30m month compositing Fraction Vegetation Cover (FVC) data set of Heihe River Basin provides the results of monthly FVC synthesis in 2011-2014. The data constructs multi-angle observation data sets by using China's domestic satellite HJ/CCD data with high temporal resolution (2 days after networking) and spatial resolution (30m) , and divides the country into different vegetation divisions and land types. The conversion coefficients of NDVI and FVC are calculated respectively, and use the calculated conversion coefficient lookup table and monthly compositing NDVI to produce the regional monthly compositing FVC products. The 30m month compositing FVC product in the Heihe River Basin can directly obtain the vegetation coverage ratio through high-resolution data, and mitigate the influence of low-resolution data heterogeneity; in addition, selecting the typical period of vegetation growth change, by fitting the vegetation index of each pixel time series to obtain the growth curve parameters that correspond to each pixel; then the land use map and the vegetation classification map are combined to find the representative uniform pixels of the region for training the conversion coefficients of the vegetation index. Compared with the ASTER reference FVC results, the 30m/month compositing FVC product in the Heihe River Basin is slightly higher than the ASTER reference result, but the overall deviation is not large, and the maximum value of the root mean square error (RMSE) of the product and the reference value is less than 0.175. In addition, compared with the ground survey data of Huailai experimental site in Hebei Province, the 30 m/month compositing FVC products generally reflect the seasonal variation of vegetation growth, and the deviation from the ground survey data is less than 0.1. At the same time, compared with the ground measurements of vegetation coverage in many watersheds in Northeast, North China and Southeast China, the overall error between the compositing FVC products and the ground measurements is less than 0.2. In all, the 30m/month compositing FVC data set of Heihe River Basin comprehensively utilizes multi-temporal and multi-angle remote sensing data to improve the estimation accuracy and time resolution of FVC parameter products, so as to better serve the application of remote sensing data products.

According to the sample survey data, in August 2013, 30 forest plots were set up in the Tianlaochi watershed, with a plot size of 10 m×20 m. The long side of the plot was parallel to the slope of the hillside, including 26 blocks of Picea crassifolia forest. 2 blocks of Sabina Przewalsskii forest and 2 mixed forests of Picea and Sabina. In the plot, the diameter of the breast of each tree (the diameter of the trunk at a height of 1.3 m) is measured by a diameter tape, and the height of each tree and the height under the branches (the height of the first live branch at the lower end of the canopy) is measured by a hand-held ultrasonic altimeter. The north-south direction and the east-west crown width are measured with a tape measure, and the sample site is positioned by differential GPS. The parallel version of HASM-AD algorithm is used to simulate the classified LIDAR point cloud data. DEM is generated from ground points, DSM is generated from all points, and the height of surface features is obtained by differential operation between DSM and DEM. In forest area, it is called Canopy Height Model (CHM). A circular window with a given search radius is used to find the local maximum value on CHM. If the central pixel value is the maximum value, it is determined as the crown vertex. The pixel attribute value of the tree vertex is the tree height, and the spatial resolution is 1m.

This dataset was acquired on May 25, 2008 by the L&K-band airborne microwave radiometer at the Linze-Biandukou flight area.The L-band frequency is 1.4 GHz, the rear view is 35 degrees, and the dual-polarization (H and V) information is obtained; the K-band frequency is 18.7 GHz, with zenith angle observation, and there is no polarization information. The plane took off from Zhangye Airport at 9:51 (Beijing time, the same below) and landed at 15:01. The observation from 10:10 to 12:30 was in the Linze area, the flight altitude is about 1800m, and the flight speed is about 250km/hr. The plane flew low over Linze Reservoir from 12:31 to 12:38. The plane works in the Bianduko aerophotography region from13:13 to 14:35, the flight altitude is about 3000m, and the flight speed is about 250km/hr. The original data is divided into two parts: microwave radiometer data and GPS data. The L and K bands of microwave radiometer are all from non-imaging observation, the digital values obtained from instantaneous observation are recorded by text files, the longitude and latitude of flight and the attitude parameters of aircraft are recorded by GPS data. At the same time, through the respective clock records of the microwave radiometer and GPS, the microwave observation can be linked with the GPS record, and the microwave observation can be matched with the geographical coordinate information. Due to the relatively low resolution of the microwave radiometer, the leeway, welter and pitching of the aircraft are generally neglected in data processing. According to the target of use and relative flight altitude (H), after calibration and coordinate matching, the observation information can be rasterized. The resolution (x) of the L and K bands can be considered consistent with the observation footprint. The reference resolution is: L band, x = 0.3H; K band, x = 0.24H. After the above steps, products that can be directly used by users can be obtained.

