of microwave radiometer, the effects of aircraft yaw, roll and pitch are generally ignored in data processing. According to the target and flight relative altitude (H), after calibration and coordinate matching, the observation information can also be gridded. The resolution (x) of L band and K band is consistent with that of observation footprint. The reference resolution is: L band, x = 0.3H; K band, x = 0.24h. After the above steps, we can get the products that users can use directly.
WANG Shuguo, WANG Xufeng, CHE Tao, ZHAO Kai, JIN Jinan, XIAO Qing, Liu Qiang
Advanced along orbit scanning radiometer (AATSR) is an advanced tracking scanning radiometer sensor mounted on the European Space Agency ENVISAT satellite. It is one of many high-precision and stable infrared radiometers for retrieving sea surface temperature (SST). Its accuracy can reach 0.3k, and it can also be used to record meteorological data. AATSR is a multi-channel imaging radiometer. Its main goal is to provide global ocean surface temperature with high accuracy and stability for monitoring the earth's climate change. At present, there are 38 ENVISAT AATSR images in Heihe River Basin. The acquisition time is 2008-05-17 (2 scenes), 2008-05-27 (2 scenes), 2008-05-30 (2 scenes), 2008-06-02 (2 scenes), 2008-06-12 (2 scenes), 2008-06-15 (2 scenes), 2008-06-18 (2 scenes), 2008-06-21 (2 scenes), 2008-07-04 (2 scenes), 2008-07-072008-07-102008-07-172008-07-202008-07-232008-07-262008-08-022008-08-052008-08-082008 -08-11，2008-08-14，2008-08-21，2008-08-24，2008-08-27，2008-08-30，2008-09-06，2008-09-12，2008-09-15，2008-09-18，2008-09-25。 The product level is L1B, which has been corrected by radiation but not by geometry. The ENVISAT AATSR remote sensing data set of Heihe comprehensive remote sensing joint test was obtained through the China EU "dragon plan" project (Project No.: 5322) (see the data use statement for details).
LI Xin
ASTER data in 2007 and 2008 are 15 scenes, covering the whole Heihe River Basin. Acquisition time: 2007-10-22 (1 scene), 2007-11-14 (1 scene), 2007-11-23 (1 scene), 2007-12-04 (1 scene), 2008-01-28 (1 scene), 2008-02-13 (1 scene), 2008-05-03 (4 scenes), 2008-05-05 (1 scene), 2008-05-17 (1 scene), 2008-06-04 (2 scenes), 2008-06-13 (1 scene). The product level is L1B, which has been calibrated by radiation and geometry. The ASTER Remote sensing data set of Heihe integrated remote sensing joint experiment was obtained from NASA's data website (https://wist.echo.nasa.gov/) through international cooperation.
National Aeronautics and Space Administration
This data is produced using knowledge rule-based land cover classification methods. It is a set of USGS global land cover classification standards that can be used in atmospheric models and land surface process models of land cover types in the Heihe River Basin. The data covers the upper, middle, and lower reaches of the Heihe River Basin. The data uses Albers Conical Equal Area projection with a spatial resolution of 1 km. It is an ASCII file containing the land cover classification code and named: Rule_Based_Lulc_of_HRB2009.asc. You can directly use a text program (such as Notepad) to open and view the file, you can also input it in ArcGIS for other operations. The NOAH land surface process parameter table and parameter table description matched with the data are provided. Users can refer to this parameter table to apply the data to the land surface process model. The two files are USGS_LULC_NOAHVEGPARM.TBL and NOAHVEGPARM_documentation.txt, both can be opened by the text program (such as Notepad).
