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Research Articles

Lead Detection in the Arctic Ocean from Sentinel-3 Satellite Data: A Comprehensive Assessment of Thresholding and Machine Learning Classification Methods

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Pages 462-495 | Received 08 Mar 2022, Accepted 04 Jun 2022, Published online: 08 Jul 2022

References

  • Blalock, H. M. Jr., 1963. Correlated independent variables: The problem of multicollinearity. Social Forces 42 (2):233–7.
  • Breiman, L. 1996. Bagging predictors. Machine Learning 24 (2):123–40.
  • Cai, D., X. He, and J. Han. 2008. Training linear discriminant analysis in linear time. 2008 IEEE 24th international conference on data engineering, 209–217.
  • Cazenave, A., B. Hamlington, M. Horwath, V. R. Barletta, J. Benveniste, D. Chambers, P. Döll, A. E. Hogg, J. F. Legeais, M. Merrifield, et al. 2019. Observational requirements for long-term monitoring of the global mean sea level and its components over the altimetry era. Frontiers in Marine Science 6:582.
  • Collecte Localisation Satellites (CLS). 2011. Surface Topography Mission (STM) SRAL/MWR L2 Algorithms Definition, Accuracy and Specification.
  • Dawson, G., J. Landy, M. Tsamados, A. S. Komarov, S. Howell, H. Heorton, and T. Krumpen. 2022. A 10-year record of Arctic summer sea ice freeboard from CryoSat-2. Remote Sensing of Environment 268:112744.
  • Deng, Z., X. Zhu, D. Cheng, M. Zong, and S. Zhang. 2016. Efficient kNN classification algorithm for big data. Neurocomputing 195:143–8.
  • Dettmering, D., A. Wynne, F. L. Müller, M. Passaro, and F. Seitz. 2018. Lead detection in polar oceans-a comparison of different classification methods for Cryosat-2 SAR data. Remote Sensing 10 (8)1190.
  • Dinardo, S., L. Fenoglio-Marc, C. Buchhaupt, M. Becker, R. Scharroo, M. J. Fernandes, and J. Benveniste. 2018. Coastal SAR and PLRM altimetry in German Bight and West Baltic Sea. Advances in Space Research 62 (6):1371–404.
  • Donlon, C., B. Berruti, A. Buongiorno, M.-H. Ferreira, P. Féménias, J. Frerick, P. Goryl, U. Klein, H. Laur, C. Mavrocordatos, et al. 2012. The global monitoring for environment and security (GMES) sentinel-3 mission. Remote Sensing of Environment 120:37–57.
  • Fenoglio-Marc, F., S. Dinardo, R. Scharroo, A. Roland, M. Dutour Sikiric, B. Lucas, M. Becker, J. Benveniste, and R. Weiss. 2015. The German Bight: A validation of CryoSat-2 altimeter data in SAR mode. Advances in Space Research 55 (11):2641–56.
  • Fernandez-Moran, R., L. Gómez-Chova, L. Alonso, G. Mateo-García, and D. López-Puigdollers. 2021. Towards a novel approach for Sentinel-3 synergistic OLCI/SLSTR cloud and cloud shadow detection based on stereo cloud-top height estimation. ISPRS Journal of Photogrammetry and Remote Sensing 181:238–53.
  • Flach, P. A. 2016. ROC analysis. In Encyclopedia of machine learning and data mining. Boston, USA: Springer.
  • Fleizach, C, and S. Fukushima. 1998. A naive bayes classifier on 1998 JDD cup.
  • Freund, Y., and R. Schapire. 1999. A short introduction to boosting. Journal of Japanese Society for Artificial Intelligence 14 (5):771–80.
  • Friedman, N., D. Geiger, and M. Goldszmidt. 1997. Bayesian Network Classifiers. Machine Learning 29 (2/3):131–63.
  • Grossi, E., and B. Massimo. 2007. Introduction to artificial neural networks. European Journal of gastroenterology & hepatology 19 (12):1046–54.