This data set includes observation data of meteorological elements in the downstream desert station of Heihe Hydrometeorological Observation Network from January 1, 2016 to December 31, 2016. The site is located in the desert beach of Ejina Banner, Inner Mongolia, and the underlying surface is desert. The latitude and longitude of the observation point is 100.9872E, 42.1135N, and the altitude is 1054m. The air temperature and relative humidity sensors are installed at 5m and 10m, facing the north; the barometer is installed at 2m; the tipping bucket rain gauge is installed at 10m; the wind speed sensor is set at 5m, 10m, and the wind direction sensor is set at 10m, facing the north; the four-component radiometer is installed at 6m, facing south; two infrared thermometers are installed at 6m, facing south, the probe orientation is vertically downward; the soil temperature probe is buried in the ground surface 0cm and underground 2cm, 4cm, 10cm, 20cm 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower; soil moisture sensors are buried in the underground 2cm, 4cm, 10cm, 20cm, 40cm, 60cm and 100cm, in the south of the 2m from the meteorological tower; soil heat flux plates (3 pieces) are buried in the ground 6 cm in order. Observation items include: air temperature and humidity (Ta_5m, RH_5m, Ta_10m, RH_10m) (unit: centigrade, percentage), air pressure (Press) (unit: hectopascal), precipitation (Rain) (unit: mm), wind speed (WS_5m, WS_10m) (unit: m / s), wind direction (WD_10m) (unit: degree), four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watts / square meter), surface radiation temperature (IRT_1, IRT_2 ) (unit: centigrade), soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watts/square meter), soil moisture (Ms_2cm, Ms_4cm, Ms_10cm, Ms_20cm, Ms_40cm, Ms_60cm, Ms_100cm) (unit: volumetric water content, percentage) and soil temperature (Ts_0cm, Ts_2cm, Ts_4cm, Ts_10cm, Ts_20cm, Ts_40cm, Ts_60cm, Ts_100cm) (unit: centigrade). Processing and quality control of the observation data: (1) ensure 144 data per day (every 10 minutes), when there is missing data, it is marked by -6999; (2) eliminate the moment with duplicate records; (3) delete the data that is obviously beyond the physical meaning or the range of the instrument; (5) the format of date and time is uniform, and the date and time are in the same column. For example, the time is: 2016-6-10 10:30; (6) the naming rules are: AWS+ site name. For hydrometeorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

This data set is the acquisition of the super-site forest 3D structure of the scanning point cloud data and other ancillary data based on the ground-based lidar (LiDAR) . Data acquisition time is from June 4, 2008 to June 12, 2008. Riegl LMS-Z360i ground-based LiDAR was used. The super site is divided into 16 sub-samples of 25m×25m, LiDAR base station points are set in each sub-sample, and LiDAR acquisition 3D full coverage LiDAR point metadata is set at each base station point. The content of the data set: total station measurement coordinates (x, y, z) for each LiDAR data acquisition base station point, the instrument attitude measured by a digital slope meter and an angle meter when each station collects data, and the laser radar scanning point cloud data at each station. This data set can provide realistic 3D forest scenes, provide detailed ground observation data for the development and correction of various 3D forest remote sensing models, and provide ground verification data for airborne and spaceborne remote sensing data.

