An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

Pengyi Chen; Zhongbiao Chen; Runxia Sun; Yijun He

doi:10.1007/s13131-023-2248-9

Volume 43 Issue 5

May 2024

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Article Navigation > Acta Oceanologica Sinica > 2024 > 43(5): 78-90

Pengyi Chen, Zhongbiao Chen, Runxia Sun, Yijun He. An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images[J]. Acta Oceanologica Sinica, 2024, 43(5): 78-90. doi: 10.1007/s13131-023-2248-9

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Pengyi Chen, Zhongbiao Chen, Runxia Sun, Yijun He. An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images[J]. Acta Oceanologica Sinica, 2024, 43(5): 78-90. doi: 10.1007/s13131-023-2248-9

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An ensemble learning method to retrieve sea ice roughness from Sentinel-1 SAR images

doi: 10.1007/s13131-023-2248-9

1.
School of Marine Sciences, Nanjing University of Information Science & Technology, Nanjing 210044, China
2.
East Sea Information Center of State Oceanic Administration, Shanghai 200136, China

Funds: The National Key Research and Development Program of China under contract No. 2021YFC2803301; the National Natural Science Foundation of China under contract No. 41977302; the National Natural Science Youth Foundation of China under contract No. 41506199; the Natural Science Youth Foundation of Jiangsu Province under contrant No. BK20150905; the Science and Technology Project of China Huaneng Group Co., Ltd. under contract No. HNKJ20-H66.

More Information

Corresponding author: E-mail: chenzhongbiao@nuist.edu.cn
Received Date: 2023-06-14
Accepted Date: 2023-08-29

Available Online: 2024-03-08

Publish Date: 2024-05-30

Abstract

Abstract

Sea ice surface roughness (SIR) affects the energy transfer between the atmosphere and the ocean, and it is also an important indicator for sea ice characteristics. To obtain a small-scale SIR with high spatial resolution, a novel method is proposed to retrieve SIR from Sentinel-1 synthetic aperture radar (SAR) images, utilizing an ensemble learning method. Firstly, the two-dimensional continuous wavelet transform is applied to obtain the spatial information of sea ice, including the scale and direction of ice patterns. Secondly, a model is developed using the Adaboost Regression model to establish a relationship among SIR, radar backscatter and the spatial information of sea ice. The proposed method is validated by using the SIR retrieved from SAR images and comparing it to the measurements obtained by the Airborne Topographic Mapper (ATM) in the summer Beaufort Sea. The determination of coefficient, mean absolute error, root-mean-square error and mean absolute percentage error of the testing data are 0.91, 1.71 cm, 2.82 cm, and 36.37%, respectively, which are reasonable. Moreover, K-fold cross-validation and learning curves are analyzed, which also demonstrate the method’s applicability in retrieving SIR from SAR images.
- 2-D Cauchy continuous wavelet transform (CWT),
- Adaboost Regression,
- sea ice,
- sea ice surface roughness

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References(41)

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