The basic data set of remote sensing for ecological assets assessment of the Qinghai-Tibet Plateau includes the annual Fraction Vegetation Coverage (FVC), Net Primary Productivity (NPP) and Leaf Area Index (LAI) of the Qinghai-Tibet Plateau since 2000, and other ecological parameters based on remote sensing inversion. The FVC data are mainly developed from MODIS NDVI data. NPP estimation method based on algorithm of CASA model.

This dataset is derived from the paper: Ding, J., Wang, T., Piao, S., Smith, P., Zhang, G., Yan, Z., Ren, S., Liu, D., Wang, S., Chen, S., Dai, F., He, J., Li, Y., Liu, Y., Mao, J., Arain, A., Tian, H., Shi, X., Yang, Y., Zeng, N., & Zhao, L. (2019). The paleoclimatic footprint in the soil carbon stock of the Tibetan permafrost region. Nature Communications, 10(1), 4195. doi:10.1038/s41467-019-12214-5. This data contains R code and a new estimate of Tibetan soil carbon pool to 3 m depth, at a 0.1° spatial resolution. Previous assessments of the Tibetan soil carbon pools have relied on a collection of predictors based only on modern climate and remote sensing-based vegetation features. Here, researchers have merged modern climate and remote sensing-based methods common in previous estimates, with paleoclimate, landform and soil geochemical properties in multiple machine learning algorithms, to make a new estimate of the permafrost soil carbon pool to 3 m depth over the Tibetan Plateau, and find that the stock (38.9-34.2 Pg C) is triple that predicted by ecosystem models (11.5 ± 4.2 Pg C), which use pre-industrial climate to initialize the soil carbon pool. This study provides evidence that illustrates, for the first time, the bias caused by the lack of paleoclimate information in ecosystem models. The data contains the following fields: Longitude (°E) Latitude (°N) SOCD (0-30cm) (kg C m-2) SOCD (0-300cm) (kg C m-2) GridArea (k㎡) 3mCstcok (10^6 kg C)

This dataset is the spatial distribution map of the marshes in the source region of the Yellow River near the Zaling Lake-Eling Lake, covering an area of about 21,000 square kilometers. The data set is classified by the Landsat 8 image through an expert decision tree and corrected by manual visual interpretation. The spatial resolution of the image is 30 m, using the WGS 1984 UTM projected coordinate system, and the data format is grid format. The image is divided into five types of land, the land type 1 is “water body”, the land type 2 is “high-cover vegetation”, the land type 3 is “naked land”, and the land type 4 is “low-cover vegetation”, and the land type 5 is For "marsh", low-coverage vegetation and high-coverage vegetation are distinguished by vegetation coverage. The threshold is 0.1 to 0.4 for low-cover vegetation and 0.4 to 1 for high-cover vegetation.

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%.

The data set is based on the reflectance of MODIS channels and the observation data of SIF to establish the neural network model, so as to obtain the SIF data with high spatial and temporal resolution, which is often used as a reference for primary productivity. The data is from Zhang et al. (2018), and the specific algorithm is shown in the article. The source data range is global, and the Qinghai Tibet plateau region is selected in this data set. This data integrates the original 4-day time scale data into the monthly data. The processing method is to take the maximum value of the month, so as to achieve the effect of removing noise as much as possible. This data set is often used to evaluate the temporal and spatial patterns of vegetation greenness and primary productivity, which has practical significance and theoretical value.

Based on 2015 ESA global land cover data (ESA GlobCover), combined with the Tsinghua university global land cover data (FROM GLC)、NASA MODIS global land cover data (MCD12Q1)、University of Maryland global land data (UMD)、USGS global land data (IGBP DISCover),we build the LUC classification system in the Tibet Plateau and the rest of the data transformation rules of the classification system. We also build the land cover classification confidence function and the rules of fusing land classification to finish the Integration and modification of land cover products and finally complet the land use data in the Tibet Plateau V1.0.

The Tibetan Plateau has an average altitude of over 4000 m and is the region with the highest altitude and the largest snow cover in the middle and low latitudes of the Northern Hemisphere regions. Snow cover is the most important underlying surface of the seasonal changes on the Tibetan Plateau and an important composing element of ecological environment. Ice and snow melt water is an important water resource of the plateau and its downstream areas. At the same time, plateau snow, as an important land-surface forcing factor, is closely related to disastrous weather (such as droughts and floods) in East Asia, the South Asian monsoon and in the middle and lower reaches of the Yangtze River. It is an important indicator of short-term climate prediction and one of the most sensitive responses to global climate change. The snow depth refers to the vertical depth from the surface of the snow to the ground. It is an important parameter for snow characteristics and one of the conventional meteorological observation elements. It is the key parameter of snow water equivalent estimation, climate effect studies of snow cover, the basin water balance, the simulation and monitoring of snow-melt, and snow disaster evaluation and grading. In this data set, the Tibetan Plateau boundary was determined by adopting the natural topography as the leading factor and by comprehensive consideration of the principles of altitude, plateau and mountain integrity. The main part of the plateau is in the Tibetan Autonomous Region and Qinghai Province, with an area of 2.572 million square kilometers, accounting for 26.8% of the total land area of China. The snow depth observation data are the monthly maximum snow depth data after quality detection and quality control. There are 102 meteorological stations in the study area, most of which were built during the 1950s to 1970s. The data for some months or years for sites existing during this period were missing, and the complete observational records from 1961 to 2013 were adopted. The temporal resolution is daily, the spatial coverage is the Tibetan Plateau, and all the data were quality controlled. Accurate and detailed plateau snow depth data are of great significance for the diagnosis of climate change, the evolution of the Asian monsoon and the management of regional snow-melt water resources.