The data set contains the observation data of meteorological elements from the Barren-land Station,which is located along the lower reaches of the Heihe Hydro-meteorological Observation Network, and the data set covers data from January 1, 2014 to December 31, 2014. The station is located in Sidaoqiao,Dalaihubu Town, Ejina Banner, Inner Mongolia. The underlying surface is barren land. The latitude and longitude of the observation point is 101.1326E, 41.9993N, and the altitude is 878m. The four-component radiometer is installed 6 meters above the ground, facing South; two infrared thermometers are installed 6 meters above the ground, facing South, and the probe orientation is vertical downward; the soil temperature probes are buried respectively at 0cm on the ground surface, 2cm and 4cm under the ground, they are located 2 meters from the meteorological tower in the South; the soil moisture sensors (installed on March 15,2014) are buried 2cm and 4cm under the ground, 2 meters from the meteorological tower in the South; the soil heat flow boards (3 pieces) are buried 6cm under the ground, 2 meters from the meteorological tower in the South. Observed items include: four-component radiation (DR, UR, DLR_Cor, ULR_Cor, Rn) (unit: watt / square meter), surface radiation temperature (IRT_1, IRT_2) (unit: Celsius) , soil heat flux (Gs_1, Gs_2, Gs_3) (unit: watt / square meter), soil moisture (Ms_2cm , Ms_4cm) (unit: volumetric water content, percentage), soil temperature (Ts_0cm, Ts_2cm, Ts_4cm) (unit: Celsius). Processing and quality control of observation data: (1) Ensure 144 data per day (every 10 minutes), if there is missing data, it is marked as -6999. The surface radiation temperature IRT2 data during October 12,2014 to November 8,2014 is missing because of sensor problem; Some 2cm soil moisture data during March21 to March 29 and October 12 to November 8 is missing due to probe problem. (2) Eliminate moments with duplicate records; (3) Remove data that is significantly beyond physical meaning or beyond the measuring range of the instrument; (4) Data marked by red is debatable; (5) The formats of the date and time are uniform, and the date and time are in the same column. For example, the time is: 2014-9-10 10:30; (6) The naming rule is: AWS + site name. For hydro-meteorological network or site information, please refer to Li et al. (2013). For observation data processing, please refer to Liu et al. (2011).

This data is originated from the 1:100,000 national basic geographic database, which was open freely for public by the National Basic Geographic Information Center in November 2017. The boundary of the Qinghai-Tibet Plateau was spliced and clipped as a whole, so as to facilitate the study on the Qinghai-Tibet plateau. This data set is the 1:100,000 administrative boundaries of the qinghai-tibet plateau, including National_Tibet_line、 Province_Tibet、City_Tibet、County_Tibet_poly and County_Tibet_line. Administrative boundary layer (County_Tibet_poly) property name and definition: Item Properties Describe Example PAC Administrative division code 513230 NAME The name of the County line name Administrative boundary layer (BOUL) attribute name and definition: Item Properties Describe Example GB classification code 630200 Administrative boundary layer (County_Tibet_line) attribute item meaning: Item Properties Describe Example GB 630200 Provincial boundary GB 640200 Prefectural, municipal and state administrative boundaries GB 650201 county administrative boundaries (determined)

This dataset includes data recorded by the Cold and Arid Research Network of Lanzhou university obtained from an observation system of Meteorological elements gradient of Liancheng Station from January 1 to December 31, 2018. The site (102.833E, 36.681N) was located on a forest in the Tulugou national forest park, which is near Liancheng city, Gansu Province. The elevation is 2912 m. The installation heights and orientations of different sensors and measured quantities were as follows: air temperature and humidity profile (4 and 8 m, towards north), wind speed and direction profile (windsonic; 4 and 8 m, towards north), air pressure (1.5 m), rain gauge (2 m), four-component radiometer (4 m, towards south),infrared temperature sensors (2 m, towards south, vertically downward), photosynthetically active radiation (4 m, towards south), soil heat flux (2 duplicates below the vegetation;-0.05 and -0.1m in south of tower), soil soil temperature/ moisture/ electrical conductivity profile (below the vegetation;-0.05 and -0.1m in south of tower), sunshine duration sensor(4 m, towards south). The observations included the following: air temperature and humidity (Ta_4 m and Ta_8 m; RH_4 m and RH_8 m) (℃ and %, respectively), wind speed (Ws_2 m, Ws_4 m, and Ws_8 m) (m/s), wind direction (WD_2 m, WD_4 m, and WD_8 m) (°), air pressure (press) (hpa), precipitation (rain) (mm), four-component radiation (DR, incoming shortwave radiation; UR, outgoing shortwave radiation; DLR_Cor, incoming longwave radiation; ULR_Cor, outgoing longwave radiation; Rn, net radiation) (W/m^2), infrared temperature (IRT) (℃), photosynthetically active radiation (PAR) (μmol/ (s m-2)), soil heat flux (Gs_5 cm, Gs_10 cm) (W/m^2), soil temperature (Ts_5 cm, Ts_10 cm) (℃), soil moisture (Ms_5 cm, Ms_10 cm) (%, volumetric water content), soil water potential (SWP_5cm,SWP_10cm)(kpa), soil conductivity (EC_5cm,EC_10cm)(μs/cm), sun time (h). The data processing and quality control steps were as follows: (1) The AWS data were averaged over intervals of 10 min for a total of 144 records per day. The soil heat flux data were wrong during Jan.1 to May 30 because of rodent damage; The data during May. 30 to July 6 were missing because the power supply failure; The air humidity data were rejected due to program error. (2) Data in duplicate records were rejected. (3) Unphysical data were rejected. (4) The data marked in red are problematic data. (5) The format of the date and time was unified, and the date and time were collected in the same column, for example, date and time: 2018-6-10 10:30.

