The data set consists of four sub tables, which are remote sensing monitoring of Lake area from 2000 to 2019, total lake water storage based on underwater 3D simulation model, Lake area volume equation based on underwater 3D simulation model, and key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province. The first sub table is the time series Lake area data from 2000 to 2019 from remote sensing image data monitoring. The third sub table stores the area storage capacity equation of the lake based on the underwater three-dimensional simulation model of the lake. The second sub table is the estimation result by combining the time series Lake area data and the area storage capacity equation, Finally, the key parameters and results of water storage measurement and Simulation of 24 typical lakes in Qinghai Province from 2000 to 2019 are obtained, including simulated water depth, maximum water depth, simulated reference water level and corresponding Lake area of each lake, which are stored in the fourth sub table.
FANG Chun, LU Shanlong, JU Jianting, TANG Hailong
The global monthly all-sky land surface temperature (2000-2020) is produced by the method from Chen et al. 2017 JHM.
CHEN Xuelong, BOB Su, MA Yaoming
Thematic data on desertification in Western Asia, includes two parts: Distribution Map of Sandy Land in Western Asia, Distribution Map of Grassland Degradation in Western Asia. The spatial resolution of the data is 30m. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences, the spatial resolution of data is 30 m. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101. The map of artificial oasis pattern in Amu river basin is based on Landsat TM and ETM image data in 2015. Firstly, with the help of eCognition software, the object-oriented classification is carried out. Secondly, the classification results are checked and corrected manually.
Lakes on the Tibetan Plateau (TP) are an indicator and sentinel of climatic changes. We extended lake area changes on the TP from 2010 to 2021, and provided a long and dense lake observations between the 1970s and 2021. We found that the number of lakes, with area larger than 1 k㎡ , has increased to ~1400 in 2021 from ~1000 in the 1970s. The total area of these lakes decreased between the 1970s and ~1995, and then showed a robust increase, with the exception of a slight decrease in 2015. This expansion of the lakes on the highest plateau in the world is a response to a hydrological cycle intensified by recent climate changes.
ZHANG Guoqing
The multi-decadal lake number and area changes in China during 1960s–2020 are derived from historical topographic maps and >42151 Landsat satellite images, including lakes as fine as ≥1 km^2 in size for the past 60 years (1960s, 1970s, 1990, 1995, 2000, 2005, 2010, 2015, 2020). From the 1960s to 2020, the total number of lakes (≥ 1 km ^ 2) in China increased from 2127 to 2621, and the area expanded from 68537 km ^ 2 to 82302 km ^ 2.
ZHANG Guoqing
This dataset is based on the long sequence (1981-2013)normalized difference vegetation index product(Version 3) of the latest NOAA Global Inventory Monitoring and Modeling System (GIMMS). First, the NDVI data products were re-sampled from the spatial resolution of 1/12 degree to 0.5 degree, then the time series of every year was smoothed by the double-logistic method, and the smoothed curvature was calculated. The maximum curvature of spring was selected as the returning green stage of the vegetation in Spring. This data can be used to analyze the temporal and spatial characteristics of the Holarctic vegetation phenology in Spring.
XU Xiyan
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
The dataset is the land cover of Qing-Tibet Plateau in 2010. The data format is a TIFF file, spatial resolution is 300 meters, including crop land, grassland, forest land, urban land, and so on. The dataset offers a geographic fundation for studying the interaction between urbanization and ecological reservation of Qing-Tibet Plateau. This land cover data is a product of CCI-LC project conducted by European Space Agency. The coordinate reference system of the dataset is a geographic coordinate system based on the World Geodetic System 84 reference ellipsoid. There are 22 major classes of land covers. The data were generated using multiple satellite data sources, including MERIS FR/RR, AVHRR, SPOT-VGT, PROBA-V. Validation analysis shows the overall accuracy of the dataset is more than 70%, but it varies with locations and land cover types.
DU Yunyan
Thematic data on desertification (land desertification, salinization and vegetation degradation) in Central Asia, includes three parts: Distribution Map of Sandy Land in Central Asia, Distribution Map of Salinized Land in Central Asia and Distribution Map of Land Vegetation Degradation in Central Asia. The spatial resolution of the data is 1km, the time resolution is in 2015. The data produced by the key laboratory of remote sensing and GIS, Xinjiang institute of ecology and geography, Chinese Academy of Sciences. Data production Supported by the Strategic Priority Research Program of Chinese Academy of Sciences, Grant No. XDA20030101.
XU Wenqiang
The data include soil organic matter data of Tibetan Plateau , with a spatial resolution of 1km*1km and a time coverage of 1979-1985.The data source is the soil carbon content generated from the second soil census data.Soil organic matter mainly comes from plants, animals and microbial residues, among which higher plants are the main sources.The organisms that first appeared in the parent material of primitive soils were microorganisms.With the evolution of organisms and the development of soil forming process, animal and plant residues and their secretions become the basic sources of soil organic matter.The data is of great significance for analyzing the ecological environment of Tibetan Plateau
FANG Huajun
The sand drift potential data sets of Central Asia in 2017 is in tif format. It covers five countries in Central Asia, including Uzbekistan, Tajikistan, Kyrgyzstan, Kazakhstan and Turkmenistan. The sand drift potential is absolutely drift potential, that is, the sum of the flux in all directions, regardless of the direction of the potential. The data was obtained by GLDAS global three-hour assimilation data extraction calculation. The temporal resolution is month, the spatial resolution is 0.25°, and the time range is 2017. This data set can be used as an important reference data for sand storm disaster assessment.
GAO Xin
The SRTM sensor has two bands, namely C-band and X-band. The SRTM we are using now comes from the C-band. The publicly released SRTM digital elevation products include DEM data at three different resolutions: * SRTM1 covers only the continental United States, with a spatial resolution of 1s; * SRTM3 data covers the world with a spatial resolution of 3s. This is the most widely used dataset. The elevation reference of SRTM3 is the geoid of EGM96 and the horizontal reference is WGS84. The nominal absolute elevation accuracy is ± 16m, and the absolute plane accuracy is ± 20m. * SRTM30 data also covers the world, with a resolution of 30s. There are multiple versions of SRTM data. The early SRTM data was completed by NASA's "JPL" (Jet Propulsion Laboratory) ground data processing system (GDPS). The data is called SRTM3- 1. The National Geospatial Intelligence Agency has further processed the data, and the lack of data has been significantly improved. The data is called SRTM3-2. This dataset is mainly the fourth version of SRTM terrain data obtained by CIAT (International Center for Tropical Agriculture) using a new interpolation algorithm. This method better fills the SRTM 90 data hole. The interpolation algorithm comes from Reuter et al. (2007). The data of SRTM is organized as follows: every 5 latitude and longitude grids is divided into a file, which are divided into 24 rows (-60 to 60 degrees) and 72 columns (-180 to 180 degrees). The file naming rule is srtm_XX_YY.zip, where XX indicates the number of columns (01-72), and YY indicates the number of rows (01-24). The resolution of the data is 90 m. Data use: SRTM data uses a 16-bit value to represent the elevation value (-/ + / 32767 meters), the maximum positive elevation is 9000 meters, and the negative elevation (12,000 meters below sea level). -32767 standard for empty data.
CGIAR-CSI
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