This data set is the global high accuracy global elevation control point dataset, including the geographic positioning, elevation, acquisition time and other information of each elevation control point. The accuracy of laser footprint elevation extracted from satellite laser altimetry data is affected by many factors, such as atmosphere, payload instrument noise, terrain fluctuation in laser footprint and so on. The dataset extracted from the altimetry observation data of ICESat satellite from 2003 to 2009 through the screening criteria constructed by the evaluation label and ranging error model, in order to provide global high accuracy elevation control points for topographic map or other scientific fields relying on good elevation information. It has been verified that the elevation accuracy of flat (slope<2°), hilly (2°≤slope<6°), and mountain (6°≤slope<25°) areas meet the accuracy requirements of 0.5m, 1.5m, and 3m respectively.
XIE Huan, LI Binbin, TONG Xionghua, TANG Hong, LIU Shijie, JIN Yanmin, WANG Chao, YE Zhen, CHEN Peng, XU Xiong, LIU Sicong, FENG Yongjiu
The surface elevation of the ice sheet is very sensitive to climate change, so the elevation change of the ice sheet is considered as an important variable to evaluate climate change. The time series of long-term ice sheet surface elevation change has become a fundamental data for understanding climate change. The longest time series of ice sheet surface elevation can be established by combining the observation records of radar satellite altimetry missions. However, the previous methods for correcting the intermission bias still have error residue when cross-calibrating different missions. Therefore，we modify the commonly used plane-fitting least-squares regression model by restricting the correction of intermission bias and the ascending–descending bias at the same time to ensure the self-consistency and coherence of surface elevation time series across different missions. Based on this method, we use Envisat and CryoSat-2 data to construct the time series of Antarctic ice sheet elevation change from 2002 to 2019. The time series is the monthly grid data, and the spatial grid resolution is 5 km×5 km. Using airborne and satellite laser altimetry data to evaluate the results, it is found that compared with the traditional method, this method can improve the accuracy of intermission bias correction by 40%. Using the merged elevation time series, combining with firn densification-modeled volume changes due to surface processes, we find that ice dynamic processes make the ice sheet along the Amundsen Sea sector the largest volume loss of the Antarctic ice sheet. The surface processes dominate the volume changes in Totten Glacier sector, Dronning Maud Land, Princess Elizabeth Land, and the Bellingshausen Sea sector. Overall, accelerated volume loss in the West Antarctic continues to outpace the gains observed in the East Antarctic. The total volume change during 2002–2019 for the AIS was −68.7 ± 8.1 km3/y, with an acceleration of −5.5 ± 0.9 km3/y2.
ZHANG Baojun, WANG Zemin, YANG Quanming, LIU Jingbin, AN Jiachun, LI Fei, GENG Hong
This dataset contains the ground surface water (including liquid water, glacier and perennial snow) distribution in Qilian Mountain Area in 2020. The dataset was produced based on classical Normalized Difference Water Index (NDWI) extraction criterion and manual editing. Landsat images collected in 2020 were used as basic data for water index extraction. Sentinel-2 images and Google images were employed as reference data for adjusting the extraction threshold. The dataset was stored in SHP format and attached with the attributions of coordinates and water area. Consisting of 1 season, the dataset has a temporal resolution of 1 year and a spatial resolution of 30 meters. The accuracy is about 1 pixel (±30 meter). The dataset directly reflects the distribution of water bodies within the Qilian Mountain in 2020, and can be used for quantitative estimation of water resource.
Li Jia, Li Jia, LI Jia, LI Jia, LI Jianjiang, LI Xin, LIU Shaomin
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