Monitoring Arctic thin ice: a comparison between CryoSat-2 SAR altimetry data and MODIS thermal-infrared imagery
-
Published:2023-02-17
Issue:2
Volume:17
Page:809-825
-
ISSN:1994-0424
-
Container-title:The Cryosphere
-
language:en
-
Short-container-title:The Cryosphere
Author:
Müller Felix L.ORCID, Paul StephanORCID, Hendricks StefanORCID, Dettmering DeniseORCID
Abstract
Abstract. Areas of thin sea ice in the polar regions not only are experiencing the highest rate of sea-ice production but also are, therefore, important hot spots for ocean ventilation as well as heat and moisture exchange between the ocean and the atmosphere. Through co-location of (1) an unsupervised waveform classification (UWC) approach applied to CryoSat-2 radar waveforms with (2) Moderate Resolution Imaging Spectroradiometer-derived (MODIS) thin-ice-thickness estimates and (3) Sentinel-1A/B synthetic-aperture radar (SAR) reference data, thin-ice-based waveform shapes are identified, referenced, and discussed with regard to a manifold of waveform shape parameters. Here, strong linear dependencies are found between binned thin-ice thickness up to 25 cm from MODIS and the CryoSat-2 waveform shape parameters that show the possibility of either developing simple correction terms for altimeter ranges over thin ice or directing adjustments to current retracker algorithms specifically for very thin sea ice. This highlights the potential of CryoSat-2-based SAR altimetry to reliably discriminate between occurrences of thick sea ice, open-water leads, and thin ice within recently refrozen leads or areas of thin sea ice. Furthermore, a comparison to the ESA Climate Change Initiative's (CCI) CryoSat-2 surface type classification with classes sea ice, lead, and unknown reveals that the newly found thin-ice-related waveforms are divided up almost equally between unknown (46.3 %) and lead type (53.4 %) classifications. Overall, the UWC results in far fewer unknown classifications (1.4 % to 38.7 %). Thus, UWC provides more usable information for sea-ice freeboard and thickness retrieval and at the same time reduces range biases from thin-ice waveforms processed as regular sea ice in the CCI classification.
Publisher
Copernicus GmbH
Subject
Earth-Surface Processes,Water Science and Technology
Reference70 articles.
1. Adams, S., Willmes, S., Schröder, D., Heinemann, G., Bauer, M., and Krumpen, T.: Improvement and sensitivity analysis of thermal thin-ice thickness retrievals, IEEE T. Geosci. Remote, 51, 3306–3318, 2012. a 2. Aldenhoff, W., Heuzé, C., and Eriksson, L. E. B.: Sensitivity of Radar Altimeter Waveform to Changes in Sea Ice Type at Resolution of Synthetic Aperture Radar, Remote Sens.-Basel, 11, 2602, https://doi.org/10.3390/rs11222602, 2019. a 3. Alexandrov, V., Sandven, S., Wahlin, J., and Johannessen, O. M.: The relation between sea ice thickness and freeboard in the Arctic, The Cryosphere, 4, 373–380, https://doi.org/10.5194/tc-4-373-2010, 2010. a 4. Bouzinac, C.: CryoSat-2 Product Handbook Baseline D 1.1 C2-LI-ACS-ESL-5319, European Space Agency, https://earth.esa.int/eogateway/documents/20142/37627/CryoSat-Baseline-D-Product-Handbook.pdf (last access: October 2021), 2019. a 5. Celebi, M.: Partitional Clustering Algorithms, EBL-Schweitzer, Springer International Publishing, ISBN 978-3-319-09259-1, https://doi.org/10.1007/978-3-319-09259-1, 2014. a
Cited by
2 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|