The Cryosphere
The Cryosphere
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Articles | Volume 19, issue 1
Articles | Volume 19, issue 1
https://doi.org/10.5194/tc-19-37-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-19-37-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 19, issue 1
Research article
 | 
08 Jan 2025
Research article |  | 08 Jan 2025

Machine learning of Antarctic firn density by combining radiometer and scatterometer remote-sensing data

Weiran Li, Sanne B. M. Veldhuijsen, and Stef Lhermitte

Data sets

DMSP SSM/I-SSMIS Daily Polar Gridded Brightness Temperatures W. N. Meier et al. https://doi.org/10.5067/MXJL42WSXTS1

Standard BYU ASCAT Land/Ice Image Products Brigham Young University (BYU) Microwave Earth Remote Sensing (MERS) laboratory https://www.scp.byu.edu/data/Ascat/SIR/Ascat_sir.html

Surface Mass Balance and Snow Depth on Sea Ice Working Group (SUMup) snow density subdataset, Greenland and Antartica, 1950-2018 Lora Koenig and Lynn Montgomery https://doi.org/10.18739/A2JH3D23R

ERA5-Land hourly data from 1950 to present J. Muñoz-Sabater https://doi.org/10.24381/cds.e2161bac

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Short summary
This study used a machine learning approach to estimate the densities over the Antarctic Ice Sheet, particularly in the areas where the snow is usually dry. The motivation is to establish a link between satellite parameters to snow densities, as measurements are difficult for people to take on site. It provides valuable insights into the complexities of the relationship between satellite parameters and firn density and provides potential for further studies.
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