{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T16:05:00Z","timestamp":1724601900129},"reference-count":38,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2018,7,28]],"date-time":"2018-07-28T00:00:00Z","timestamp":1532736000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"In polar regions, sea-ice hinders the precise observation of Sea Surface Heights (SSH) by satellite altimetry. In order to derive reliable heights for the openings within the ice, two steps have to be fulfilled: (1) the correct identification of water (e.g., in leads or polynias), a process known as lead classification; and (2) dedicated retracking algorithms to extract the ranges from the radar echoes. This study focuses on the first point and aims at identifying the best available lead classification method for Cryosat-2 SAR data. Four different altimeter lead classification methods are compared and assessed with respect to very high resolution airborne imagery. These methods are the maximum power classifier; multi-parameter classification method primarily based on pulse peakiness; multi-observation analysis of stack peakiness; and an unsupervised classification method. The unsupervised classification method with 25 clusters consistently performs best with an overall accuracy of 97%. Furthermore, this method does not require any knowledge of specific ice characteristics within the study area and is therefore the recommended lead detection algorithm for Cryosat-2 SAR in polar oceans.<\/jats:p>","DOI":"10.3390\/rs10081190","type":"journal-article","created":{"date-parts":[[2018,7,30]],"date-time":"2018-07-30T15:55:08Z","timestamp":1532966108000},"page":"1190","source":"Crossref","is-referenced-by-count":11,"title":["Lead Detection in Polar Oceans\u2014A Comparison of Different Classification Methods for Cryosat-2 SAR Data"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"http:\/\/orcid.org\/0000-0002-8940-4639","authenticated-orcid":false,"given":"Denise","family":"Dettmering","sequence":"first","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"given":"Alan","family":"Wynne","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0003-4219-0407","authenticated-orcid":false,"given":"Felix L.","family":"M\u00fcller","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3372-3948","authenticated-orcid":false,"given":"Marcello","family":"Passaro","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-0718-6069","authenticated-orcid":false,"given":"Florian","family":"Seitz","sequence":"additional","affiliation":[{"name":"Deutsches Geod\u00e4tisches Forschungsinstitut, Technische Universit\u00e4t M\u00fcnchen, Arcisstra\u00dfe 21, 80333 Munich, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2018,7,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2167","DOI":"10.5194\/tc-9-1955-2015","article-title":"Lead Detection in Arctic Sea Ice from Cryosat-2: Quality Assessment, Lead Area Fraction and Width Distribution","volume":"9","author":"Wernecke","year":"2015","journal-title":"Cryosphere"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"947","DOI":"10.1038\/nature02050","article-title":"High Interannual Variability of Sea Ice Thickness in the Arctic Region","volume":"425","author":"Laxon","year":"2003","journal-title":"Lett. Nat."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Farrell, S.L., Laxon, S.W., McAdoo, D.C., Yi, D., and Zwally, H.J. (2009). Five Years of Arctic Sea Ice Freeboard Measurements from the Ice, Cloud and land Elevation Satellite. J. Geophys. Res., 114.","DOI":"10.1029\/2008JC005074"},{"key":"ref_4","unstructured":"Dwyer, R.E., and Godin, R.H. (1980). Determining Sea-Ice Boundaries and Ice Roughness using GEOS-3 Altimeter Data, Technical Report."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"915","DOI":"10.1080\/01431169408954124","article-title":"Sea Ice Altimeter Processing Scheme at the EODC","volume":"15","author":"Laxon","year":"1994","journal-title":"Int. J. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.rse.2008.10.015","article-title":"Comparison of Envisat Radar and Airborne Laser Altimeter Measurements Over Arctic Sea Ice","volume":"113","author":"Connor","year":"2009","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"343","DOI":"10.5194\/tc-6-343-2012","article-title":"An Algorithm to Detect Sea Ice Leads by Using AMSR-E Passive Microwave Imagery","volume":"6","author":"Kaleschke","year":"2012","journal-title":"Cryosphere"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1315","DOI":"10.5194\/tc-7-1315-2013","article-title":"Waveform Classification of Airborne Synthetic Aperture Radar Altimeter over Arctic Sea Ice","volume":"7","author":"Zygmuntowska","year":"2013","journal-title":"Cryosphere"},{"key":"ref_9","unstructured":"Friedman, N., and Kohavi, R. (2002). Bayesian Classification. Handbook of Data Mining and Knowledge Discovery, Oxford University Press."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Passaro, M., M\u00fcller, F.L., and Dettmering, D. (2017). Lead Detection using CryoSat-2 Delay Doppler Processing and Sentinel-1 SAR images. Adv. Space Res.","DOI":"10.1016\/j.asr.2017.07.011"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"M\u00fcller, F.L., Dettmering, D., Bosch, W., and Seitz, F. (2017). Monitoring the Arctic Seas: How Satellite Altimetry can be used to Detect Open Water in Sea-Ice Regions. Remote Sens., 9.","DOI":"10.3390\/rs9060551"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"3336","DOI":"10.1016\/j.eswa.2008.01.039","article-title":"A Simple and Fast Algorithm for K-medoids Clustering","volume":"36","author":"Park","year":"2009","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cover, T.M., and Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Trans. Inf. Theory, 13.","DOI":"10.1109\/TIT.1967.1053964"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1948","DOI":"10.1109\/LGRS.2017.2743339","article-title":"Sea Ice Classification Using Cryosat-2 Altimeter Data by Optimal Classifier\u2013Feature Assembly","volume":"14","author":"Shen","year":"2017","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1665","DOI":"10.5194\/tc-12-1665-2018","article-title":"Arctic Lead Detection using a Waveform Mixture Algorithm from CryoSat-2 Data","volume":"12","author":"Lee","year":"2018","journal-title":"Cryosphere"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1607","DOI":"10.5194\/tc-8-1607-2014","article-title":"Sensitivity of CryoSat-2 Arctic Sea-ice Freeboard and Thickness on Radar-Waveform Interpretation","volume":"8","author":"Ricker","year":"2014","journal-title":"Cryosphere"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, L., Schneider, R., Andersen, O.B., and Bauer-Gottwein, P. (2017). CryoSat-2 Altimetry Applications over Rivers and Lakes. Water, 9.","DOI":"10.3390\/w9030211"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1109\/36.718861","article-title":"The Delay\/Doppler Radar Altimeter","volume":"36","author":"Raney","year":"1998","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Martin-Puig, C., and Ruffini, G. (2009, January 12\u201317). SAR Altimeter Retracker Performance Bound over Water Surfaces. Proceedings of the 2009 IEEE International Geoscience and Remote Sensing Symposium, Cape Town, South Africa.","DOI":"10.1109\/IGARSS.2009.5417633"},{"key":"ref_20","unstructured":"European Space Agency (2017, September 21). Cryosat Product Handbook. Available online: http:\/\/emits.sso.esa.int\/emits-doc\/ESRIN\/7158\/CryoSat-PHB-17apr2012.pdf."},{"key":"ref_21","unstructured":"Scagliola, M., and Fornari, M. (2018, July 27). Available online: https:\/\/earth.esa.int\/documents\/10174\/1773005\/C2-BaselineC_L1b_improvements_1.3."},{"key":"ref_22","unstructured":"Dinardo, S. (2013). Guidelines for the SAR (Delay-Doppler) L1b Processing, European Space Agency, ESRIN. Available online: https:\/\/wiki.services.eoportal.org\/tiki-download_wiki_attachment.php?attId=2540."