Plant functional types (PFT) is a combination of large plant species according to the ecosystem function and resource utilization mode of plant species. Each planting functional type shares similar plant attributes, which simplifies the diversity of plant species into the diversity of plant function and structure.The concept of plant-functional has been advocated by ecologists especially ecosystem modelers.The basic assumption is that globally important ecosystem dynamics can be expressed and simulated through limited plant functional types.At present, vegetation-functional model has been widely used in biogeographic model, biogeochemical model, land surface process model and global dynamic vegetation model. For example, the land surface process model of the national center for atmospheric research (NCAR) in the United States has changed the original land cover information into the applied plant-functional map (Bonan et al., 2002).Functional plant has been used in the dynamic global vegetation model (DGVM) to predict the changes of ecosystem structure and function under the global change scenario. 1. Functional classification system of Plant 1 Needleleaf evergreen tree, temperate 2 Needleleaf evergreen tree, boreal 3 Needleleaf deciduous tree 4 Broadleaf evergreen tree, tropical 5 Broadleaf evergreen tree, temperate 6 Broadleaf deciduous tree, tropical 7 Broadleaf deciduous tree, temperate 8 Broadleaf deciduous tree, boreal 9 Broadleaf evergreen shrub, temperate 10 Broadleaf deciduous shrub, temperate 11 Broadleaf deciduous shrub, boreal 12 C3 grass, arctic 13 C3 grass 14 C4 grass 15 Crop 16 Permanent wetlands 17 Urban and built-up lands 18 Snow and ice 19 Barren or sparsely vegetated lands 20 Bodies of water 2. Drawing method China's 1km plant function map is based on the climate rules of land cover and plant function conversion proposed by Bonan et al. (Bonan et al., 2002).Ran et al., 2012).MICLCover land cover map is a blend of 1:100000 data of land use in China in 2000, the Chinese atlas (1:10 00000) the type of vegetation, China 1:100000 glacier map, China 1:10 00000 marshes and MODIS land cover 2001 products (MOD12Q1) released the latest land cover data, using IGBP land cover classification system.The evaluation shows that it may be the most accurate land cover map on the scale of 1km in China.Climate data is China's atmospheric driven data with spatial resolution of 0.1 and temporal resolution of 3 hours from 1981 to 2008 developed by he jie et al. (2010).The data incorporates Princeton land-surface model driven data (Sheffield et al., 2006), gewex-srb radiation data (Pinker et al., 2003), TRMM 3B42 and APHRODITE precipitation data, and observations from 740 meteorological stations and stations under the China meteorological administration.According to the evaluation results of RanYouhua et al. (2010), GLC2000 has a relatively high accuracy in the current global land cover data set, and there is no mixed forest in its classification system. Therefore, the mixed forest in the MICLCover land cover diagram USES GLC2000 (Bartholome and Belward, 2005).The information in xu wenting et al., 2005) was replaced.The data can be used in land surface process model and other related researches.
RAN Youhua, LI Xin
This data set is based on the evaluation of existing land cover data and the evidence theory，including a 1:100,000 land use map for the year 20 2000、a 1:1,000,000 vegetation map、a 1:1,000,000 swamp-wetland map, a glacier map and a Moderate-Resolution Imaging Spectroradiometer land cover map for China in 2001 (MODIS2001) were merged，Finally, the decision is made based on the principle of maximum trust, and a new 1KM land cover data of China in 2000 with IGBP classification system is produced. The new land cover data not only maintain the overall accuracy of China's land use data, but also supplement the information of vegetation types and vegetation seasons in China's vegetation map, update China's wetland map, add the latest information of China's glacier map, and make the classification system more general.
