DynIceData:a gridded ice-water classification dataset at short-time intervals based on observations from multiple satellites over the marginal ice zone  

作　　者：Lin Huang Yubao Qiu Yang Li Shuwen Yu Wanyang Zhong Changyong Dou 

机构地区：[1]Key Laboratory of Digital Earth Science,Aerospace Information Research Institute,Chinese Academy of Sciences,Beijing,China [2]International Research Center of Big Data for Sustainable Development Goals,Beijing,China [3]University of Chinese Academy of Sciences,Beijing,China [4]School of Resources and Geosciences,China University of Mining and Technology,Xuzhou,China

出　　处：《Big Earth Data》2024年第2期249-273,共25页地球大数据（英文）

基　　金：funded by the National Key Research and Development Program of China(No.2019YFE0105700 and No.2017YFE0111700);the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA19070201 and No.XDA19070102);the Innovative Research Program of the International Research Center of Big Data for Sustainable Development Goals(No.CBAS2022IRP08);the International Partnership Program of the Chinese Academy of Sciences“Remote Sensing and Modeling of the Snow and Ice Physical Process”(RSMSIP No.313GJHZ2022054MI).

摘　　要：High-resolution observations of short-term changes in sea ice are critical to understanding ice dynamics and also provide important information used in advice to shipping,especially in the Arctic.Although individual satellite sensors provide periodic sea ice obser-vations with spatial resolutions of tens of meters,information regarding changes that occur over short time intervals of minutes or hours is limited.In this study,a gridded ice-water classification dataset with a high temporal resolution was developed based on observations acquired by multiple satellite sensors in the Marginal Ice Zone(MIZ).This dataset-DynIceData-which combines Sentinel-1 Synthetic Aperture Radar(SAR)data with Gaofen-3(GF-3)SAR and SDGSAT-1 thermal infrared imagery was used to obtain observations of the MIZ with a range of temporal resolutions ran-ging from minutes to tens of hours.The areas of the Arctic covered include the Kara Sea,Beaufort Sea,and Greenland Sea during the period from August 2021 to August 2022.Object-oriented segmen-tation and thresholding were used to obtain the ice-water classifi-cation map from Sentinel-1 and GF-3 SAR image pairs and Sentinel-1 SAR and SDGSAT-1 thermal image pairs.The time interval between the images in each pair ranged from 1 minute to 68 hours.Ten-kilometer grid sample granules with a spatial resolution of 25 m for the GF-3 SAR data and 30 m for the SDGSAT-1 thermal data were used.The classification was verified as having an overall accuracy of at least 95.58%.The DynIceData dataset consists of 7338 samples,which could be used as reference data for further research on rapid changes in sea ice patterns at different short time scales and provide support for research on thermodynamic and dynamic models of sea ice in combination with other environmen-tal data,thus potentially improving the accuracy of sea ice forecast-ing using Artificial Intelligence.The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.

关 键 词：Sea ice dynamics ice-water classification GF-3 SDGSAT-1 marginal ice zone 

分 类 号：TN92[电子电信—通信与信息系统]