the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Mar 2025
High-resolution maps of Arctic surface skin temperature and type retrieved from airborne thermal infrared imagery collected during the HALO-(𝒜𝒞)³ campaign
Abstract. Two retrieval methods for the determination of Arctic surface skin temperature and surface type based on radiance measurements from the thermal infrared (TIR) imager VELOX (Video airbornE Longwave Observations within siX channels) are introduced. VELOX captures TIR radiances in terms of brightness temperatures in the atmospheric window for wavelengths from 7.7 μm to 12 μm in six spectral channels. It was deployed on the High Altitude and LOng Range research aircraft (HALO) during the HALO–(𝒜𝒞)3 airborne field campaign conducted in the framework of the Arctic Amplification: Climate Relevant Atmospheric and SurfaCe Processes and Feedback Mechanisms (𝒜𝒞)3 research program. The measurements were taken over the Fram Strait and the central Arctic in March and April 2022. To derive the surface skin temperature, radiative transfer simulations assuming cloud-free atmospheric conditions were performed, quantifying the influence of water vapour on the measured brightness temperature. Since this influence was negligible, it was possible to apply a single-channel retrieval of the surface skin temperature. The derived surface skin temperatures were compared with data from the MODerate-resolution Imaging Spectroradiometer (MODIS). Furthermore, a pixel-by-pixel surface classification into types of open water, sea-ice water mixture, thin sea ice, and snow-covered sea ice was developed using a random forest algorithm. When the resulting sea-ice concentrations are compared with satellite data, a mean absolute error (MAE) of 5 % is obtained. In addition, the classified pixels where aggregated into segments of the same surface type, providing different segment size distributions for all surface types. When grouped by the distance to the sea ice edge, the segment size distribution shows a shift, favoring fewer but larger floes in the direction of the pack ice.
Competing interests: Some authors are members of the editorial board of journal AMT.
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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- BibTeX
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Journal article(s) based on this preprint
Interactive discussion
Status: closed
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CC1: 'Comment on amt-2024-3967', Meng Qu, 12 May 2025
- AC1: 'Reply on CC1', Joshua Müller, 30 Jun 2025
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RC1: 'Comment on amt-2024-3967', Anonymous Referee #2, 19 May 2025
This study is well structured, well written, and makes a great insight into retrieving the high resolution Arctic surface skin temperature and types via infrared imagery during the HALO AC3 campaign. The results present the small scale variability to complement the MODIS surface temperature products, bridging the gap between small scale observations and satellite produces, and assessingof surface heterogeneity using power-law statistics. The paper is clearly written, well-structured, and the figures are informative. The authors provide a technically sound and well supported approach to examine the sea ice processes with changing sea ice conditions and dynamics.
I agree with the previous comment about the technical side. On top of which, I would recommend the authors to consider to address 2 points: (1) the potentials and possible challenges if one would extend this work to a wider time frame with stronger spatial variability which can have a bigger potential to impact the surface energy budget?
(2) the classification scheme is a great start and provides an inspiring direction for the arctic community, but i wonder if it is realistic to upscale this method to provide a pan-arctic insight?
Citation: https://doi.org/10.5194/amt-2024-3967-RC1 - AC2: 'Reply on RC1', Joshua Müller, 01 Jul 2025
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EC1: 'Comment on amt-2024-3967', Andreas Richter, 22 May 2025
The discussion of this manuscript has been closed after receiving two reviewer comments. One of them, provided by Meng Qu, was inadvertently posted as a community comment but is a normal reviewer comment.
Andreas Richter
Citation: https://doi.org/10.5194/amt-2024-3967-EC1
Interactive discussion
Status: closed
-
CC1: 'Comment on amt-2024-3967', Meng Qu, 12 May 2025
The manuscript present analysis Arctic surface skin temperature and surface type, obtained from airborne thermal measurements during HALO-(AC)3. In general, the manuscript provide updated assessment of sea ice surface in the marginal ice zone (MIZ) using high-resolution airborne thermal images, which will promote research on sea ice dynamic and thermodynamic process in the MIZ.
