{"refrec":{"BRefID":337696,"RR":"<b>Safieddine, S.; Parracho, A.C.; George, M.; Aires, F.; Pellet, V.; Clarisse, L.; Whitburn, S.; Lezeaux, O.; Thépaut, J.-N.; Hersbach, H.; Radnoti, G.; Goettsche, F.; Martin, M.; Doutriaux-Boucher, M.; Coppens, D.; August, T.; Zhou, D.K.; Clerbaux, C.</b> (2020). Artificial neural networks to retrieve land and sea skin temperature from IASI. <i>Remote Sens. 12(17)</i>: 2777. <a href=\"https://hdl.handle.net/10.3390/rs12172777\" target=\"_blank\">https://hdl.handle.net/10.3390/rs12172777</a>","BEntID":334320,"PublicFlag":1,"CheckedFlag":1,"wosflag":1,"vabbflag":1,"RefStringPartII":". <i>Remote Sens. 12(17)</i>: 2777. <a href=\"https://hdl.handle.net/10.3390/rs12172777\" target=\"_blank\">https://hdl.handle.net/10.3390/rs12172777</a>","DocTypID":8,"DocType":"Journal article","MarineFlag":0,"FreshFlag":0,"BrackishFlag":0,"TerrestrialFlag":0,"Authorstring":"Safieddine, S.; Parracho, A.C.; George, M.; Aires, F.; Pellet, V.; Clarisse, L.; Whitburn, S.; Lezeaux, O.; Thépaut, J.-N.; Hersbach, H.; Radnoti, G.; Goettsche, F.; Martin, M.; Doutriaux-Boucher, M.; Coppens, D.; August, T.; Zhou, D.K.; Clerbaux, C.","OrigTitleTranslFlag":0,"Authorstringtrunc":"Safieddine, S. <i>et al.</i>","Englishabstract":"Surface skin temperature (T<sub>skin</sub>) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving T<sub>skin</sub> based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI T<sub>skin</sub> product (i.e., T<sub>ANN</sub>) is evaluated against T<sub>skin</sub> from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI T<sub>ANN</sub> and the T<sub>skin</sub> from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust T<sub>skin</sub> dataset suitable for T<sub>skin</sub> long-term spatio-temporal variability and trend analysis.","AbstractOtherLang":null,"BibLvlCode":"AS","StandardTitle":"Artificial neural networks to retrieve land and sea skin temperature from IASI","OrigTitleLangCode":"en","OrigTitleLangCodeExtended":"eng","OrigTitleLangID":15,"DateLastModified":{"date":"2026-05-09 01:33:28.502409","timezone_type":1,"timezone":"+02:00"},"UserAccessRight":null,"UserAccID":null,"AuthorKeywords":"skin temperature; IASI; neural networks; entropy reduction; ERA5;EUMETSAT; SURFRAD","OtherDescriptors":null,"Notes":null,"AnaPub":2020,"MonPub":null,"DateUpdate":"2021-05-18","DateCreate":"2021-05-17","SecASFANote":null,"ConfID":null,"PeerRev":1,"VlizCoreFlag":1,"WoScode":"WOS:000571160000001","VABBcode":null,"OpenAcc":1,"Handle":"10.3390/rs12172777"},"refs":null,"anarec":{"AnaID":337696,"PubliDate":2020,"Pagination":"2777","XtraPublOfAnaID":null,"ISBN":null,"Volume":"12","Issue":"17","BRefMon":null,"BRefMonRR":null,"BRefXtra":null,"BRefXtraRR":null,"SerBRefID":221386,"SerRR":"Remote Sensing. 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