Articles | Volume 18, issue 18
https://doi.org/10.5194/amt-18-4695-2025
https://doi.org/10.5194/amt-18-4695-2025
Research article
 | 
24 Sep 2025
Research article |  | 24 Sep 2025

High-resolution maps of Arctic surface skin temperature and type retrieved from airborne thermal infrared imagery collected during the HALO–(𝒜 𝒞)3 campaign

Joshua J. Müller, Michael Schäfer, Sophie Rosenburg, André Ehrlich, and Manfred Wendisch

Related authors

Cloud liquid water path detectability and retrieval accuracy from airborne passive microwave observations over Arctic sea ice
Nils Risse, Mario Mech, Catherine Prigent, Joshua Jeremias Müller, and Susanne Crewell
EGUsphere, https://doi.org/10.5194/egusphere-2025-3311,https://doi.org/10.5194/egusphere-2025-3311, 2025
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary

Cited articles

Anhaus, P., Katlein, C., Nicolaus, M., Hoppmann, M., and Haas, C.: From Bright Windows to Dark Spots: Snow Cover Controls Melt Pond Optical Properties During Refreezing, Geophys. Res. Lett., 48, https://doi.org/10.1029/2021GL095369, 2021. a, b
Bateson, A. W., Feltham, D. L., Schröder, D., Wang, Y., Hwang, B., Ridley, J. K., and Aksenov, Y.: Sea ice floe size: its impact on pan-Arctic and local ice mass and required model complexity, The Cryosphere, 16, 2565–2593, https://doi.org/10.5194/tc-16-2565-2022, 2022. a
Belgiu, M. and Drăguţ, L.: Random forest in remote sensing: A review of applications and future directions, ISPRS J. Photogramm., 114, 24–31, https://doi.org/10.1016/j.isprsjprs.2016.01.011, 2016. a
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001. a
Breiman, L.: Classification and Regression Trees, Routledge, New York, ISBN 978-1-315-13947-0, https://doi.org/10.1201/9781315139470, 2017. a
Download
Short summary
We retrieved high-resolution maps of Arctic surface temperature and type using an airborne thermal infrared imager during an Arctic aircraft campaign. Our study highlights small-scale surface variability, complementing satellite observations. Surface temperature was retrieved via radiative transfer simulations, while surface type was classified using machine learning. Additionally, we analysed segment sizes of each surface type, presenting results based on their distance from the sea-ice edge.
Share