Articles | Volume 17, issue 7
https://doi.org/10.5194/tc-17-2829-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-17-2829-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
14 Jul 2023
Research article |
| 14 Jul 2023
| 14 Jul 2023
Spaceborne thermal infrared observations of Arctic sea ice leads at 30 m resolution
Yujia Qiu
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
University of Chinese Academy of Sciences, Beijing 100049, China
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Huadong Guo
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
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Cited
24 citations as recorded by crossref.
- Assessing lead fraction derived from passive microwave images and improving estimates at pixel-wise level X. Zhao et al. https://doi.org/10.1016/j.rse.2024.114517
- Ice-channel detection using deep networks in visible and infrared from shipborne camera A. Gorad et al. https://doi.org/10.1016/j.coldregions.2026.105072
- Land surface temperature retrieval from SDGSAT-1 thermal infrared spectrometer images: Algorithm and validation Y. Teng et al. https://doi.org/10.1016/j.rse.2024.114412
- Stripe noise removal for the thermal infrared spectrometer of the SDGSAT-1 M. Dai et al. https://doi.org/10.1016/j.jag.2024.103847
- SDGSAT-1: Capabilities for Monitoring and Evaluating SDG Indicators H. GUO et al. https://doi.org/10.11728/cjss2024.04.2024-yg15
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. https://doi.org/10.3390/rs16203764
- A new sea ice concentration retrieval algorithm from thermal infrared imagery Y. Ye et al. https://doi.org/10.1080/17538947.2024.2353116
- An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery Y. Liu et al. https://doi.org/10.62762/CJIF.2025.695812
- Toward an emissivity-adaptive method for arctic ice/snow/water surface temperature retrieval from FengYun-3D MERSI-II data Z. Zhou et al. https://doi.org/10.1016/j.rse.2026.115487
- Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager D. Wang et al. https://doi.org/10.3390/app14062450
- Overview of the studies on the interactions between atmosphere, sea ice, and ocean in the Arctic Ocean and its climatic effects: contributions from Chinese scientists R. Lei et al. https://doi.org/10.1007/s13131-025-2466-4
- SDGSAT-1: A Professional Scientific Satellite for Monitoring SDG Indicators H. Guo et al. https://doi.org/10.1109/JPROC.2025.3649854
- Lead fractions from SAR-derived sea ice divergence during MOSAiC L. von Albedyll et al. https://doi.org/10.5194/tc-18-1259-2024
- Synergic sensing of light and heat emitted by offshore oil and gas platforms in the South China Sea Y. Qiu et al. https://doi.org/10.1080/17538947.2024.2441932
- Improvement in panchromatic-guided denoising algorithm for research on Beijing SDGSAT-1 luminous remote sensing image denoising algorithm L. Zhang et al. https://doi.org/10.1080/17538947.2025.2494075
- Detecting Sea Ice Leads and Floes in the Northwest Passage Using CryoSat-2 A. Swiggs et al. https://doi.org/10.1109/JSTARS.2024.3503286
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Detection of Arctic Leads Based on FY-3D/MERSI-II T. Chao et al. https://doi.org/10.1007/s11802-026-6291-y
- Methodology and potential applications of ice/snow surface temperature over polar regions using SDGSAT-1 satellite C. Shi et al. https://doi.org/10.1016/j.rse.2025.114868
- Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses T. Uhlíková et al. https://doi.org/10.5194/tc-18-957-2024
- Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics M. Qu et al. https://doi.org/10.1016/j.rse.2024.114193
- The SDGSAT-1 mission and its role in monitoring SDG indicators H. Guo et al. https://doi.org/10.1016/j.rse.2025.114885
- MgB2 thermal kinetic inductance detector T. Jabbari et al. https://doi.org/10.1063/5.0330476
24 citations as recorded by crossref.
