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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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  • Antoine J P, Murenzi R. 1996. Two-dimensional directional wavelets and the scale-angle representation. Signal Processing, 52(3): 259–281, doi: 10.1016/0165-1684(96)00065-5
    Antoine J P, Murenzi R, Vandergheynst P. 1999. Directional wavelets revisited: Cauchy wavelets and symmetry detection in patterns. Applied and Computational Harmonic Analysis, 6(3): 314–345, doi: 10.1006/acha.1998.0255
    Babb D G, Landy J C, Barber D G, et al. 2019. Winter sea ice export from the Beaufort Sea as a preconditioning mechanism for enhanced summer melt: A case study of 2016. Journal of Geophysical Research: Oceans, 124(9): 6575–6600, doi: 10.1029/2019JC 015053
    Beckers J F, Renner A H H, Spreen G, et al. 2015. Sea-ice surface roughness estimates from airborne laser scanner and laser altimeter observations in Fram Strait and north of Svalbard. Annals of Glaciology, 56(69): 235–244, doi: 10.3189/2015AoG 69A717
    Cafarella S M, Scharien R, Geldsetzer T, et al. 2019. Estimation of level and deformed first-year sea ice surface roughness in the Canadian Arctic archipelago from C- and L- band synthetic aperture radar. Canadian Journal of Remote Sensing, 45(3/4): 457–475, doi: 10.1080/07038992.2019.1647102
    Carlström A. 1997. A microwave backscattering model for deformed first-year sea ice and comparisons with SAR data. IEEE Transactions on Geoscience and Remote Sensing, 35(2): 378–391, doi: 10.1109/36.563277
    Carlström A, Ulander L M H, Hakansson B. 1994. Model for estimating surface roughness of level and ridged sea ice using ERS-1 SAR. In: 1994 IEEE International Geoscience and Remote Sensing Symposium. Pasadena, CA, USA: IEEE,168–170
    Daubechies I. 1992. Ten Lectures on Wavelets. Philadelphia, PA, USA: Society for Industrial and Applied Mathematics
    Drucker H. 1997. Improving regressors using boosting techniques. In: Proceedings the 14th International Conference on Machine Learning. Nashiville: Morgan Kaufmann Publishers Inc., 107–115
    Efendi A, Fitriani R, Naufal H I, et al. 2020. Ensemble Adaboost in classification and regression trees to overcome class imbalance in credit status of bank customers. Journal of Theoretical and Applied Information Technology, 98(17): 3428–3437
    Filipponi F. 2019. Sentinel-1 GRD preprocessing workflow. Proceedings, 18(1): 11
    Grenfell T C, Perovich D K. 1984. Spectral albedos of sea ice and incident solar irradiance in the southern Beaufort Sea. Journal of Geophysical Research: Oceans, 89(C3): 3573–3580, doi: 10.1029/JC089iC03p03573
    Gu Xiaowei, Angelov P P. 2022. Multiclass fuzzily weighted adaptive-boosting-based self-organizing fuzzy inference ensemble systems for classification. IEEE Transactions on Fuzzy Systems, 30(9): 3722–3735, doi: 10.1109/TFUZZ.2021.3126116
    Gupta M, Barber D G. 2015. Sub-pixel evaluation of sea ice roughness using AMSR-E data. International Journal of Remote Sensing, 36(3): 749–763, doi: 10.1080/01431161.2014.1001081
    Hong S. 2010. Detection of small-scale roughness and refractive index of sea ice in passive satellite microwave remote sensing. Remote Sensing of Environment, 114(5): 1136–1140, doi: 10.1016/j.rse.2009.12.015
    Hsieh W W. 2023. Decision trees, random forests and boosting. In: Introduction to Environmental Data Science. Cambridge: Cambridge University Press, 473–493
    Jackson C R, Apel J R. 2004. Synthetic Aperture Radar Marine User’s Manual. Washington, DC, USA: National Oceanic and Atmospheric Administration, 377–379
    Jiang Mingzhe, Clausi D A, Xu Linlin. 2022. Sea-ice mapping of RADARSAT-2 imagery by integrating spatial contexture with textural features. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15: 7964–7977, doi: 10.1109/JSTARS.2022.3205849
    Kim S H, Kim H C, Hyun C U, et al. 2020. Evolution of backscattering coefficients of drifting multi-year sea ice during end of melting and onset of freeze-up in the western Beaufort Sea. Remote Sensing, 12(9): 1378, doi: 10.3390/rs12091378
    Landy J C, Petty A A, Tsamados M, et al. 2020. Sea ice roughness overlooked as a key source of uncertainty in CryoSat-2 ice freeboard retrievals. Journal of Geophysical Research: Oceans, 125(5): e2019JC015820, doi: 10.1029/2019JC015820