NAN Zhuotong
The dataset of airborne imaging spectrometer (OMIS-II) mission was obtained in the Linze station-Linze grassland flight zone on Jun. 6, 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 linage ! endtime ! lat ! long ! alt |- | 1 || 1-13 || 2008-06-06_09-32-22_DATA.BSQ || 09:56:32 || 39.167 || 100.044 || 2945.9 || 5718 || 10:02:53 || 39.362 || 100.191 || 2936.7 |- | 2 || 1-12 || 2008-06-06_10-02-38_DATA.BSQ || 10:08:42 || 39.373 || 100.193 || 2956.1 || 5565 || 10:14:53 || 39.182 || 100.049 || 2953.1 |- | 3 || 1-11 || 2008-06-06_10-14-39_DATA.BSQ || 10:19:51 || 39.177 || 100.039 || 2931.2 || 5432 || 10:25:54 || 39.363 || 100.179 || 2958.3 |- | 4 || 1-10 || 2008-06-06_10-25-39_DATA.BSQ || 10:31:50 || 39.376 || 100.182 || 2959.7 || 5396 || 10:37:50 || 39.190 || 100.041 || 2952.7 |- | 5 || 1-9 || 2008-06-06_10-37-35_DATA.BSQ || 10:43:06 || 39.179 || 100.026 || 2956.4 || 5399 || 10:49:06 || 39.368 || 100.169 || 2939.0 |- | 6 || 1-8 || 2008-06-06_10-48-51_DATA.BSQ || 10:55:20 || 39.383 || 100.174 || 2943.2 || 5643 || 11:01:36 || 39.1922 || 100.029 || 2944.8 |- | 7 || 1-7 || 2008-06-06_11-01-22_DATA.BSQ || 11:07:04 || 39.185 || 100.0175 || 2947.2 || 5306 || 11:12:58 || 39.373 || 100.159 || 2943.9 |- | 8 || 1-6 || 2008-06-06_11-12-43_DATA.BSQ || 11:18:57 || 39.386 || 100.162 || 2948.1 || 5604 || 11:25:10 || 39.196 || 100.018 || 2950.5 |- | 9 || 1-5 || 2008-06-06_11-24-56_DATA.BSQ || 11:30:22 || 39.188 || 100.006 || 2934.0 || 5469 || 11:36:26 || 39.378 || 100.149 || 2935.4 |- | 10 || 1-4 || 2008-06-06_11-36-12_DATA.BSQ || 11:42:30 || 39.389 || 100.151 || 2935.4 || 5570 || 11:48:41 || 39.198 || 100.007 || 2949.0 |- | 11 || 1-3 || 2008-06-06_11-48-27_DATA.BSQ || 11:54:21 || 39.205 || 100.005 || 2915.2 || 5028 || 11:59:57 || 39.380 || 100.138 || 2908.8 |- | 12 || 1-2 || 2008-06-06_11-59-42_DATA.BSQ || 12:06:00 || 39.395 || 100.142 || 2931.0 || 5523 || 12:12:08 || 39.205 || 99.999 || 2950.0 |- | 13 || 1-1 || 2008-06-06_12-11-53_DATA.BSQ || 12:18:17 || 39.197 || 99.985 || 2916.5 || 5451 || 12:24:20 || 39.389 || 100.131 || 2907.9 |}
Liu Liangyun, LI Xin, MA Mingguo
The first dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Yingke oasis and Huazhaizi desert steppe on 28-29 June, 7, 10, 26 July, 2 August, 2012 (UTC+8). The dataset of ground truth measurements synchronizing with airborne Polarimetric L-band Multibeam Radiometer (PLMR) mission was obtained in the Linze Inland River Basin Comprehensive Research Station on 3 July, 2012. PLMR is a dual-polarization (H/V) airborne microwave radiometer with a frequency of 1.413 GHz, which can provide multi-angular observations with 6 beams at ±7º, ±21.5º and ±38.5º. The PLMR spatial resolution (beam spot size) is approximately 0.3 times the altitude, and the swath width is about twice the altitude. The measurements were conducted in the southwest part of the Zhangye Oasis, which included two sampling plots. One was located in Gobi desert with an area of 1 km × 1 km. Due to its homogeneous landscape, around 10 points were sampled to acquire the situation of soil water content. The other sampling plot was designed in farmlands with a dominant plant type of maize. Ground measurements took place along 16 transects, which were arranged parallelly with an interval of 160 m between each other in the east-west direction. In each 2.4 km long transect, soil moisture was sampled at every 80 m in the north-south direction. Steven Hydro probes were used to collect soil moisture and other measurements. For each sampling point in farmland, two measurements were acquired within an area of 1 m2, with one for the soil covered by plastic film (point name was tagged as LXPXXA) and the other for exposed soil (point name was tagged as LXPXXB). The field campaign started from 11:00 AM, but stopped at 4:00 PM on 28 June because of rain. The rest of measurements were completed from 10:30 AM to 5:30 PM on 29 June. Concurrently with soil moisture sampling, vegetation properties were measured at around 10 locations within the farmland sampling plot. Observation items included: Soil parameters: volumetric soil moisture (inherently converted from measured soil dielectric constant), soil temperature, soil dielectric constant, soil electric conductivity. Vegetation parameters: biomass, vegetation water content, canopy height. Data and data format: This dataset includes two parts of measurements, i.e. soil and vegetation parameters. The former is as shapefile, with measured items stored in its attribute table. The measured vegetation parameters are recorded in an Excel file.