Glacier monitoring mass balance data are the most direct and reliable data for glaciers responding to climate change. The data set of global glacier monitoring mass balance collects information on 76 glaciers and their glacier mass balance data, both with continuous (uninterrupted) observation time series and by collecting and arranging globally accessible mass balance data with a time resolution of one year from 1950 to 2016.

The data set of lake dynamics on the Tibetan Plateau was mainly derived from Landsat remote sensing data. Band ratio and the threshold segmentation method were applied. The temporal coverage of the data set was from 1984 to 2016, with a temporal resolution of 5 years. It covered the whole Tibetan Plateau at a spatial resolution of 30 meters. The water body area extraction method mainly adopted the band ratio (B4/B2) or water body index to construct the classification tree. The algorithm construction considered the spatial and temporal variations of the spectral characteristics of the water body and adjusted the threshold of the decision tree by the slope and the slope aspect information of the water body. The long-term sequence satellite-borne data came from different sensors, e.g., Landsat MSS, TM, ETM+, and OLI. The minimum unit for extracting water body information was 2*2 pixels, and all water body areas less than 0.36*10^-2 Km² were removed. The water body information extracted by high-resolution remote sensing data and the verification of the water body checkpoint determined by visual interpretation indicated that the overall accuracy of the water body area information for the Tibetan Plateau was above 95%. The data were saved as a shape file, and projected by Albers projection, with a central meridian of 105 ° and a double standard latitude of 25 ° and 47 °.

This data comes from the National Geographic Information Resources Catalogue Service System, which was provided free to the public by the National Basic Geographic Information Center in November 2017. We have spliced and cut the source of the three rivers as a whole, so as to facilitate the use of the study of the source area of the three rivers. The data trend is 2017. The data set is 1:1 million traffic data in Sanjiangyuan area, including road (LRDL) and railway (LRRL) layers. Highway (LRDL) includes national, provincial, county, Township and other highways; Railway (LRRL) includes standard rail, narrow rail, subway and light rail. Highway (LRDL) Attribute Item Name and Definition: Attribute Item Description Fill in Example GB National Standard Classification Code 420301 RN Road Number X828 NAME Road Name RTEG Road Grade IV TYPE Road Type Viaduct Meaning of Highway (LRDL) Attribute Item: Attribute Item Code Description GB 420101 National Highway 420102 Building China Road 420201 Provincial Highway 420102 Provincial Highway in Architecture 420301 County Road 420302 Jianzhong County Road 420400 Rural Road 420800 Tractor ploughing Road 440100 Simple Highway 440200 Rural Road 440300 Trail Name and definition of railway (LRRL) attribute item: Attribute Item Description Fill in Example GB National Standard Classification Code 410101 RN Railway No. 0907 NAME Railway Name Qinghai-Tibet Railway TYPE Railway Type Elevated

The data set was obtained from the background survey of wildlife diversity in Three River Source National Park by Northwest Institute of Plateau Biology, Chinese Academy of Sciences. The time range of the data set is 2017, and the survey area is Three River Source National Park. The survey species include a variety of rare wildlife such as Equus kiang, Canis lupus, Vulpes vulpes, Cervus elaphus, Accipiter nisus, Phoenicurus erythrogastrus, Prionailurus bengalensis, Buteo hemilasius, Procapra picticaudata, Tetraogallus tibetanus, Perdix hodgsoniae, Falco cherrug, etc.