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

Abstract

ABSTRACT 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 observations 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 ranging 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 segmentation and thresholding were used to obtain the ice–water classification 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 environmental data, thus potentially improving the accuracy of sea ice forecasting using Artificial Intelligence. The dataset can be accessed at https://doi.org/10.57760/sciencedb.j00001.00784.