WANG Shuguo, LI Xin
The dataset of airborne Polarimetric L-band Multibeam Radiometers (PLMR) was acquired on 4 July, 2012, located along the riverway of Heihe River in the middle reaches of the Heihe River Basin. The aircraft took off at 10:50 am (UTC+8) from Zhangye airport and landed at 14:50 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 LAI measurements was obtained in the Linze station foci experimental area. (1) LAI of maize, desert scrub and the poplar measured by the fisheye camera (CANON EOS40D with a lens of EF15/28), shooting straight downwards, with exceptions of higher plants, which were shot upwards in Wulidun farmland quadrates (Jun. 3, 4 and 29, May 28 and 30 and Jul. 11), inside Linze station quadrates (Jun. 19, 25 and 30, Jul. 3 and 10, May 27), the desert transit zone (May 28 and 30) and the poplar forest (May 30). Sample points were archived in coordiantes.xls. Data included original photos (.JPG) and those processed by can_eye5.0 (in excel). For more details, see Readme file. (2) LAI measured by the ruler and the set square in Wulidun farmland quadrate inside Linze station on May 22, 23, 24, 28 and 30 and Jul. 11, 2008. Part of the samples were also measured by LI-3100 and compared with those by manual work for further correction. Data were archived as Excel files. (3) LAI and SD of maize measured by LAI2000 in Wulidun farmland quadrates (Jun. 24 and 29 and Jul. 10) and inside Linze station quadrates (Jun. 19, 25 and 30, Jul. 3, 9 and 10). Data educed from LAI2000 periodically were archived as text files (.txt) and marked with one ID. Raw data (table of word and txt) and processed data (Excel) were included. Besides, observation time, the observation method and the repetition were all archived. 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.
DONG Jian, LI Jing, Li Xiangyun, Qu Yonghua, SONG Danxia, SUN Qingsong, XIAO Yueting, XIAO Zhiqiang, YU Yingjie, ZHOU Hongmin, JIANG Hao, LI Shihua,
On 26 July 2012, a Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) carried by the Y-12 aircraft was used in a visible near Infrared thermal Dual-mode airborne remote sensing experiment, which is located in the artificial oasis eco-hydrology experimental area (5×5 km). WIDAS includes an CCD cameras with spatial resolution 0.2 m, a visible near Infrared multispectral camera with five bands scanner (an maximum observation angle 48° and spatial resolution 1 m), and a thermal image camera with spatial resolution 4.8 m. The CCD camera data are recorded in DN values processed by mosaic and orthorectification. The multispectral camera data are recorded in reflectance processed by atmospheric and geometric correction. Thermal image camera data are recorded in radiation brightness temperature processed by atmospheric and geometric correction.