Although, i have a few minor question for the author
1. It seems you used Level 3 MODIS daily IST for radiative transfer simulations and evaluation of airborne IST algorithm, and you mentioned the temperal mismatch between airborne and satellite data. Assuming there were 1 or 2 HALO flight in one day during the compaign, have you tried Level2 MODIS swath data, i.e. MOD29/MYD29 instead of Level 3 gridded product? the swath data suffer more from cloud contamination of course, but it will help reduce the time different to less than an hour, and possibly, you would find better agreement between airborne and satellite data.2. if i get it right, the class Sea Ice-Water Mixture (IWM) is not included in the training set, but only classified Open-water pixels with surface temperature below -2.5℃. it seems the IWM only account for a minor portion of area (Fig.7), and its surface temperature is very close to thin ice(TI). the question is, how is the threshold determined? Also, since you used Sentinel-2 MSI for labeling of the images in training set, It is recommended to check the distribution of corresponding reflectance (from MSI) for different surface class in the training set, IWM samples might appear in the OW samples as anomaly.
3.about the size distribution of surface features in the MIZ, it seems the thin ice area is connected into a very large segment in Fig.7. i'm not sure if is it a common phenomenon in your data set, but it seems the TI is the dominant class in Fig.11. It is recommended to break down the large TI segment into pieces by any means, otherwise the FSD/SSD result for TI could be misleading.
Citation: https://doi.org/10.5194/amt-2024-3967-CC1 - AC1: 'Reply on CC1', Joshua Müller, 30 Jun 2025
-
RC1: 'Comment on amt-2024-3967', Anonymous Referee #2, 19 May 2025
This study is well structured, well written, and makes a great insight into retrieving the high resolution Arctic surface skin temperature and types via infrared imagery during the HALO AC3 campaign. The results present the small scale variability to complement the MODIS surface temperature products, bridging the gap between small scale observations and satellite produces, and assessingof surface heterogeneity using power-law statistics. The paper is clearly written, well-structured, and the figures are informative. The authors provide a technically sound and well supported approach to examine the sea ice processes with changing sea ice conditions and dynamics.
I agree with the previous comment about the technical side. On top of which, I would recommend the authors to consider to address 2 points: (1) the potentials and possible challenges if one would extend this work to a wider time frame with stronger spatial variability which can have a bigger potential to impact the surface energy budget?
(2) the classification scheme is a great start and provides an inspiring direction for the arctic community, but i wonder if it is realistic to upscale this method to provide a pan-arctic insight?
Citation: https://doi.org/10.5194/amt-2024-3967-RC1 - AC2: 'Reply on RC1', Joshua Müller, 01 Jul 2025
-
EC1: 'Comment on amt-2024-3967', Andreas Richter, 22 May 2025
The discussion of this manuscript has been closed after receiving two reviewer comments. One of them, provided by Meng Qu, was inadvertently posted as a community comment but is a normal reviewer comment.
Andreas Richter
Citation: https://doi.org/10.5194/amt-2024-3967-EC1
Peer review completion
Journal article(s) based on this preprint
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Joshua Jeremias Müller
Michael Schäfer
Sophie Rosenburg
André Ehrlich
Manfred Wendisch
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(3274 KB) - Metadata XML
The manuscript present analysis Arctic surface skin temperature and surface type, obtained from airborne thermal measurements during HALO-(AC)3. In general, the manuscript provide updated assessment of sea ice surface in the marginal ice zone (MIZ) using high-resolution airborne thermal images, which will promote research on sea ice dynamic and thermodynamic process in the MIZ.
Although, i have a few minor question for the author
1. It seems you used Level 3 MODIS daily IST for radiative transfer simulations and evaluation of airborne IST algorithm, and you mentioned the temperal mismatch between airborne and satellite data. Assuming there were 1 or 2 HALO flight in one day during the compaign, have you tried Level2 MODIS swath data, i.e. MOD29/MYD29 instead of Level 3 gridded product? the swath data suffer more from cloud contamination of course, but it will help reduce the time different to less than an hour, and possibly, you would find better agreement between airborne and satellite data.
2. if i get it right, the class Sea Ice-Water Mixture (IWM) is not included in the training set, but only classified Open-water pixels with surface temperature below -2.5℃. it seems the IWM only account for a minor portion of area (Fig.7), and its surface temperature is very close to thin ice(TI). the question is, how is the threshold determined? Also, since you used Sentinel-2 MSI for labeling of the images in training set, It is recommended to check the distribution of corresponding reflectance (from MSI) for different surface class in the training set, IWM samples might appear in the OW samples as anomaly.
3.about the size distribution of surface features in the MIZ, it seems the thin ice area is connected into a very large segment in Fig.7. i'm not sure if is it a common phenomenon in your data set, but it seems the TI is the dominant class in Fig.11. It is recommended to break down the large TI segment into pieces by any means, otherwise the FSD/SSD result for TI could be misleading.