RAN Youhua, LI Xin
Gridded climatic datasets with fine spatial resolution can potentially be used to depict the climatic characteristics across the complex topography of China. In this study we collected records of monthly temperature at 1153 stations and precipitation at 1202 stations in China and neighboring countries to construct a monthly climate dataset in China with a 0.025° resolution (~2.5 km). The dataset, named LZU0025, was designed by Lanzhou University and used a partial thin plate smoothing method embedded in the ANUSPLIN software. The accuracy of LZU0025 was evaluated based on three aspects: (1) Diagnostic statistics from the surface fitting model during 1951–2011. The results indicate a low mean square root of generalized cross validation (RTGCV) for the monthly air temperature surface (1.06 °C) and monthly precipitation surface (1.97 mm1/2). (2) Error statistics of comparisons between interpolated monthly LZU0025 with the withholding of climatic data from 265 stations during 1951–2011. The results show that the predicted values closely tracked the real true values with values of mean absolute error (MAE) of 0.59 °C and 70.5 mm, and standard deviation of the mean error (STD) of 1.27 °C and 122.6 mm. In addition, the monthly STDs exhibited a consistent pattern of variation with RTGCV. (3) Comparison with other datasets. This was done in two ways. The first was via comparison of standard deviation, mean and time trend derived from all datasets to a reference dataset released by the China Meteorological Administration (CMA), using Taylor diagrams. The second was to compare LZU0025 with the station dataset in the Tibetan Plateau. Taylor diagrams show that the standard deviation, mean and time trend derived from LZU had a higher correlation with that produced by the CMA, and the centered normalized root-mean-square difference for this index derived from LZU and CMA was lower. LZU0025 had high correlation with the Coordinated Energy and Water Cycle Observation Project (CEOP) - Asian Monsoon Project, (CAMP) Tibet surface meteorology station dataset for air temperature, despite a non-significant correlation for precipitation at a few stations. Based on this comprehensive analysis, we conclude that LZU0025 is a reliable dataset. LZU0025, which has a fine resolution, can be used to identify a greater number of climate types, such as tundra and subpolar continental, along the Himalayan Mountain. We anticipate that LZU0025 can be used for the monitoring of regional climate change and precision agriculture modulation under global climate change.
HUANG Wei, ZHAO Hong
This dataset is blended by two other sets of data, snow cover dataset based on optical instrument remote sensing with 1km spatial resolution on the Qinghai-Tibet Plateau (1989-2018) produced by National Satellite Meteorological Center, and near-real-time SSM/I-SSMIS 25km EASE-grid daily global ice concentration and snow extent (NISE, 1995-2018) provided by National Snow and Ice Data Center (NSIDC, U.S.A). It covers the time from 1995 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 is covered by snow. The input data sources include daily snow cover products generated by NOAA/AVHRR, MetOp/AVHRR, and alternative to AVHRR taken from TERRA/MODIS corresponding observation, and snow extent information of NISE derived from observation by SSM/I or SSMIS of DMSP satellites. The processing method of data collection is as following: first, taking 1km snow cover product from optical instruments as initial value, and fully trusting its snow and clear sky without snow information; then, under the aid of sea-land template with relatively high resolution, replacing the pixels or grids where is cloud coverage, no decision, or lack of satellite observation, by NISE's effective terrestrial identification results. For some water and land boundaries, there still may be a small amount of cloud coverage or no observation data area that can’t be replaced due to the low spatial resolution of NISE product. Blended daily snow cover product achieves about 91% 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, CAO Guangzhen
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
The China Meteorological Forcing Dataset (CMFD) is a high spatial-temporal resolution gridded near-surface meteorological dataset that was developed specifically for studies of land surface processes in China. The dataset was made through fusion of remote sensing products, reanalysis dataset and in-situ observation data at weather stations. Its record starts from January 1979 and keeps extending (currently up to December 2018) with a temporal resolution of three hours and a spatial resolution of 0.1°. Seven near-surface meteorological elements are provided in CMFD, including 2-meter air temperature, surface pressure, specific humidity, 10-meter wind speed, downward shortwave radiation, downward longwave radiation and precipitation rate.
