A new sea ice concentration retrieval algorithm from thermal infrared imagery

Sea ice concentration (SIC) can be retrieved from thermal infrared (TIR) imagery due to the distinctive thermal properties of ice and water. Nevertheless, existing TIR-based SIC algorithms rely on surface temperature data, which often introduces additional errors. To address this issue, we have deve...

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Bibliographic Details
Published in:International Journal of Digital Earth
Main Authors: Yufang Ye, Xin Wang, Shaozhe Sun, Qiang Liu, Xinqing Li, Xiao Cheng, Zhuoqi Chen
Format: Article in Journal/Newspaper
Language:English
Published: Taylor & Francis Group 2024
Subjects:
Thermal infrared
sea ice concentration
brightness temperature
MODIS
Mathematical geography. Cartography
GA1-1776
Sea ice
Online Access:https://doi.org/10.1080/17538947.2024.2353116
https://doaj.org/article/780e031d614545cfb76a85b22f99d38e
Description
Summary:Sea ice concentration (SIC) can be retrieved from thermal infrared (TIR) imagery due to the distinctive thermal properties of ice and water. Nevertheless, existing TIR-based SIC algorithms rely on surface temperature data, which often introduces additional errors. To address this issue, we have developed a new TIR ice concentration algorithm (TIRIA) that directly utilizes TIR brightness temperatures. TIRIA considers factors such as seawater salinity, observation angle and their impacts on seawater brightness temperature. TIRIA and a traditional algorithm, the MODIS potential open water algorithm (MPA), are applied to MODIS TIR imagery. Results are evaluated with near-infrared (NIR) SICs and compared with passive microwave (PM) SICs. Overall, TIRIA outperforms MPA, exhibiting a smaller root mean square error (RMSE) (14.01% compared to 17.63%) and higher correlation coefficient (0.89 compared to 0.81). Both TIRIA and MPA tend to underestimate SIC in high SICs while overestimating it in low SICs. Due to its more accurate identification of water, TIRIA significantly mitigates the overestimation in low SICs. Compared to PM-based SICs, both TIR-based SICs exhibit overall overestimations, with better consistency between TIRIA and PM-based SICs. TIRIA, being independent of surface temperature products and theoretically applicable to any TIR data, showcases great potential for future application.

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