XIAO Qing, Wen Jianguang
The dataset of airborne WiDAS mission was obtained in the Linze station-Linze grassland flight zone on Jul. 11, 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 || 1#13 || 1500m || 1:52:06 || 11:58:02 || 90 || processed; compelete || good || Pingchuan reservoir |- | 2 || 1#11 || 1500m || 12:11:38 || 12:09:54 || 95 || processed; compelete || good || Linze grassland station |- | 3 || 1#9 || 1500m || 12:14:58 || 12:20:42 || 87 || processed; compelete || good || Pingchuan reservoir |- | 4 || 1#7 || 1500m || 12:27:14 || 12:33:18 || 92 || processed; compelete || good || desert transit zone |- | 5 || 1#5 || 1500m || 12:38:22 || 12:44:14 || 89 || processed; compelete || good || north-south desert plot |- | 6 || 1#3 || 1500m || 12:50:30 || 12:56:26 || 90 || processed; compelete || good || Pingchuan reservoir |- | 7 || 1#1 || 1500m || 13:01:46 || 13:07:46 || 91 || processed; compelete || good || Linze station |}
Liu Qiang, XIAO Qing, Wen Jianguang, FANG Li, Wang Heshun, LI Bo, LIU Zhigang, LI Xin, MA Mingguo
The dateset of the ground-based RPG-8CH-DP microwave radiometer observations was obtained in the Biandukou foci experimental area from Mar. 14 to 17, 2008. Observation items included the brightness temperature by the ground-based microwave radiometer (18.7GHz and 36.5GHz), the soil temperature by the thermal resistor, the gravimetric soil moisture by the microwave drying method, and the surface roughness by the grid board. The wheat stubble land (38°15'44.13"N, 100°55'35.34"E) was chosen for continuous observations from 11:00 to 24:00 on Mar. 14, with the incidence 20°-70° and the step length 5°. The rape stubble land (38°15'23.17"N, 100°58'37.84"E) was chosen for continuous observations from 10:00 to 21:30 on Mar. 16, with the incidence 20°-70° and the step length 5°. The deep plowed land (38°18'8.28"N, 101° 3'27.22"E) was chosen for short time observations from 17:26 to 19:20 on Mar. 17, with the azimuth angle 240°-300° and the step length 10°, the incidence 40°-70° and the step length 5°. The brightness temperature was archived as .BRT and .txt files (the ASCII format). Each row in .txt was listed by year, month, date, hour, minute, second, 6.925GHz (h), 6.925GHz (v), 10.65GHz (h), 10.65GHz (v) , 18.7GHz (h), 18.7GHz (v), 36.5GHz (h), 36.5GHz (v), the elevation angle, and the azimuth angle. Values for 6.925GHz and 10.65GHz were zero due to malfunction. The roughness data were obtained by the grid board and the camera and the RMS height (cm) and correlation length (cm) were also calculated and archived, which could be opened by Notepad or Microsoft Office Word. Those provide reliable reference for the roughness of the same land cover type. The gravimetric soil moisture (soil samples from 0-1cm, 1-3cm and 3-5cm) was measured by the microwave drying method. The file can be opened by Microsoft Office Word. The shallow layer soil moisture was measured by hydra prob from 12:00 to 17:00 on 14 and by the Hydra probe (straight downward for 0-5cm) and HH2 (level into the soil surface) on 16. The surface temperature was measured by the thermal resistor. The file can be opened by Microsoft Office Word. Four data files were included, the brightness temperature, the surface temperature, the soil moisture and the surface roughness.
CHANG Sheng, LIANG Xingtao, PAN Jinmei, PENG Danqing, ZHANG Yongpan, ZHANG Zhiyu, ZHAO Shaojie, Zhao Tianjie, ZHENG Yue, YIN Xiaojun, ZHANG Zhiyu
On 7 July 2012 (UTC+8), a CASI/SASI sensor boarded on the Y-12 aircraft was used to obtain the visible/near Infrared hyperspectral image, which is located in the observation experimental area. The relative flight altitude is 2000 meters, The wavelength of CASI and SASI is 380-1050 nm and 950-2450 nm, respectively. The spatial resolution of CASI and SASI is 1 m and 2.4 m, respectively. Through the ground sample points and atmospheric data, the data product are recorded in reflectance processed by geometric correction and atmospheric correction based on 6S model.
XIAO Qing, Wen Jianguang
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
On 26 July 2012, Wide-angle Infrared Dual-mode line/area Array Scanner (WIDAS) carried by the Harbin Y-12 aircraft was used in a visible near Infrared thermal Dual-mode airborne remote sensing experiment, which is located in the artificial oasis eco-hydrology experimental area (5×5 km). WIDAS includes a CCD camera with a spatial resolution of 0.2 m, a visible near Infrared multispectral camera with five bands scanner (an maximum observation angle 48° and spatial resolution 1 m), and a thermal image camera with a spatial resolution of 4.8 m. The CCD camera data are recorded in DN values processed by mosaic and orthorectification.
