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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. doi: 10.1007/s13131-023-2248-9
Citation: 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. doi: 10.1007/s13131-023-2248-9

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

doi: 10.1007/s13131-023-2248-9
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.
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  • Corresponding author: E-mail: chenzhongbiao@nuist.edu.cn
  • Received Date: 2023-06-14
  • Accepted Date: 2023-08-29
  • Available Online: 2024-03-08
  • 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.
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