Based on the Tibetan Plateau wetland pattern in the 1970s interpreted using the Mire Map of China compiled by the scientific expeditions and the Tibetan Plateau wetland pattern in the 2000s interpreted using Landsat TM (resolution: 30 m) satellite image data, The Mire Map of China in the 1970s was interpreted. Visual interpretation of Landsat TM images from 2006 to 2009: a) Based on the natural zoning of the whole district, the interpretation keys of different wetland types were established with reference to the data obtained by different physical geography units and actual surveys. b) Based on the established interpretation keys, wetlands with an area greater than 10 square kilometers were primarily extracted by artificial visual interpretation method (excluding permanent, seasonal rivers and riverbeds). c) According to the interpretation results in combination with the topographic map (resolution: 90 m) of the study area and the actual situation of the wetland plaque investigation within the study area, the plaque modification and supplementation were artificially carried out. The data of the 1970s were obtained by interpretation of the Mire Map of China compiled by the Tibetan Plateau scientific expeditions of the Changchun Institute of Geography. The wetland data of the 2000s was derived from Landsat TM (resolution: 30 m) satellite image data. The data are of good quality.

This data set is the water resources data of the Qinghai Tibet Plateau from 1990 to 2010, which is the sum of renewable surface and groundwater resources. The data is in vector format and the spatial resolution is in the scale of prefecture level administrative units. The data is obtained by checking the results of VIC (variable injection capacity) hydrological model. The simulated water resources are the sum of the surface runoff and underground runoff in the output results of hydrological simulation. The simulation results are verified by comparing with the runoff data of the measured stations. According to the statistics of water resources at the provincial level in China water resources bulletin, a correction coefficient α is introduced at the provincial level, so that the product of water resources and α in the hydrological model simulation province is equal to the statistics of water resources. Then the amount of water resources in the administrative unit is the product of the total amount of water resources and α.

A comprehensive understanding of the permafrost changes in the Qinghai Tibet Plateau, including the changes of annual mean ground temperature (Magt) and active layer thickness (ALT), is of great significance to the implementation of the permafrost change project caused by climate change. Based on the CMFD reanalysis data from 2000 to 2015, meteorological observation data of China Meteorological Administration, 1 km digital elevation model, geo spatial environment prediction factors, glacier and ice lake data, drilling data and so on, this paper uses statistics and machine learning (ML) method to simulate the current changes of permafrost flux and magnetic flux in Qinghai Tibet Plateau The range data of mean ground temperature (Magt) and active layer thickness (ALT) from 2000 to 2015 and 2061 to 2080 under rcp2.6, rcp4.5 and rcp8.5 concentration scenarios were obtained, with the resolution of 0.1 * 0.1 degree. The simulation results show that the combination of statistics and ML method needs less parameters and input variables to simulate the thermal state of frozen soil, which can effectively understand the response of frozen soil on the Qinghai Tibet Plateau to climate change.

River lake ice phenology is sensitive to climate change and is an important indicator of climate change. 308 excel file names correspond to Lake numbers. Each excel file contains six columns, including daily ice coverage information of corresponding lakes from July 2002 to June 2018. The attributes of each column are: date, lake water coverage, lake water ice coverage, cloud coverage, lake water coverage and lake ice coverage after cloud treatment. Generally, the ice cover area ratio of 0.1 and 0.9 is used as the basis to distinguish the lake ice phenology. The excel file contained in the data set can further obtain four lake ice phenological parameters: Fus, fue, bus, bue, and 92 lakes. Two parameters, Fus and bue, can be obtained.

The data set is remote sensing image of Resource 3 No. 02 (ZY3-02). ZY3-02 was successfully launched from Taiyuan Satellite Launch Center at 11:17 on May 30, 2016 by Long March 4 B carrier rocket. China-made satellite imagery will be further strengthened in the areas of land surveying and mapping, resource survey and monitoring, disaster prevention and mitigation, agriculture, forestry and water conservancy, ecological environment, urban planning and construction, transportation and other fields. List of files: ZY302_PMS_E98.8_N37.4_201707_L1A0000156704 ZY302_PMS_E100.4_N37.0_20171127_L1A0000217243 ZY302_TMS_E99.5_N37.0_20170717_L1A0000160059 ZY302_TMS_E100.3_N36.6_20171127_L1A0000217279 ZY302_TMS_E100.4_N37.0_20170529_L1A0000139947 Folder Naming Rules: Satellite Name Sensor Name Central Longitude Central Latitude Acquisition Time L1****