XIAO Qing, Wen Jianguang
The dateset of sun photometer observations was obtained in the Biandukou foci experimental area from Mar. 7 to 17, 2008, simultaneous with MODIS and TM. Those provide reliable data for atmosphere correction of the same period in this area. Atmospheric parameters were measured by CE318. The optical depth in 1020nm, 936nm, 870nm, 670nm and 440nm were all acquired. Column water vapor can also be retrieved according to data in 936 nm. The dataset archived in txt files includes processed data on Mar. 7, 14 and 17 respectively.
SU Gaoli
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
On 28 August 2012, Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second ,third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 4800 m with the point cloud density 1.6 point per square meter. Airborne LiDAR-DEM and DSM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
The aim of the simultaneous observation of land surface temperature is obtaining the land surface temperature for different kinds of underlying surface, including the lager areas of homogeneous vegetation with high coverage, water, and concrete floor, while the thermal imager go into the experimental areas of the low reaches. All the land surface temperature data will be used for validation of the retrieved land surface temperature from thermal imager 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 On 1 August, 2014 2. Observation samples Three field samples were chosen in the fly zone, which were large areas of homogeneous vegetation (with high coverage), water, and concrete floor. 3. Observation method Surface temperature values were observed continuously for each sample using handheld infrared thermometers during the imager went into the flying area. 4. Instrument parameters and calibration The field of view of the handheld infrared thermometer is one degree and the emissivity was assumed to be 0.95. All instruments were calibrated on 31 July, 2014 using a black body. 5. Data storage All the observation data were stored in an excel.
Li Yimeng, REN Zhiguo, Zhou Shengnan, MA Mingguo
The dataset includes the chlorophyll content of vegetation in different site which has different types of vegetation, acquired on 8 July, 2012, in order to validate the Chlorophyll products. Observation instruments: Sampling, Acetone extraction method Measurement methods: To analyze the influence height on chlorophyll , we select 12 different corn samples based on the height of corn. To compare the chlorophyll content of different types of vegetation, we also select 3 types of vegetation sample on the first EC tower, 1 beans sample near the seventeenth EC tower and 3 reed samples on wetland. A total of selected 19 different samples are analyzed in the laboratory in the College of Life Science, Hexi. We extract chlorophyll a, chlorophyll b, the content of total chlorophyll of selected samples. Dataset contents: Chlorophyll a, chlorophyll b, the content of total chlorophyll Measurement time: 8 July, 2012
Jia Shuzhen
On 25 July 2012, Leica ALS70 airborne laser scanner carried by the Harbin Y-12 aircraft was used in a LiDAR airborne optical remote sensing experiment. Leica ALS70 airborne laser scanner has unlimited numbers of returns intensities measurements including the first, second ,third return intensities. The wavelength of laser light is 1064 nm. The absolute flight altitude is 5500 m with the point cloud density 1 points per square meter. Airborne LiDAR-DEM and DSM data production were obtained through parameter calibration, automatic classification of point cloud density and manual editing.
XIAO Qing, Wen Jianguang
The dataset of snow properties measured by the Snowfork was obtained in the Binggou watershed foci experimental area from Dec. 5-16 2007, during the pre-observation period. The aims of the measurements were to verify applicability of the instruments and to acquire snow parameters for simultaneous airborne, satellite-borne and ground-based remote sensing experiments and other control experiments. Observation items included: (1) physical quantities by direct observations: resonant frequency, the rate of attenuation and 3db bandwidth (2) physical quantities by indirect observations: snow density, snow complex permittivity (the real part and the imaginary part), snow volumetric moisture and snow gravimetric moisture. Five files including raw data and processed data are kept, data by the Snowfork on Dec 5, data by BG-A MODIS on Dec 6 and 7, data in BG-B, BG-C, BG-D and BG-E on Dec 10, and data in BG-D with the microwave radiometer on Dec 14 and 16.