- Assessing lead fraction derived from passive microwave images and improving estimates at pixel-wise level X. Zhao et al. https://doi.org/10.1016/j.rse.2024.114517
- Ice-channel detection using deep networks in visible and infrared from shipborne camera A. Gorad et al. https://doi.org/10.1016/j.coldregions.2026.105072
- Land surface temperature retrieval from SDGSAT-1 thermal infrared spectrometer images: Algorithm and validation Y. Teng et al. https://doi.org/10.1016/j.rse.2024.114412
- Stripe noise removal for the thermal infrared spectrometer of the SDGSAT-1 M. Dai et al. https://doi.org/10.1016/j.jag.2024.103847
- SDGSAT-1: Capabilities for Monitoring and Evaluating SDG Indicators H. GUO et al. https://doi.org/10.11728/cjss2024.04.2024-yg15
- Advancing Arctic Sea Ice Remote Sensing with AI and Deep Learning: Opportunities and Challenges W. Li et al. https://doi.org/10.3390/rs16203764
- A new sea ice concentration retrieval algorithm from thermal infrared imagery Y. Ye et al. https://doi.org/10.1080/17538947.2024.2353116
- An Improved YOLOv8-Based Detection Model for Multi-Scale Sea Ice in Satellite Imagery Y. Liu et al. https://doi.org/10.62762/CJIF.2025.695812
- Toward an emissivity-adaptive method for arctic ice/snow/water surface temperature retrieval from FengYun-3D MERSI-II data Z. Zhou et al. https://doi.org/10.1016/j.rse.2026.115487
- Identification of Industrial Heat Source Production Areas Based on SDGSAT-1 Thermal Infrared Imager D. Wang et al. https://doi.org/10.3390/app14062450
- Overview of the studies on the interactions between atmosphere, sea ice, and ocean in the Arctic Ocean and its climatic effects: contributions from Chinese scientists R. Lei et al. https://doi.org/10.1007/s13131-025-2466-4
- SDGSAT-1: A Professional Scientific Satellite for Monitoring SDG Indicators H. Guo et al. https://doi.org/10.1109/JPROC.2025.3649854
- Lead fractions from SAR-derived sea ice divergence during MOSAiC L. von Albedyll et al. https://doi.org/10.5194/tc-18-1259-2024
- Synergic sensing of light and heat emitted by offshore oil and gas platforms in the South China Sea Y. Qiu et al. https://doi.org/10.1080/17538947.2024.2441932
- Improvement in panchromatic-guided denoising algorithm for research on Beijing SDGSAT-1 luminous remote sensing image denoising algorithm L. Zhang et al. https://doi.org/10.1080/17538947.2025.2494075
- Detecting Sea Ice Leads and Floes in the Northwest Passage Using CryoSat-2 A. Swiggs et al. https://doi.org/10.1109/JSTARS.2024.3503286
- MFGC-Net: Bridging and Fusing Multiscale Features and Global Contexts for Multitask Sea Ice Fine Segmentation T. Ma et al. https://doi.org/10.1109/JSTARS.2025.3551976
- Cloud-Tolerant Multiwidth Arctic Sea-Ice Lead Detection Using FY-3D MERSI-II 250-m TIR Data L. Zhang et al. https://doi.org/10.1109/TGRS.2025.3631915
- Detection of Arctic Leads Based on FY-3D/MERSI-II T. Chao et al. https://doi.org/10.1007/s11802-026-6291-y
- Methodology and potential applications of ice/snow surface temperature over polar regions using SDGSAT-1 satellite C. Shi et al. https://doi.org/10.1016/j.rse.2025.114868
- Effects of Arctic sea-ice concentration on turbulent surface fluxes in four atmospheric reanalyses T. Uhlíková et al. https://doi.org/10.5194/tc-18-957-2024
- Arctic Sea ice leads detected using sentinel-1B SAR image and their responses to atmosphere circulation and sea ice dynamics M. Qu et al. https://doi.org/10.1016/j.rse.2024.114193
- The SDGSAT-1 mission and its role in monitoring SDG indicators H. Guo et al. https://doi.org/10.1016/j.rse.2025.114885
- MgB2 thermal kinetic inductance detector T. Jabbari et al. https://doi.org/10.1063/5.0330476
Saved (final revised paper)
Latest update: 05 Aug 2026
Short summary
Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate parameterization of Arctic leads. For the first time, we applied the 30 m resolution data from the Thermal Infrared Spectrometer (TIS) on the emerging SDGSAT-1 to detect Arctic leads. Validation with Sentinel-2 data shows high accuracy for the three TIS bands. Compared to MODIS, the TIS presents more narrow leads, demonstrating its great potential for observing previously unresolvable Arctic leads.
Spaceborne thermal infrared sensors with kilometer-scale resolution cannot support adequate...
