Satellite remote sensing provides an efficient pathway to map inland surface water extent across different spatial and temporal scales. However, how to monitor the surface water distribution and its spatiotemporal variability via combining optical and radar remote sensing datasets still faces substantial challenges. A monthyly surface water data set of China derived from optical and radar remote sensing (2018-2020) is provided. The dataset was generated by Seamless Surface Water Mapping Framework (SSWMF) proposed by Yang et al. (2022). The validity of this dataset was further proved over China with an overall accuracy of 92.39% and Kappa coefficient of 0.83. With seamless surface water monitoring, the changes of surface water area can be potentially used to characterize the drought/flood process and evaluate the natural hazard impact.
YANG Yongmin
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
Based on Landsat data (kh-9 data in 1976 as auxiliary data), glacial lake data of nearly 40 years (1970s-2018) in the western Nyainqentanglha range were obtained by manual digitization and visual interpretation. The variation characteristics of glacial lake over 0.0036 square kilometers in terms of type, size, elevation and watershed were analyzed in detail. The results show that, between 1976 and 2018, the number of glacial lakes increased by 56% from 192 to 299 and their total area increased by 35% from 6.75 ± 0.13 square kilometers to 9.12 ± 0.13 square kilometers ; the type of glacial lake is changing obviously; the smaller glacial lake is changing faster; the expansion of glacial lake is developing to higher altitude.
LUO Wei, ZHANG Guoqing
In 1970, land use was visually interpreted from MSS images, with an overall interpretation accuracy of more than 90%. Land classification was carried out in accordance with the land use classification system of the Chinese Academy of Sciences. For detailed classification rules, please read the data description document. The 2005 and 2015 data sets were collected from the European Space Agency (ESA) Data acquisition of global land cover types includes five Central Asian countries (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan) and Xinjiang, China. There are 22 land use types in the data set. The IPCC land use classification system is adopted. Please refer to the documentation for specific classification details.
LUO Geping
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 (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 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
Using the Landsat8 OLI images at the summerof 2015, the spectral characteristics of satellite sensors were extracted in the Belt and Road's region. The bands included the band (0.45 - 0.51μm)、band (0.53 - 0.59μm)、band (0.64 - 0.67μm)、band (0.85 - 0.88μm)、band (1.57 - 1.65μm)、band (2.11 - 2.29 μm)、band (10.60 - 11.19 μm)和band (11.50 - 12.51 μm). And the Land cover data of the Belt and Road's region (Version 1.0) (2015) was used to extract the land cover/use at each location. Data includes the format of excel and shp. The data of shp format includes the spatial distribuition and the spectral characteristics of each sampling point.
XU Erqi
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.
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
The Qinghai Tibet Plateau belongs to the plateau mountain climate. The temperature and its seasonal variation have been one of the hot spots in the global climate change research. The data includes the temperature data of Qinghai Tibet Plateau, with spatial resolution of 1km * 1km, temporal resolution of month and year, and time coverage of 2000, 2005, 2010 and 2015. The data are obtained by Kring interpolation on the data of national weather station in Qinghai Tibet Plateau. The data can be used to analyze the temporal and spatial distribution of air temperature in the Qinghai Tibet Plateau. In addition, the data can also be used to analyze the law of temperature change with time in the Qinghai Tibet Plateau, which is of great significance to the study of the ecological environment of the Qinghai Tibet Plateau.
FANG Huajun
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
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
Greenland digital elevation models (DEMs) are indispensable to fieldwork, ice velocity calculations, and mass change estimations. Previous DEMs have provided reasonable estimations for the entire Greenland, but the time span of applied source data may lead to mass change estimation bias. To provide a DEM with a specific time-stamp, we applied approximately 5.8×108 ICESat-2 observations from November 2018 to November 2019 to generate a new DEM, including the ice sheet and glaciers in peripheral Greenland. A spatiotemporal model fit process was performed at 500 m, 1,2, and 5 km grid cells separately, and the final DEM was posted at the modal resolution of 500 m. A total of 98% of the grids were obtained by the model fit, and the remaining DEM gaps were estimated via the ordinary Kriging interpolation method. Compared with IceBridge mission data acquired by the Airborne Topographic Mapper (ATM) Lidar system, the ICESat-2 DEM was estimated to have a maximum median difference of -0.48 m. The performance of the grids obtained by model fit and interpolation was similar, which both agreed well with the IceBridge data. DEM uncertainty rises in regions of low latitude and high slope or roughness. Furthermore, the ICESat-2 DEM showed significant accuracy improvements compared with other altimeter-derived DEMs, and the accuracy was comparable to those derived from stereo-photogrammetry and interferometry. Overall, the ICESat-2 DEM showed excellent accuracy stability under various topographic conditions, which can provide a specific time-stamped DEM with high accuracy that will be useful to study Greenland elevation and mass balance changes.
FAN Yubin, KE Changqing, SHEN Xiaoyi
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
Ecological carrying capacity refers to the maximum population scale with a certain level of social and economic development that can be sustainably carried by the ecosystem without damaging the production capacity and functional integrity of the ecosystem, per person/square kilometer. Spatial distribution data of ecological carrying capacity were calculated based on NPP data simulated by VPM model and FAO production and trade data of agriculture, forestry and animal husbandry. Based on NPP data and combined with the land use data of cci-ci and biomass ratio parameters of various ecosystems, ANPP data was obtained to serve as ecological supply quantity. Based on agricultural, forestry and animal husbandry production and trade data and combined with population data, per capita ecological consumption standards of countries along the One Belt And One Road line were obtained, and then national scale data space was rasterized. The spatial rasterized ecological bearing data are obtained by dividing the ecological supply data with the per capita ecological consumption standard.
YAN Huiming
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
This data set includes land cover classification products of 30 meters in Qilian mountain area from 1985 to 2019. Firstly, the product uses Landsat-8/OLI to construct the 2015 time series data. According to the different NDVI time series curves of various ground features, the knowledge of different features is summarized, the rules are set to extract different features, and the land cover classification map in 2015 is obtained. The classification system refers to IGBP classification system and from_ LC classification system can be divided into 10 categories: cultivated land, woodland, grassland, shrub, wetland, water body, impervious surface, bare land, glacier and snow. According to the accuracy evaluation of Google Earth HD images and field survey data, the overall accuracy of land cover classification products in 2015 was as high as 92.19%. Based on the land cover classification products in 2015, based on the Landsat series data and strong geodetic data processing ability of Google Earth engine platform, the land cover classification products from 1985 to 2019 are produced by using the idea and method of change detection. By comparing the classification products, it is concluded that the land cover classification products based on Google Earth engine platform have good consistency with the classification products based on time series method. In short, the land cover data set in the core area of Qilian Mountain has high overall accuracy, and the method based on Google Earth engine platform sample training can expand the existing classification products in time and space, and can reflect more land cover type change information in a long time series.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
This data set includes the distribution products of 30 m cultivated land and construction land in Qilian mountain area from 1985 to 2019. The product comes from the land cover classification products of 30m in Qilian mountain area from 1985 to 2019. NDVI products, light data products, DEM products and SAR data of sentry 1 are used in the production of the products. The total accuracy of the product is better than 85%. Among them, the peoducts from 1985-2015 have a 5 year- time resolution, and the other products have a 1 year - time resolution.
YANG Aixia, ZHONG Bo, JUE Kunsheng, WU Junjun
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
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