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Crevasse dataset over typical glaciers in Greenland ice sheet（2018-2020）
Crevasse dataset over typical glaciers in Greenland ice sheet（2018-2020）

We propose an algorithm for ice fissure identification and detection using u-net network, which can realize the automatic detection of ice fissures of Typical Glaciers in Greenland ice sheet. Based on the data of sentinel-1 IW from July and August every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then the representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking two typical glaciers in Greenland (Jakobshavn and Kangerdlussuaq) as examples, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.

Created Time:2022-07-29
Crevasse dataset over typical ice shelves in Antarctica（2015、2016、2020）
Crevasse dataset over typical ice shelves in Antarctica（2015、2016、2020）

We propose an algorithm for ice crack identification and detection using u-net network, which can realize the automatic detection of Antarctic ice cracks. Based on the data of sentinel-1 EW from January to February every year, in order to suppress the speckle noise of SAR image, the probabilistic patch based weights (ppb) algorithm is selected for filtering, and then representative samples are selected and input into the u-net network for model training, and the ice cracks are predicted according to the trained model. Taking five typical ice shelves（Amery、Fimbul、Nickerson、Shackleton、Thwaiters) in Antarctica as an example, the average accuracy of classification results can reach 94.5%, of which the local accuracy of fissure area can reach 78.6%, and the recall rate is 89.4%.

Created Time:2022-07-28
An elevation change dataset in Greenland ice sheet from 2003 to 2020 using satellite altimetry data
An elevation change dataset in Greenland ice sheet from 2003 to 2020 using satellite altimetry data

This dataset provided the long-term elevation change rates data of the GrIS in three different periods using the ICESat data (February 2003 to October 2009), the Cryosat-2 data (August 2010 to October 2018) and the ICESat-2 data (October 2018 to December 2020) respectively. The dataset is named by the data.The data revealed that the elevation change rates of the GrIS were -12.19±3.81 cm/yr, -19.70±3.61 cm/yr and -23.39±3.06 cm/yr in the three different periods. In general, the obtained results agree with the trends discovered by other studies that were also derived from satellite altimetry data. This dataset provides the basic data for research into the impact of climate change over the GrIS.

Created Time:2022-07-26
Data set of solar radiation at Dome C, Antarctic (2006-2016)
Data set of solar radiation at Dome C, Antarctic (2006-2016)

Global solar radiation and diffuse horizontal solar radiation at Dome C (Antarctica) are measured by radiation sensors (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground are obtained from the IPEV/PNRA Project “Routine Meteorological Observation at Station Concordia”, http://www.climantartide.it. This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Lanconelli, C.; Lupi, A.; Driemel, A.; Vitale, V.; Li, K.; Song, T. 2022. Long-Term Variations of Global Solar Radiation and Its Potential Effects at Dome C (Antarctica). Int. J. Environ. Res. Public Health, 19, 3084. https://doi.org/10.3390/ijerph19053084). The observed global solar radiation and meteorological parameters are available at https://doi.org/10.1594/PANGAEA.935421. The data set can be used to study solar radiation and its attenuation at Dome C, Antarctica.

Created Time:2022-07-26
Data set of solar radiation at Qomolangma, China (2007-2020)
Data set of solar radiation at Qomolangma, China (2007-2020)

Global solar radiation at Qomolangma station (The Tibetan Plateau) is measured by radiation sensor (pyranometers CM22, Kipp & Zonen Inc., The Netherlands), and water vapor pressure (hPa) at the ground is measured by HMP45C-GM (Vaisala Inc., Vantaa, Finland). This dataset includes hourly solar radiation and its absorbing and scattering losses caused by the absorbing and scattering atmospheric substances (MJ m-2, 200-3600 nm), and the albedos at the top of the atmosphere and the surface. The above solar radiations are calculated by using an empirical model of global solar radiation (Bai, J.; Zong, X.; Ma, Y.; Wang, B.; Zhao, C.; Yang, Y.; Guang, J.; Cong, Z.; Li, K.; Song, T. 2022. Long-Term Variations in Global Solar Radiation and Its Interaction with Atmospheric Substances at Qomolangma. Int. J. Environ. Res. Public Health, 19, 8906. https://doi.org/10.3390/ijerph19158906). The observed global solar radiation and meteorological variables are available at https://data.tpdc.ac.cn/zh-hans/data/b9ab35b2-81fb-4330-925f-4d9860ac47c3/. The data set can be used to study solar radiation and its attenuation at Qomolangma region.