YANG Kun, HE Jie, WENJUN TANG , LU Hui, QIN Jun , CHEN Yingying, LI Xin
The distribution of lakes in space and its change over time are closely related to agricultural, environmental and ecological issues, and are critical factors for human socio-economic development. In the past decades, satellite based remote sensing has been developed rapidly to provide essential data sources for monitoring temporal lakes dynamics with its advantage of rapidness, wide coverage, and lower cost. This dataset was produced from Landsat images using the automated water detection method (Feng et al, 2015). We collected 96,278 Landsat images (about 25 terabytes) that acquired since 2000 with less than 80% cloud contamination in the arid region of central Asia and Tibetan Plateau. Water is detected in each of the image and then aggregated to monthly temporal resolution by taking advantage of the high-performance processing capability and large data storage provided by Global Land Cover Facility (GLCF) at University of Maryland. The results are validated systematically and quantitatively using manually interpreted dataset, which consists of a set of locations collected by a stratified random sampling strategy to effectively represent different spatial-temporal distributions in the region. The validation suggests high accuracy of the results (overall accuracy: 99.45(±0.59); user accuracy: 85.37%±(3.74); produce accuracy: 98.17(±1.05)).
FENG Min, CHE Xianghong
Digital Elevation Model (DEM) is a kind of solid ground Model that represents the ground Elevation in the form of a set of ordered numerical arrays. The third pole region of40°1′52″N~23°11′59″N、105°43′45″E~61°28′45″E of the roof of the world ecological geographic area,These include the qinghai-tibet plateau, the hengduan mountains, the Himalayas, the Hindu kush mountains and the pamirs plateau.Classified according to：At 4000 m altitude as a benchmark, the fusion of slope, reference mountain integrity and ecological system integrity, the spatial resolution of 0.008 ° x 0.008 °
National Aeronautics and Space Administration
The third pole 1:100,000 Water data set includes：Different grades of river lines（Tibet_River）、Polygonal drainage pattern（Tibet_Water_poly）vector space data set and its attribute name：Name（name）、Type（Type）、water leng（leng）、water area（Area）. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
Surface evapotranspiration (ET) is an important link of water cycle and energy transmission in the earth system. The accurate acquisition of ET is helpful to the study of global climate change, crop yield estimation, drought monitoring, and has important guiding significance for regional and even global water resources planning and management. With the development of remote sensing technology, remote sensing estimation of surface evapotranspiration has become an effective way to obtain regional and global evapotranspiration. At present, a variety of low and medium resolution surface evapotranspiration products have been produced and released in business. However, there are still many uncertainties in the model mechanism, input data, parameterization scheme of remote sensing estimation of surface evapotranspiration model. Therefore, it is necessary to use the real method. The accuracy of remote sensing estimation of evapotranspiration products was quantitatively evaluated by sex test. However, in the process of authenticity test, there is a problem of spatial scale mismatch between the remote sensing estimation value of surface evapotranspiration and the site observation value, so the key is to obtain the relative truth value of satellite pixel scale surface evapotranspiration. Based on the flux observation matrix of "multi-scale observation experiment of non-uniform underlying surface evaporation" in the middle reaches of Heihe River Basin from June to September 2012, the stations 4 (Village), 5 (corn), 6 (corn), 7 (corn), 8 (corn), 11 (corn), 12 (corn), 13 (corn), 14 (corn), 15 (corn), 17 (orchard) and the lower reaches of January to December 2014 Oasis Populus euphratica forest station (Populus euphratica forest), mixed forest station (Tamarix / Populus euphratica), bare land station (bare land), farmland station (melon), sidaoqiao station (Tamarix) observation data (automatic meteorological station, eddy correlator, large aperture scintillation meter, etc.) are used as auxiliary data, and the high-resolution remote sensing data (surface temperature, vegetation index, net radiation, etc.) are used as auxiliary data. See Fig. 1 for the distribution map. Considering the land Through direct test and cross test, six scale expansion methods (area weight