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Artificial neural network-based techniques for the retrieval of SWE [snow water equivalent] and snow depth from SSM/I data / Marco Tedesco in Remote sensing of environment, vol 90 n° 1 (15/03/2004)  

Public [article]  inRemote sensing of environment > vol 90 n° 1 (15/03/2004) . - pp 76 - 85 Titre : Artificial neural network-based techniques for the retrieval of SWE [snow water equivalent] and snow depth from SSM/I data Type de document : Article/Communication Auteurs : Marco Tedesco, Auteur ; J.T. Pulliainen, Auteur ; M. Takala, Auteur ; M.T. Hallikainen, Auteur ; P. Pampaloni, Auteur Année de publication : 2004 Article en page(s) : pp 76 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse comparative [Termes IGN] Finlande [Termes IGN] neige [Termes IGN] réalité de terrain [Termes IGN] réseau neuronal artificiel [Termes IGN] Special sensor microwave imager [Termes IGN] télédétection en hyperfréquence Résumé : (Auteur) The retrieval of snow water equivalent (SWE) and snow depth is performed by inverting Special Sensor Microwave Imager (SSM/I) brightness temperatures at 19 and 37 GHz using artificial neural network ANN-based techniques. The SSM/I used data, which consist of Pathfinder Daily EASE-Grid brightness temperatures, were supplied by the National Snow and Ice Data Centre (NSIDC). They were gathered during the period of time included between the beginning of 1996 and the end of 1999 all over Finland. A ground snow data set based on observations of the Finnish Environment Institute (SYKE) and the Finnish Meteorological Institute (FMI) was used to estimate the performances of the technique. The ANN results were confronted with those obtained using the spectral polarization difference (SPD) algorithm, the HUT model-based iterative inversion and the Chang algorithm, by comparing the RMSE, the R , and the regression coefficients. In general, it was observed that the results obtained through ANN-based technique are better than, or comparable to, those obtained through other approaches, when trained with simulated data. Performances were very good when the ANN were trained with experimental data. Numéro de notice : A2004-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2003.12.002 En ligne : https://doi.org/10.1016/j.rse.2003.12.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26656 [article]