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Studinger, M., Koenig, L., Martin, S., and Sonntag, J. (2010, January 25\u201330). Operation icebridge: Using instrumented aircraft to bridge the observational gap between icesat and icesat-2. Proceedings of the 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA.","DOI":"10.1109\/IGARSS.2010.5650555"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"456","DOI":"10.1016\/j.rse.2017.10.010","article-title":"Assessing three waveform retrackers on sea ice freeboard retrieval from Cryosat-2 using Operation IceBridge Airborne altimetry datasets","volume":"204","author":"Xia","year":"2018","journal-title":"Remote Sens. Environ."},{"key":"ref_25","unstructured":"Dominguez, R. (2010). IceBridge DMS L1B Geolocated and Orthorectified Images, Version 1, Updated 2017."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1016\/j.asr.2005.07.027","article-title":"CryoSat: A Mission to Determine the Fluctuations in Earth\u2019s Land and Marine Ice Fields","volume":"37","author":"Wingham","year":"2006","journal-title":"Adv. Space Res."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning, Springer-Verlag.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Durbin, R., Eddy, S.R., Krogh, A., and Mitchison, G. (1998). Biological Sequence Analysis, Cambridge University Press.","DOI":"10.1017\/CBO9780511790492"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Banks, D., House, L., McMorris, F., Arabie, P., and Gaul, W. (2004). Classification, Clustering, and Data Mining Applications: Proceedings of the Meeting of the International Federation of Classification Societies (IFCS), Illinois Institute of Technology, Chicago, 15\u201318 July 2004, Springer. Studies in Classification, Data Analysis, and Knowledge Organization.","DOI":"10.1007\/978-3-642-17103-1"},{"key":"ref_30","unstructured":"Kvingedal, B. (2005). On Sea Ice Variability in the Nordic Seas. [Ph.D. Thesis, University of Bergen]. Available online: http:\/\/web.gfi.uib.no\/publikasjoner\/pdf\/Kvingedal.pdf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1093\/comjnl\/7.4.308","article-title":"A Simplex Method for Function Minimization","volume":"7","author":"Nelder","year":"1965","journal-title":"Comput. J."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"16341","DOI":"10.1029\/94JC01194","article-title":"Surface Characteristics of Lead Ice","volume":"99","author":"Perovich","year":"1994","journal-title":"J. Geophyis. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Onana, V.D.P., Kurtz, N.T., Farrell, S.L., Koenig, L.S., Studinger, M., and Harbeck, J.P. (2013). A Sea-Ice Lead Detection Algorithm for Use With High-Resolution Airborne Visible Imagery. IEEE Trans. Geosci. Remote Sens., 51.","DOI":"10.1109\/TGRS.2012.2202666"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An Introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recogn. Lett."},{"key":"ref_35","unstructured":"Xue, R., Wunsch, D.C., and IEEE Computational Intelligence Society (2009). Clustering, Wiley. IEEE Press Series on Computational Intelligence."},{"key":"ref_36","first-page":"397","article-title":"Accuracy Assessment: A User\u2019s Perspective","volume":"52","author":"Story","year":"1986","journal-title":"Am. Soc. Photogramm. Remote Sens. Remote Sens. Brief"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1399","DOI":"10.1109\/LGRS.2013.2293960","article-title":"Measuring the Pitch of CryoSat-2 Using the SAR Mode of the SIRAL Altimeter","volume":"11","author":"Galin","year":"2014","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Armitage, T.W.K., and Davidson, M.W.J. (2014). Using the Interferometric Capabilities of the ESA CryoSat-2 Mission to Improve the Accuracy of Sea Ice Freeboard Retrievals. IEEE Trans. Geosci. Remote Sens., 52.","DOI":"10.1109\/TGRS.2013.2242082"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1190\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,11]],"date-time":"2024-06-11T22:47:13Z","timestamp":1718146033000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/10\/8\/1190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7,28]]},"references-count":38,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2018,8]]}},"alternative-id":["rs10081190"],"URL":"https:\/\/doi.org\/10.3390\/rs10081190","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,7,28]]}}}