method, scale expansion method based on Priestley Taylor formula, unequal weight surface to surface regression Kriging method, artificial neural network, random forest, depth belief network) were compared and analyzed, and finally a comprehensive method (on the underlying surface) was optimized. The area weight method is used when the underlying surface is moderately inhomogeneous; the unequal weight surface to surface regression Kriging method is used when the underlying surface is moderately inhomogeneous; the random forest method is used when the underlying surface is highly inhomogeneous) to obtain the relative true value (spatial resolution of 1km) of the surface evapotranspiration pixel scale of MODIS satellite transit instantaneous / day in the middle and lower reaches of the flux observation matrix area respectively, and to observe through the scintillation with large aperture. The results show that the overall accuracy of the data set is good. The average absolute percentage error (MAPE) of the pixel scale relative truth instantaneous and day-to-day is 2.6% and 4.5% for the midstream satellite, and 9.7% and 12.7% for the downstream satellite, respectively. It can be used to verify other remote sensing products. The evapotranspiration data of the pixel can not only solve the problem of spatial mismatch between the remote sensing estimation value and the station observation value, but also represent the uncertainty of the verification process. For all site information and scale expansion methods, please refer to Li et al. (2018) and Liu et al. (2016), and for observation data processing, please refer to Liu et al. (2016).
LIU Shaomin, LI Xiang , XU Ziwei
Surface evapotranspiration (ET) is an important variable that connects the land energy balance, water cycle and carbon cycle. The accurate acquisition of ET is helpful to the research of global climate change, crop yield estimation, drought monitoring, and it is of great significance to regional and global water resource planning and management. The methods of obtaining evapotranspiration mainly include ground observation, remote sensing estimation, model simulation and assimilation. The high-precision surface evapotranspiration data can be obtained by ground observation, but the spatial representation of observation stations is very limited; remote sensing estimation, model simulation and assimilation methods can obtain the spatial continuous surface evapotranspiration, but there are problems in the verification of accuracy and the rationality of spatial-temporal distribution pattern. Therefore, this study makes full use of a large number of high-precision station observation data, combined with multi-source remote sensing information, to expand the observation scale of ground stations to the region, to obtain high-precision, spatiotemporal distribution of continuous surface evapotranspiration. Based on the "Heihe River Integrated Remote Sensing joint experiment" (water), "Heihe River Basin Ecological hydrological process integrated remote sensing observation joint experiment" (hiwater), the accumulated station observation data (automatic meteorological station, eddy correlator, large aperture scintillation instrument, etc.), 36 stations (65 station years, distribution map is shown in Figure 1) are selected in combination with multi-source remote sensing data (land cover) Five machine learning methods (regression tree, random forest, artificial neural network, support vector machine, depth belief network) were used to construct different scale expansion models of surface evapotranspiration, and the results showed that: compared with The other four methods, random forest method, are more suitable for the study of the scale expansion of surface evapotranspiration from station to region in Heihe River Basin. Based on the selected random forest scale expansion model, taking remote sensing and air driven data as input, the surface evapotranspiration time-space distribution map (etmap) of Heihe River Basin during the growth season (May to September) from 2012 to 2016 was produced. The results show that the overall accuracy of etmap is good. The RMSE (MAPE) of upstream (las1), midstream (las2-las5) and downstream (las6-las8) are 0.65 mm / day (18.86%), 0.99 mm / day (19.13%) and 0.91 mm / day (22.82%), respectively. In a word, etmap is a high-precision evapotranspiration product in Heihe River Basin, which is based on the observation data of stations and the scale expansion of random forest algorithm. Please refer to Xu et al. (2018) for all station information and scale expansion methods, and Liu et al. (2018) for observation data processing.
