The Antarctic Peninsula is also called "Palmer peninsula" or "Graham land". Located in the southwest polar continent, it is the largest peninsula in the Antarctic continent and the farthest peninsula extending northward into the ocean (63 ° south latitude), bordering the Weddell Sea and berengske sea in the East and West. The Antarctic Peninsula is known as the "tropics" of Antarctica. This is a typical sub polar marine climate. Compared with the Antarctic continent, it is one of the warmest and wettest regions in Antarctica. There are a small number of pioneer plants distributed on the islands in the marginal area, mainly bryophytes and lichens. The spectrum and annotation data of Antarctic Peninsula and its surrounding plants are the spectral data of 37 sample points in 9 regions of Fildes Peninsula and Adeli island around the Antarctic Peninsula on January 7-22, 2018, which provide the background information for the study of the distribution and change of Antarctic plants.
0 2022-03-28
Supported by the Strategic Priority Research Program of the Chinese Academy of Science (XDA19070100). Tao Che, the director of this program, who comes from Key Laboratory of Remote Sensing of Gansu Province, Northwest Institute of Eco-Environment and Resources, CAS. They used machine learning methods combined with multi-source gridded snow depth product data to derive a long-time series over the Northern Hemisphere. Firstly, the applicability of artificial neural network (ANN), support vector machine (SVM) and random forest (RF) method in snow depth fusion are compared. It is found that random forest method shows strong advantages in snow depth data fusion. Secondly, using the random forest method, combined with remote sensing snow depth products such as AMSR-E, AMSR-2, NHSD and GlobSnow and reanalysis data such as ERA-Interim and MERRA-2. These gridded snow depth products and environmental factor variables are used as the input independent variables of the model. In situ observations of China Meteorological Station (945), Russia Meteorological Station (620), Russian snow survey data (514), and global historical meteorological network (41261) are used as reference truth to train and verify the model. The daily gridded snow depth dataset of the snow hydrological year from 1980 to 2019 (September 1 of the previous year to May 31 of the current year) is prepared on the cloud platform provided by the CASEarth. Since the passive microwave brightness temperature data from 1980 to 1987 is the data of every other day, there will be a small number of missing trips in the data during this period. Using the ESM-SnowMIP and independent ground observation data for verification, the quality of the fusion data set has been improved. According to the comparison between the ground observation data and the snow depth products before fusion, the determination coefficient (R2) of the fusion data is increased from 0.23 (GlobSnow snow depth product) to 0.81, and the corresponding root mean square error (RMSE) and mean absolute error (MAE) are also reduced to 7.7 cm and 2.7 cm.
0 2022-03-22
The energy supply resilience of the countries along the Belt and Road reflects the level of energy supply resilience of the countries along the Belt and Road, and the higher the value of the data, the stronger the energy supply resilience of the countries along the Belt and Road. "The energy supply resilience data for countries along the "Belt and Road" are prepared with reference to the International Energy Agency (IEA) national energy statistics (https://www.iea.org/data-and-statistics), using the 2000-2019 The energy supply resilience product was prepared based on sensitivity and adaptability analysis, using year-by-year data on coal, oil and natural gas supply in countries along the "Belt and Road", and taking into account the year-by-year changes of each energy source.
0 2022-03-10
The data set includes ASTER GDEM data and its Mosaic. ASTER Global DEM (ASTER GDEM) is a Global digital elevation data product jointly released by NASA and Japan's ministry of economy, trade and industry (METI) on June 29, 2009. The DEM data is based on the observation results of NASA's new earth observation satellite TERRA.It is produced by the ASTER(Advanced Space borne Thermal Emission and Reflection Radio meter) sensor, which collects 1.3 million stereo image data, covering more than 99% of the earth's land surface.The data has a horizontal accuracy of 30 m (95% confidence) and an elevation accuracy of 7-14 m (95% confidence).This data is the third global elevation data, which is significantly higher than previous SRTM3 DEM and GTOPO30 data. We from NASA's web site (http://wist.echo.nasa.gov/api) to download the data of heihe river basin, and through the data center to distribute.The data distributed by the center completely retains the original appearance of the data without any modification to the data.If users need details about ASTER GDEM preparation process, please refer to the data documents of metadata connections, or visit http://www.ersdac.or.jp/GDEM/E/3.html or directly from https://lpdaac.usgs.gov/ reading and ASTER Global DEM related documents. ASTER GDEM is divided into several data blocks of 1×1 degree in distribution, and the distribution format is zip compression format. Each compressed file includes three files. The file naming format is as follows: ASTGTM_NxxEyyy_dem.tif ASTGTM_NxxEyyy_num.tif reademe.pdf Where xx is the starting latitude and yyy is the starting longitude._dem. Tif is the dem data file, _num. Tif is the data quality file, and reademe is the data description file. In order to facilitate users to use the data, on the basis of the fractional ASTER GDEM data, we splice fractional SRTM data to prepare the ASTER GDEM Mosaic map of the black river basin, which retains all the original features of ASTER GDEM without any resamulation. This data includes two files: heihe_aster_gdem_mosaic_dem.img Heihe_Aster_GDEM_Mosaic_num. Img The data is stored in the format of Erdas image, where the file _dem.img is the dem data file and the file _num. Img is the data quality file.