HAO Xiaohua, LIANG Ji
The dataset of airborne Polarimetric L-band Multibeam Radiometers (PLMR) was acquired on 26 July, 2012, located in the middle reaches of the Heihe River Basin. The aircraft took off at 9:10 am (UTC+8) from Zhangye airport and landed at 13:40 pm, with the flight time of 4.5 hours. The flight was performed in the altitude of about 2300 m and at the speed of about 220-250 km during the observation, corresponding to an expected ground resolution of about 700 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 TIR spectral emissivity was obtained in the arid region hydrology experiment area and A'rou foci experiment area. Observations were by: (1) Spectral emissivity obtained from 102F at 2-25um in cooperation with the handheld infrared thermometer (BNU) for the surface radiative temperature and one au-plating board for downward atmospheric radiation. The radiative transfer equation and TES methods were applied to retrieve emissivity. The grassland and the concrete floor were measured on May, 27, 2008, the wheat field and the maize field at ICBC resort on May, 29, 2008, the concrete floor (multiangle measurements) at ICBC resort on Jun. 3, 2008, the bare soil and the maize leaf in Yingke oasis maize field on Jun. 22, 2008, the maize and wheat canopy in Yingke oasis maize field on Jun. 23, 2008, the rape field in Biandukou experimental area on Jun. 24, 2008, the alfalfa, the saline land, the grassland and the barley land on Jun. 26, 2008, the wheat field and the maize field in Yingke oasis maize field on Jun. 29, 2008, the desert bare land and vegetation (Reaumuria soongorica) in No. 2 Huazhaiai desert plot on Jun. 30, 2008, the rape field and the grassland in Biandukou experimental area on Jul. 6, 2008, and the grassland and the bare land (multiangle) in A'rou experimental area on Jul. 14, 2008. The cold blackbody calibration (*.CBX/*.CBB), the warm blackbody calibration (*.WBX/*.WBB), the ground objects measurements (*.SAX), au-plating board measurements, and the downward atmospheric radiation (*.DWX) were all needed during observation. Moreover, the spectral radiance and emissivity were also archived. The response function of various bands could be acquired by 102F. And then emissivity of 2-25um could be retrieved. Two results of emissivity were developed: one was direct from 102F and the other was retrieved by ISSTES (Iterative spectrally smooth temperature-emissivity separation). Spectral resolution for raw data and proprecessed data was 4cm-1. (2) Spectral emissivity obtained from BOMAN at 2 -13μm in cooperation with the blackbody barrel and the blackbody from Institute of Remote Sensing Applications and the blackbody (BNU). The desert was measured on Jun. 30 and Jul. 1, 2008, A'rou foci experimental area on Jul. 14, 2008, indoor observations on the deep and shallow layer soil, vegetation, small stones, two maize plants from Yingke No.2 (YKYZYMD02) field and one maize plant and bare land from No. 3 (YKYZYMD03)field on on Jul. 16, 2008, Linze experimental area on Jul. 17, 2008, and gobi on Jul. 18, 2008. The sample site, coordinates, time and photos were all archived. During each observation, BOMAN was preheated and the blackbody was set at the predicted target temperature, which would be changed after the infrared radiation of the blackbody was measured by BOMAN. And then the target infrared radiation, the downward atmospheric radiation (reflected by the au-plating board) and the infrared radiation of the blackbody would be measured one by one. Raw data were archived in Igm, and after processed by FTSW500, the result was Rad (radiation). Finally, Rad would be changed into txt files by Matlab programs.
REN Huazhong, CHEN Ling, YAN Guangkuo, DU Yongming, LI Hua, LIU Yani, WANG Heshun, XIAO Qing, ZHOU Chunyan
The dataset of ground truth measurements for snow synchronizing with the airborne PHI mission was obtained in the Binggou watershed foci experimental area on Mar. 24, 2008. 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 as the snow surface temperature by the handheld infrared thermometer, the snow layer temperature by the probe thermometer, the snow grain size by the handheld microscope, and snow density by the aluminum case in BG-A1, BG-A2, BG-B, BG-D, BG-E and BG-F5 (three sampling units each) from 11:11-12:35 (BJT) with the airplane overpass. 64 points were selected by four groups. (3) Snow albedo by the total radiometer in BG-A. (4) The snow spectrum by ASD (Xinjiang Meteorological Administration) in BG-A11 Two files including raw data and preprocessed data were archived.
GE Chunmei, GU Juan, HAO Xiaohua, LI Hongyi, LI Zhe, LIANG Ji, MA Mingguo, SHU Lele, WANG Jianhua, WANG Xufeng, WU Yueru, XU Zhen, ZHU Shijie, LIANG Xingtao, LIU Zhigang, QU Wei, REN Jie, FANG Li, LI Hua, CHANG Cun, DOU Yan, MA Zhongguo, JIANG Tenglong, XIAO Pengfeng , LIU Yan, ZHANG Pu
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