  • Hamada, M., Y. Kanat, and A. Adejor. 2019. Sea ice drift in the Arctic since the 1950s. International Journal of Innovative Technology and Exploring Engineering 2:1016–9.
  • Hastie, T., R. Tibshirani, and J. Friedman. 2009. The elements of statistical learning: Data mining, inference, and prediction. New York, USA: Springer.
  • IPCC. 2021. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, eds. V. Masson-Delmotte, P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou. Cambridge, UK: Cambridge University Press. In Press.
  • Kaufman, L., and P. Rousseeuw. 1987. Clustering by means of medoids. Statistical data analysis based L1 norm related methods, 405–16. Basel, Switzerland: Birkhäuser Verlag.
  • Kutner, M. H., C. J. Nachtsheim, J. Neter, and W. W. Li. 2005. Applied linear statistical models. New York, USA: McGraw Hill Irwin.
  • Kwok, R. 2018. Arctic sea ice thickness, volume, and multiyear ice coverage: Losses and coupled variability (1958–2018). Environmental Research Letters 13 (10):105005.
  • Laxon, S. 1994. Sea ice altimeter processing scheme at the EODC. International Journal of Remote Sensing 15 (4):915–24. doi:10.1080/01431169408954124.
  • Laxon, S. W., K. A. Giles, A. L. Ridout, D. J. Wingham, R. Willatt, R. Cullen, R. Kwok, A. Schweiger, J. Zhang, C. Haas, et al. 2013. CryoSat‐2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters 40 (4):732–7.
  • Lee, S., J. Im, J. Kim, M. Kim, M. Shin, H. Kim, and L. Quackenbush. 2016. Arctic sea ice thickness estimation from CryoSat-2 Satellite Data using machine learning-based lead detection. Remote Sensing 8 (9):698.
  • Müller, F. L., D. Dettmering, W. Bosch, and F. Seitz. 2017. Monitoring the arctic seas: How satellite altimetry can be used to detect open water in sea-ice regions. Remote Sensing 9 (6):551.
  • Murtagh, F., and P. Contreras. 2012. Algorithms for hierarchical clustering: An overview. WIREs Data Mining and Knowledge Discovery 2 (1):86–97.
  • Peacock, N., and S. Laxon. 2004. Sea surface height determination in the Arctic Ocean from ERS altimetry. Journal of Geophysical Research 109 (C7):1–14.
  • Poisson, J., G. Quartly, A. Kurekin, P. Thibaut, D. Hoang, and F. Nencioli. 2018. Development of an ENVISAT altimetry processor providing sea level continuity between open ocean and arctic leads. IEEE Transactions on Geoscience and Remote Sensing 56 (9):5299–319.
  • Qin, A., S. Shi, P. Suganthan, and M. Loog. 2005. Enhanced direct linear discriminant analysis for feature extraction on high dimensional data. Proceedings of the National Conference on Artificial Intelligence 2, 851–5.
  • Quartly, G. D., E. Rinne, M. Passaro, O. B. Andersen, S. Dinardo, S. Fleury, A. Guillot, S. Hendricks, A. A. Kurekin, F. L. Müller, et al. 2019. Retrieving sea level and freeboard in the Arctic: A review of current radar altimetry methodologies and future perspectives. Remote Sensing 11 (7)881.
  • Quinlan, J. 1986. Induction of decision trees. Machine Learning 1 (1):81–106.
  • Raney, R. 1998. The delay/doppler radar altimeter. IEEE Transactions on Geoscience and Remote Sensing 36 (5):1578–88.
  • Ray, C., C. Martin-Puig, M. P. Clarizia, G. Ruffini, S. Dinardo, C. Gommenginger, and J. Benveniste. 2015. SAR altimeter backscattered waveform model. IEEE Transactions on Geoscience and Remote Sensing 53 (2):911–9.
  • Ricker, R., S. Hendricks, V. Helm, H. Skourup, and M. Davidson. 2014. Sensitivity of CryoSat-2 Arctic sea-ice freeboard and thickness on radar-waveform interpretation. The Cryosphere 8 (4):1607–22.