LIU Shaomin, XU Tongren
The third pole 1:100,000 range data set includes:Mountains(Tibet_Mountains)vector space data set and its attribute name:Name(Name)、Countries Name（CNTRY_NAME）、Countries Referred to as（CNTRY_CODE）、Latitude（LATITUDE）、Longitude(LONGITUDE). The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
Third Pole 1:100,000 airport and runway data set include：airport（Tibet_Airport）and(Tibet_Airport_runways) vector space data set and its attribute name:Airport name(Name）、Name of airport（CNTRY_NAME）、Airport country abbreviation（CNTRY_CODE）、latitude（LATITUDE）、longitude（LONGITUDE）. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
The Third Pole 1:100,000 settlements distribution data set：Settlements（Tibet_Cities)、Capitals（Tibet_Capitals）、Cities up to 75K（Tibet_Cities_up_to_75K）vector space data set and its attribute name：Cities Name(ENG_NAME)、 urban population（CITY_POP) The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
Third pole 1:100,000 road data set includes: main road (Tibet_main_highways), road (Tibet_Road)and railway (Tibet_railway) vector space data set and its related attribute data :road names(Name), Type(Type) The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, D_WGS_1984 datum surface
ADC WorldMap
This data set provides a 1 km resolution Digital Elevation Model (DEM) of Antarctica. The DEM combines measurements from the European Remote Sensing Satellite-1 (ERS-1) Satellite Radar Altimeter (SRA) and the Ice, Cloud, and land Elevation Satellite (ICESat) Geosciences Laser Altimeter System (GLAS). The ERS-1 data are from two long repeat cycles of 168 days initiated in March 1994, and the GLAS data are from 20 February 2003 through 21 March 2008. The data set is approximately 240 MB comprised of two gridded binary files and two Environment for Visualizing Images (ENVI) header files viewable using ENVI or other similar software packages. The data are available via FTP.
National Aeronautics and Space Administration
The dataset of Antarctic mountains(1:1000000) includes vector spatial distribution data and some related attribute data: Name, Country name of the mountains , Country abbreviation, Latitude、 Longitude The data comes from the ADC World Map（1:1000000） global dataset, the data is topological, and it is the comprehensive, latest and seamless geographic digital data. The world map coordinate system is the latitude and longitude, WGS84 datum, and the Antarctic data set is the South Pole Stereographic.
ADC WorldMap
Antarctic 1:100,000 airport distribution data set includes vector space data and related attribute data of airports (Antarctic_Airport) and airport runways (Antarctic_Airport_runways)：Airport Name(Name), airport country Name（CNTRY_NAME）, airport country abbreviation（CNTRY_CODE）, LATITUDE, LONGITUDE. The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
Antarctic administrative boundary datasets consist of the properties of the state boundaries of the Antarctic states (properties properties), and the corresponding names and types of those properties :(CITY_POP), (ENG_NAME), (CNTRY_NAME), (TYPE), (CNTRY_CODE), (YEAR). The data comes from the 1:100,000 ADC_WorldMap global data set，The data through topology, warehousing and other data quality inspection，Data through the topology, into the library，It's comprehensive, up-to-date and seamless geodigital data. The world map coordinate system is latitude and longitude, WGS84 datum surface，Antarctic specific projection parameters（South_Pole_Stereographic）.
ADC WorldMap
The long-time series data set of snow cover area on the qinghai-tibet plateau is derived from the fusion of MODIS 005 version and IMS data set, andThe cloud-free products of daily snow cover area were obtained by using interpolation de-cloud algorithm.The projection is latitude and longitude, the spatial resolution is 0.005 degrees (about 500m), and the time is a long time series from January 1, 2003 to December 31, 2014. Each file is the result of the proportion of snow cover area on that day, and the value is 0-100 (%). It is the ENVI standard file, The naming convention： ims_mts_yyyyddd.tif, where YYYY stands for year and DDD stands for Julian day (001-365/366).Files can be directly used ENVI or ARCMAP software open view. Document description: 200 snow, 100 lake ice, 25 land, 37 sea
HAO Xiaohua
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