2012 Arctic Summer Sea Ice Drift Dataset

• Main Authors: Yan Fang, Ruirui Wang, Xue Wang, Zhuoqi Chen, Fengming Hui
• Format: Dataset
• Language: English
• Published: Science Data Bank 2022
• Subjects: Arctic; Summer; Sea ice drift; Feature Tracking; Environmental science and resources science and technology; Arctic Ocean; Heg; Sea ice
• Online Access: https://doi.org/10.11922/sciencedb.01591

The 2012 Arctic Summer Sea Ice Drift Dataset produced by School of Geospatial Engineering and Science, Sun Yat-Sen University contains the daily sea ice motion information in the Arctic Ocean from 2012 Apr to Sep, stored in CSV format. By using a total of 8118 MOD09&MYD09 (MODIS Atmosphereically Corrected Surface Reflectance Data) images (500m spatial resolution, Band 1) from April to September 2012, we selected images with a time interval of 18 ~ 30 hours and a spatial overlap of more than 60% to form 23760 pairs of images. Then the A-KAZE algorithm is used to extract feature points in image pairs and the sea ice motion information is obtained through the matching between the feature points. This dataset is generated with the assistance of the NASA HEG tool (HDF-EOS To GeoTIFF Conversion Tool) and Python OpenCV. The geocoding and preprocessing of images are carried out in HEG, and the image matching is realized under OpenCV.This dataset records the daily sea ice motion vectors of the whole Arctic above 65°N in the summer of 2012 and provides a total of six CSV files, including the sea ice drift information ofeach month from April to September respectively. The first and second columns in the CSV file are the time information of the vector, including the month and date obtained by the vector. The third to sixth columns respectively record the longitude and latitude coordinates of the starting point and ending point of the sea ice motion vector, and the seventh column records the drift speed of the sea ice, in kilometer per day. The sea ice drift information recorded in this dataset is not evenly distributed in space, and there are gaps of coverage in some areas, which is mainly due to the characteristics of feature tracking algorithm and the interference of cloud in MODIS images. In general, this dataset is helpful to study summer sea ice drift in the Arctic and can be applied to the study of Arctic regions.