  • Rose, S., R. Forsberg, and L. Pedersen. 2013. Measurements of sea ice by satellite and airborne altimetry, PhD diss., DTU Space.
  • Rose, S. K., O. B. Andersen, M. Passaro, C. A. Ludwigsen, and C. Schwatke. 2019. Arctic Ocean sea level record from the complete radar altimetry era: 1991–2018. Remote Sensing 11 (14):1672.
  • Sani, H. M, C. Lei, and D. Neagu. 2018. Computational complexity analysis of decision tree algorithms. In International Conference on Innovative Techniques and Applications of Artificial Intelligence. Springer.
  • Schulz, A. T., and M. Naeije. 2018. SAR Retracking in the Arctic: Development of a year-round retrackers system. Advances in Space Research 62 (6):1292–306.
  • Seiffert, C., T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano. 2008. RUSBoost: Improving classification performance when training data is skewed. Presented at 2008 19th International Conference on Pattern Recognition, IEEE.
  • Savas, C., and F. Dovis. 2019. The impact of different kernel functions on the performance of scintillation detection based on support vector machines. Sensors 19 (23):5219.
  • Shen, X., J. Zhang, X. Zhang, J. Meng, and C. Ke. 2017. Sea ice classification Using Cryosat-2 altimeter data by optimal classifier-feature assembly. IEEE Geoscience and Remote Sensing Letters 14 (11):1948–52.
  • Shu, S., X. Zhou, X. Shen, Z. Liu, Z. Tang, H. Li, C. Ke, and J. Li. 2020. Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF). Marine Geodesy 43 (3):213–33.
  • Tangirala, S. 2020. Evaluating the impact of GINI index and information gain on classification using decision tree classifier algorithm. International Journal of Advanced Computer Science and Applications 11 (2):612–9.
  • Timmermans, B. W., C. P. Gommenginger, G. Dodet, and J. R. Bidlot. 2020. Global wave height trends and variability from new multimission satellite altimeter products, reanalyses, and wave buoys. Geophysical Research Letters 47 (9):e2019GL086880.
  • Wernecke, A., and L. Kaleschke. 2015. Lead detection in Arctic sea ice from CryoSat-2: Quality assessment, lead area fraction and width distribution. The Cryosphere 9 (5):1955–68.
  • Wingham, D., C. Francis, S. Baker, C. Bouzinac, D. Brockley, R. de Cullen, P. Chateau-Thierry, S. Laxon, U. Mallow, C. Mavrocordatos, et al. 2006. CryoSat: A mission to determine the fluctuations in Earth’s land and marine ice fields. Advances in Space Research 37 (4):841–71.
  • Whittingham, H, and S. K. Ashenden. 2021. Hit discovery. In The Era of Artificial Intelligence, Machine Learning, and Data Science in the Pharmaceutical Industry. San Diego, USA: Academic Press.
  • Xu, L., J. Li, and A. Brenning. 2014. A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery. Remote Sensing of Environment 141:14–23. rse.2013.10.012.
  • Yoo, W., R. Mayberry, S. Bae, K. Singh, Q. P. He, and J. W. Lillard. Jr. 2014. A study of effects of multicollinearity in the multivariable analysis. International Journal of applied science and technology 4 (5):9–19.
  • Zakharova, E. A., S. Fleury, K. Guerreiro, S. Willmes, F. Rémy, A. V. Kouraev, and G. Heinemann. 2015. Sea ice leads detection using SARAL/AltiKa altimeter. Marine Geodesy 38 (sup1):522–33.
  • Zhang, H., T. W. Weng, P. Y. Chen, C. J. Hsieh, and L. Daniel. 2018. Efficient neural network robustness certification with general activation functions. Advances in neural information processing systems 31: 4944–4953.
  • Zygmuntowska, M., K. Khvorostovsky, V. Helm, and S. Sandven. 2013. Waveform classification of airborne synthetic aperture radar altimeter over Arctic sea ice. The Cryosphere 7 (4):1315–24.