Created Time:2022-07-26
Post-processing products for vegetation cover around the Arctic Circle and the Tibetan Plateau in 2013 and 2018
Post-processing products for vegetation cover around the Arctic Circle and the Tibetan Plateau in 2013 and 2018

Fractional Vegetation Cover (FVC) refers to the percentage of the vertical projected area of vegetation to the total area of the study area. It is an important indicator to measure the effectiveness of ecological protection and ecological restoration. It is widely used in the fields of climate, ecology, soil erosion and so on. FVC is not only an ideal parameter to reflect the productivity of vegetation, but also can play a good role in evaluating topographic differences, climate change and regional ecological environment quality. This research work is mainly to post process two sets of glass FVC data, and give a more reliable vegetation coverage of the circumpolar Arctic Circle (north of 66 ° n) and the Qinghai Tibet Plateau (north of 26 ° n to 39.85 °, east longitude 73.45 ° to 104.65 °) in 2013 and 2018 through data fusion, elimination of outliers and clipping.

Created Time:2022-07-16
Antarctic peninsula typical year typical zone vegetation cover post processing products (2008, 2017, 2018)
Antarctic peninsula typical year typical zone vegetation cover post processing products (2008, 2017, 2018)

The vegetation data of the Antarctic Peninsula were obtained from the Antarctic Pioneer vegetation cover classification data of the spatio-temporal three-level environmental big data platform by applying pure image element PPI to extract the end element spectra of mosses, lichens, rocks, sea and snow and applying the linear Mixture Model (LMM) to calculate them. The characteristic vegetation cover of the Fildes Peninsula was obtained based on its correlation with the linear relationship of abundance. The data format is geotiff format. The data content is the vegetation cover of the typical zone of the Antarctic Peninsula in a typical year. In this research work, tif raster format products were generated by post-processing the typical annual vegetation cover of the typical area of the Antarctic Peninsula, and the value of the main body of the raster is the vegetation cover. The vegetation cover of the Antarctic Peninsula typical area obtained in this study is a mosaic of Antarctic pioneer plant abundance data products, including the plant abundance data products in and around the Antarctic Peninsula. The typical area of the Antarctic Peninsula including Adley, north and south were mosaicked by ArcGIS to obtain six vegetation cover maps identified by spectral angle matching method (SAM) and spectral information scatter method (SID) including 2008, 2017 and 2018.

Created Time:2022-07-16
A database of radiogenic Sr-Nd isotopes at the "three poles”
A database of radiogenic Sr-Nd isotopes at the "three poles”

The radiogenic isotope composition of strontium (Sr) and neodymium (Nd) on surface of the Earth are a powerful tool for tracing dust sources and sinks on surface of the Earth. To differentiate the spatial variability of aeolian dust sources in key cryospheric regions at the three poles (including the ‘Third Pole’ covering the high mountain area in Asia, the Arctic and Antarctica). A dataset of the Sr-Nd isotopic compositions from the terrestrial extremely cold or arid environments in this study was compiled as the similar method by Blanchet (2019). The database identified on snow, ice, sand, soil (loess) and sediment from the modern dust samples and paleoclimatic records of the three poles based on 43 different references with 967 data points in total. There are 274 data points from the third pole, 302 data points from the Arctic, and 391 data points from Antarctica. The sampling and measurement methods, and quality of these data are recognized and introduced. In each pole, geographical coordinates and other information are provided. The main scientific purpose of this dataset is to provide our own measurement and collect documentation of the Sr-Nd dataset, which will be useful for determining the sources and transport pathways of dust at the three poles and to investigate whether there are multiple dust sources for each of the poles. These datasets provide detail exhaustive documentation of the isotopic signature from the three poles at specific time intervals, which would be useful for understanding the dust source or sink of the three poles.

Created Time:2022-03-08

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