As an important parameter of snowpack, the snow depth can adjust changes in sea ice and plays a vital role in the climate system. This dataset provides the daily snow depth on Arctic sea ice during cold-season from 2012 to 2020 (October-April). The snow depth retrieval algorithm combines linear regression and deep learning algorithm (LSTM). Firstly, according to AMSR2 brightness temperature data and the buoy snow depth data, the optimal gradient ratio for estimating the snow depth on first-year ice (FYI) is determined, and the snow depth retrieval model on FYI is established. The input of LSTM is different gradient rates, polarization ratio (PR) of the brightness temperature at 37 GHz, and the output is snow depth on sea ice. Then, the snow depth retrieval model on multi-year ice (MYI) is determined. Finally, the snow depth on Arctic sea ice with a 25-km spatial resolution is obtained. This dataset performs well in reflecting snow depths over thick and thin sea ice, which agrees well with the NASA Operation IceBridge (OIB) data, with the root mean square error of 7.35 cm. Hence, this dataset provides accurate snow depth estimates for retrieving sea ice thickness and is conducive to analyses of Arctic mass balance and energy balance.

File naming and required software

File naming method: the snow depth on sea ice data is stored in TIF format, and the name of the file is "Nyyyymmdd_ LSTM_ hs.tif ", where N stands for the Northern Hemisphere, yyyy stands for year, mm stands for month,dd stands for day, LSTM stands for deep learning method, and hs stands for snow depth. For example, N20121001_ LSTM_ hs.tif represents the spatial distribution of snow depth on Arctic sea ice on October 1, 2012.
Data reading mode: TIF data can be opened directly by ArcGIS.

Data Citations Data citation guideline What's data citation?

Cite as:

Li, H., Ke, C. (2021). 25 km resolution snow depth on Arctic sea ice dataset (2012-2020). A Big Earth Data Platform for Three Poles, DOI: 10.11888/Snow.tpdc.271639. CSTR: 18406.11.Snow.tpdc.271639. (Download the reference： RIS | Bibtex )

Using this data, the data citation is required to be referenced and the related literatures are suggested to be cited.

Copyright & License

To respect the intellectual property rights, protect the rights of data authors, expand services of the data center, and evaluate the application potential of data, data users should clearly indicate the source of the data and the author of the data in the research results generated by using the data (including published papers, articles, data products, and unpublished research reports, data products and other results). For re-posting (second or multiple releases) data, the author must also indicate the source of the original data.

License： This work is licensed under an Attribution 4.0 International (CC BY 4.0)