{"refrec":{"BRefID":337931,"RR":"<b>Barth, A.; Alvera-Azcárate, A.; Licer, M.; Beckers, J.-M.</b> (2020). DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations. <i>Geosci. Model Dev. 13(3)</i>: 1609-1622. <a href=\"https://hdl.handle.net/10.5194/gmd-13-1609-2020\" target=\"_blank\">https://hdl.handle.net/10.5194/gmd-13-1609-2020</a>","BEntID":334555,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Geosci. Model Dev. 13(3)</i>: 1609-1622. <a href=\"https://hdl.handle.net/10.5194/gmd-13-1609-2020\" target=\"_blank\">https://hdl.handle.net/10.5194/gmd-13-1609-2020</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":1,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Barth, A.; Alvera-Azcárate, A.; Licer, M.; Beckers, J.-M.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Barth, A. <i>et al.</i>","Englishabstract":"A method to reconstruct missing data in sea surface temperature data using a neural network is presented. Satellite observations working in the optical and infrared bands are affected by clouds, which obscure part of the ocean underneath. In this paper, a neural network with the structure of a convolutional auto-encoder is developed to reconstruct the missing data based on the available cloud-free pixels in satellite images. Contrary to standard image reconstruction with neural networks, this application requires a method to handle missing data (or data with variable accuracy) in the training phase. The present work shows a consistent approach which uses the satellite data and its expected error variance as input and provides the reconstructed field along with its expected error variance as output. The neural network is trained by maximizing the likelihood of the observed value. The approach, called DINCAE (Data INterpolating Convolutional Auto-Encoder), is applied to a 25-year time series of Advanced Very High Resolution Radiometer (AVHRR) sea surface temperature data and compared to DINEOF (Data INterpolating Empirical Orthogonal Functions), a commonly used method to reconstruct missing data based on an EOF (empirical orthogonal function) decomposition. The reconstruction error of both approaches is computed using cross-validation and in situ observations from the World Ocean Database. DINCAE results have lower error while showing higher variability than the DINEOF reconstruction.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"DINCAE 1.0: a convolutional neural network with error estimates to reconstruct sea surface temperature satellite observations","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-04-21 01:33:00.738949","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":null,"OtherDescriptors":null,"Notes":null,"AnaPub":2020,"MonPub":null,"DateUpdate":"2021-05-31","DateCreate":"2021-05-17","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000522810500002","VABBcode":null,"OpenAcc":1,"Handle":"10.5194/gmd-13-1609-2020"},"refs":null,"anarec":{"AnaID":337931,"PubliDate":2020,"Pagination":"1609-1622","XtraPublOfAnaID":null,"ISBN":null,"Volume":"13","Issue":"3","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":209852,"SerRR":"Geoscientific Model Development. 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