    Lee J S, Jurkevich L, Dewaele P, et al. 1994. Speckle filtering of synthetic aperture radar images: A review. Remote Sensing Reviews, 8(4): 313–340, doi: 10.1080/02757259409532206
    Li Xiaoming, Sun Yan, Zhang Qiang. 2021. Extraction of sea ice cover by Sentinel-1 SAR based on support vector machine with unsupervised generation of training data. IEEE Transactions on Geoscience and Remote Sensing, 59(4): 3040–3053, doi: 10.1109/TGRS.2020.3007789
    Liu Mengjie, Dai Yongshou, Zhang Jie, et al. 2016. The microwave scattering characteristics of sea ice in the Bohai Sea. Acta Oceanologica Sinica, 35(5): 89–98, doi: 10.1007/s13131-016-0861-6
    Marbouti M, Antropov O, Eriksson P, et al. 2018. Automated sea ice classification over the Baltic Sea using multiparametric features of Tandem-X InSAR images. In: 2018 IEEE International Geoscience and Remote Sensing Symposium. Valencia, Spain: IEEE, 7328–7331
    Martin T, Tsamados M, Schroeder D, et al. 2016. The impact of variable sea ice roughness on changes in Arctic Ocean surface stress: A model study. Journal of Geophysical Research: Oceans, 121(3): 1931–1952, doi: 10.1002/2015JC011186
    Mohr F, van Rijn J N. 2022. Learning curves for decision making in supervised machine learning—A survey. arXiv: 2201.12150
    Mosadegh E, Nolin A W. 2020. Estimating Arctic sea ice surface roughness by using back propagation neural network. In: AGU Fall Meeting 2020. San Francisco, CA, USA: AGU, C014–0005
    Mosadegh E, Nolin A W. 2022. A new data processing system for generating sea ice surface roughness products from the multi-angle imaging spectroradiometer (MISR) imagery. Remote Sensing, 14(19): 4979, doi: 10.3390/rs14194979
    Nolin A W, Mar E. 2018. Arctic sea ice surface roughness estimated from multi-angular reflectance satellite imagery. Remote Sensing, 11(1): 50, doi: 10.3390/rs11010050
    Palerme C, Müller M. 2021. Calibration of sea ice drift forecasts using random forest algorithms. The Cryosphere, 15(8): 3989–4004, doi: 10.5194/tc-15-3989-2021
    Pedregosa F, Varoquaux G, Gramfort A, et al. 2011. Scikit-learn: Machine learning in Python. Journal of Machine Learning Research, 12: 2825–2830
    Prasad S, Haynes R D, Zakharov I, et al. 2021. Estimation of sea ice parameters using an assimilated sea ice model with a variable drag formulation. Ocean Modelling, 158: 101739, doi: 10.1016/j.ocemod.2020.101739
    Segal R A, Scharien R K, Cafarella S, et al. 2020. Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic archipelago using Sentinel-1 synthetic aperture radar and the multi-angle imaging spectroradiometer. Annals of Glaciology, 61(83): 284–298, doi: 10.1017/aog.2020.48
    Shanmugasundar G, Vanitha M, Čep R, et al. 2021. A comparative study of linear, Random Forest and AdaBoost Regressions for modeling non-traditional machining. Processes, 9(11): 2015, doi: 10.3390/pr9112015
    Studinger M. 2014. IceBridge ATM l2 Icessn elevation, slope, and roughness, version 2. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://nsidc.org/data/ILATM2/versions/2 [2023-06-01]
    Torres R, Snoeij P, Geudtner D, et al. 2012. GMES Sentinel-1 mission. Remote Sensing of Environment, 120: 9–24, doi: 10.1016/j.rse.2011.05.028
    Tschudi M, Meier W N, Stewart J S, et al. 2019. EASE-grid sea ice age, version 4. NASA National Snow and Ice Data Center Distributed Active Archive Center. https://nsidc.org/data/NSIDC-0611/versions/4 [2023-06-01]
    Wen Xiaoyang, Xue Cunjin, Dong Qing. 2011. The Arctic sea ice surface roughness estimation and application. In: Proceedings of the 21st International Offshore and Polar Engineering Conference. Maui, HI, USA: ISOPE, 958–961
    Xiao Changjiang, Chen Nengcheng, Hu Chuli, et al. 2019. Short and mid-term sea surface temperature prediction using time-series satellite data and LSTM-AdaBoost combination approach. Remote Sensing of Environment, 233: 111358, doi: 10.1016/j.rse.2019.111358
    Yan Qingyun, Huang Weimin. 2019. Detecting sea ice from TechDemoSat-1 data using Support Vector Machines with feature selection. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(5): 1409–1416, doi: 10.1109/JSTARS.2019.2907008
    Zhu Zonghai, Wang Zhe, Li Dongdong, et al. 2020. Geometric structural ensemble learning for imbalanced problems. IEEE Transactions on Cybernetics, 50(4): 1617–1629, doi: 10.1109/TCYB.2018.2877663
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