0 2022-03-02
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.
0 2022-03-02
This data set includes the daily averages of the temperature, pressure, relative humidity, wind speed, precipitation, global radiation, P2.5 concentration and other meteorological elements observed by the Qomolangma Station for Atmospheric and Environmental Observation and Research from 2005 to 2016. The data are aimed to provide service for students and researchers engaged in meteorological research on the Tibetan Plateau. The precipitation data are observed by artificial rainfall barrel, the evaporation data are observed by Φ20 mm evaporating pan, and all the others are daily averages and ten-day means obtained after half hour observational data are processed. All the data are observed and collected in strict accordance with the Equipment Operating Specifications, and some obvious error data are eliminated when processing the generated data.
0 2022-03-02
Snow over sea ice controls the energy budgets, affects the sea ice growth/melting, and thus has essential climatic effects. Snow depth, one of the fundamental properties of snow cover, is essential for understanding of the rapid change in Antarctic climate and for sea ice thickness estimation. Passive microwave radiometer can be used for basin-scale snow depth estimation in daily scale, however, previous published methods applied for Antarctic snow depth shows clear underestimation, which limits their further application. Here, we construct a new and robust linear regression equation for snow depth retrieval using microwave radiometers by including lower frequencies, and we produce the snow depth product over Antarctic sea ice from 2002 to 2020 from AMSR-E, AMSR-2, SSMIS based on this method. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements shows that the gradient ratio (GR) calculated using brightness temperatures in vertical polarized 37 and 19 GHz, i.e., GR(37/7), is the optimal one for deriving Antarctic snow depth with an root mean square deviation (RMSD) of 8.92 cm and a correlation coefficient of -0.64, the related equation coefficients are then derived. GR(37/19) is used to retrieve snow depth from SSMIS data to fill the observation gaps between AMSR-E and AMSR-2, and the estimated snow depth is corrected for the consistence with these from AMSR-E/2. An averaged uncertainty of 3.81 cm is found based on a Gaussian error propagation, which accounts for 12% of the estimated mean snow depth. The evaluation of proposed method with in-situ measurements from Australian Antarctic Data Centre shows that the proposed method outperforms the previous available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, comparing to -14.47 cm and 19.49 cm. Comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicates that the proposed method shows slight better performance than previous method (RMSDs of 16.85 cm and 17.61 cm, respectively); and comparable performances in growth and melting seasons suggests that the proposed method can still be used in the melting season. We generate a complete snow depth product over Antarctic sea ice from 2002 to 2020 in daily scale, and negative trends can be found in all sea sectors and seasons. This dataset can be further used in the reanalysis data evaluation, sea ice thickness estimation, climate model and other aspects.
0 2021-08-17
Population age structure resilience reflects the level of population age structure resilience in the countries along the Belt and Road. The World Bank's statistical database was used to prepare the data on the resilience of the population age structure of the countries along the Belt and Road. Based on the sensitivity and adaptability analysis, a comprehensive diagnosis was made based on the year-on-year change of each indicator, and the product on the resilience of population age structure was prepared.
0 2022-01-26
Population growth resilience reflects the level of resilience of population growth in the countries along the belt and road, and the higher the value, the stronger the resilience of population growth in the countries along the belt and road. The data on the resilience of population growth is prepared by referring to the World Bank's statistical database, using the year-on-year changes in the population of countries along the Belt and Road from 2000 to 2019, taking into account the year-on-year changes in each indicator, and through comprehensive diagnosis based on sensitivity and adaptability analysis. The resilience of population growth product.
0 2022-01-26
This data set includes the average concentrations of chemical species (Na+, K+, Mg2+, Ca2+ and TDS) in meltwater runoff draining 77 glaciers worldwide, annual glacial runoff from eight mountain ranges in Asia, and the mineral compositions of glacial deposits in some typical glacial catchments within Asia. This data set comes from the field monitoring of 19 glaciers in Asia by the data set provider, the previous published data worldwide, and the data shared by the authors of published papers. This data set can be used to evaluate the impact of climate warming on glacier erosion process and chemical weathering process, and the impact of glacier melt caused by climate warming on downstream ecosystems and element cycles.
0 2021-09-18
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