A long-term (1980-2017) land evaporation (E) product with a spatial resolution of 0.25 degree. This is a merged product from three model-based E products using the Reliability Ensemble Averaging (REA) method which minimizes errors. These include the fifth-generation ECMWF Re-Analysis (ERA5), the second Modern-Era Retrospective analysis for Research and Applications (MERRA2), and the Global Land Data Assimilation System (GLDAS). To facilitate user-friendly access and download the dataset is stored individually for each year in a separate file. These files contain daily and monthly mean data (e.g., REA_1980_day.nc and REA_1980_mon.nc). The dataset is stored in NetCDF format, containing the variable E, representing land evaporation, produced in millimeters (mm) as a unit. There are three dimensions included in the dataset: longitude, latitude, and time, with the longitude ranging from -179.875E to 179.875E, the latitude from -59.875N to 89.875N. Complete time coverage is from January 1, 1980, to December 31, 2017.
0 2022-04-18
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.
0 2022-04-18
The data set records the statistical table of groundwater level dynamic changes in various monitoring areas of Qinghai Province from 2015 to 2018. The data are recorded from the Department of natural resources of Qinghai Province, and the data set contains four data tables, which are: the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2015, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2016, the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2017, and the statistical table of groundwater level dynamic change in each monitoring area of Qinghai Province in 2018 The data table has the same structure and contains 7 fields Field 1: "geographic location" Field 2: "basic balance area (km2)" Field 3: "percentage of monitoring area (%)" Field 4: "weak descent area (km2)" Field 5: "percentage (%) of monitored area" Field 6: "strong uplift area (km2)" Field 7: "percentage (%) of monitored area"
0 2022-04-18
The data consists of three fields: longitude, latitude and lake depth. Using sonar equipment to measure the depth of water on the lake, GPS synchronous measurement of longitude and latitude. The salinity and temperature data of lake water are used to correct the depth data measured by sonar, and the outliers are eliminated. The underwater topographic map of lake can be formed by interpolation of water depth data. Using the underwater topographic map, the water storage of lakes can be calculated and the total water quantity of lakes in the Qinghai Tibet Plateau can be evaluated. The underwater topographic map combined with remote sensing data can also be used to study the characteristics and influencing factors of lake water quantity variation in the Qinghai Tibet Plateau, which is an important part of the study of water quantity variation in the Asian water tower.
0 2022-04-18
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.
0 2022-04-18
The data set is the vegetation coverage in Central Asia including three temperate deserts, the Karakum, Kyzylkum and Muyunkun Deserts, and one of the world's largest arid zones. This is the MODIS-NDVI data set calculated by using the NDVI and the vegetation coverage in arid region. The space and time resolutions are 500 m and 16 days, respectively. The time is from 01, January, 2017 to 18, December, 2017. The data set uses the the Geodetic coordinate system. It can be used for the investigation of the Desert oil and gas field, and oasis cities.
0 2022-04-18
The data set records the statistical data of grassland type area and livestock carrying capacity in Haidong area of Qinghai Province in 1988 and 2012. The data are classified and counted according to the grassland group code, such as: I represents Alpine dry grassland, II represents mountain dry grassland, III represents Alpine desert, B represents medium grass group, J represents shrub group, etc, For specific grassland group type codes and their corresponding meanings, see "description of grassland group type codes in Qinghai Province. PDF" in the data set. The data are compiled from the grassland station of Qinghai Province and the grassland resources statistics of Qinghai Province issued in 1988 and 2012. The data set contains three data tables, which are: statistical data of grassland area and livestock carrying capacity of various types in Haidong area (1988), statistical data of grassland area and livestock carrying capacity in Haidong area (2012) and description of grassland group code in Qinghai Province. The data table structure is similar. For example, there are 8 fields in the statistical data (2012) of grassland type, area and livestock carrying capacity in Haidong area: Field 1: type code Field 2: grassland type name Field 3: grassland area Field 4: available area of grassland Field 5: average unit yield of fresh grass Field 6: average unit yield of edible fresh grass Field 7: stocking capacity Field 8: grassland type, etc
0 2022-04-18
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.
0 2022-04-18
Under the funding of the first project (Development of Multi-scale Observation and Data Products of Key Cryosphere Parameters) of the National Key Research and Development Program of China-"The Observation and Inversion of Key Parameters of Cryosphere and Polar Environmental Changes", the research group of Zhang, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, developed the snow depth downscaling product in the Qinghai-Tibet Plateau. The snow depth downscaling data set for the Tibetan Plateau is derived from the fusion of snow cover probability dataset and Long-term snow depth dataset in China. The sub-pixel spatio-temporal downscaling algorithm is developed to downscale the original 0.25° snow depth dataset, and the 0.05° daily snow depth product is obtained. By comparing the accuracy evaluation of the snow depth product before and after downscaling, it is found that the root mean square error of the snow depth downscaling product is 0.61 cm less than the original product. The details of the product information of the Downscaling of Snow Depth Dataset for the Tibetan Plateau (2000-2018) are as follows. The projection is longitude and latitude, the spatial resolution is 0.05° (about 5km), and the time is from September 1, 2000 to September 1, 2018. It is a TIF format file. The naming rule is SD_yyyyddd.tif, where yyyy represents year and DDD represents Julian day (001-365). Snow depth (SD), unit: centimeter (cm). The spatial resolution is 0.05°. The time resolution is day by day.
0 2022-04-18
Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60% of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 K, the mean absolute error (MAE) varies from 1.23 to 1.37 K and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1K (R>0:71, P<0:05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. More detail please refer to Zhao et al (2020). doi.org/10.5281/zenodo.3528024
0 2022-04-18
The Land Surface Temperature in China dataset contains land surface temperature data for China (about 9.6 million square kilometers of land) during the period of 2003-2017, in Celsius, in monthly temporal and 5600 m spatial resolution. It is produced by combing MODIS daily data(MOD11C1 and MYD11C1), monthly data(MOD11C3 and MYD11C3) and meteorological station data to reconstruct real LST under cloud coverage in monthly LST images, and then a regression analysis model is constructed to further improve accuracy in six natural subregions with different climatic conditions.
0 2020-12-23
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