Snow over sea ice controls the energy budgets, affects the sea ice growth/melting, and thus has essential climatic effects. Snow depth, one of the fundamental properties of snow cover, is essential for understanding of the rapid change in Antarctic climate and for sea ice thickness estimation. Passive microwave radiometer can be used for basin-scale snow depth estimation in daily scale, however, previous published methods applied for Antarctic snow depth shows clear underestimation, which limits their further application. Here, we construct a new and robust linear regression equation for snow depth retrieval using microwave radiometers by including lower frequencies, and we produce the snow depth product over Antarctic sea ice from 2002 to 2020 from AMSR-E, AMSR-2, SSMIS based on this method. A regression analysis using 7 years of Operation IceBridge (OIB) airborne snow depth measurements shows that the gradient ratio (GR) calculated using brightness temperatures in vertical polarized 37 and 19 GHz, i.e., GR(37/7), is the optimal one for deriving Antarctic snow depth with an root mean square deviation (RMSD) of 8.92 cm and a correlation coefficient of -0.64, the related equation coefficients are then derived. GR(37/19) is used to retrieve snow depth from SSMIS data to fill the observation gaps between AMSR-E and AMSR-2, and the estimated snow depth is corrected for the consistence with these from AMSR-E/2. An averaged uncertainty of 3.81 cm is found based on a Gaussian error propagation, which accounts for 12% of the estimated mean snow depth. The evaluation of proposed method with in-situ measurements from Australian Antarctic Data Centre shows that the proposed method outperforms the previous available method, with a mean difference of 5.64 cm and an RMSD of 13.79 cm, comparing to -14.47 cm and 19.49 cm. Comparison to shipborne observations from Antarctic Sea Ice Processes and Climate indicates that the proposed method shows slight better performance than previous method (RMSDs of 16.85 cm and 17.61 cm, respectively); and comparable performances in growth and melting seasons suggests that the proposed method can still be used in the melting season. We generate a complete snow depth product over Antarctic sea ice from 2002 to 2020 in daily scale, and negative trends can be found in all sea sectors and seasons. This dataset can be further used in the reanalysis data evaluation, sea ice thickness estimation, climate model and other aspects.

File naming and required software

File name: snow depth / uncertainty data is stored in GeoTIFF format, and the name of the file is "yyyydddd"_ SD. TIF "or" yyyydddd "_ SDunc. TIF ", where yyyy stands for year and dddd stands for date, SD stands for snow depth, SDunc stands for snow depth uncertainty, all values are in meter, the spatial resolution is 25 km, such as 20020601_ SD.tif represents this document to describe the snow depth over Antarctic sea ice on June 1, 2002.
Data reading method: all GeoTIFF files in the data set can be directly opened with ArcGIS, envi, MATLAB, Python and other software. The main content is numerical representation of snow depth / uncertainty of snow depth. If you want to display them with diagrams, you can open them with ArcGIS and other software.

Data Citations Data citation guideline What's data citation?

Cite as:

Shen, X., Ke, C. (2021). Snow depth product over Antarctic sea ice from 2002 to 2020. A Big Earth Data Platform for Three Poles, DOI: 10.11888/Snow.tpdc.271653. CSTR: 18406.11.Snow.tpdc.271653. (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

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