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Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean / Kunpeng Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 6 (June 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 6 (June 2021) . - pp 4579 - 4589 Titre : Retrieval of ultraviolet diffuse attenuation coefficients from ocean color using the kernel principal components analysis over ocean Type de document : Article/Communication Auteurs : Kunpeng Sun, Auteur ; Tinglu Zhang, Auteur ; Shuguo Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 4579 - 4589 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse en composantes principales [Termes IGN] atténuation [Termes IGN] couleur de l'océan [Termes IGN] image Aqua-MODIS [Termes IGN] image NPP-VIIRS [Termes IGN] méthode fondée sur le noyau [Termes IGN] rayonnement ultraviolet [Termes IGN] régression multiple Résumé : (auteur) Underwater ultraviolet radiation (UVR), which plays a significant role in photobiological and photochemical processes, is one of the key factors in marine ecosystems. A new algorithm KpcaUV, based on kernel principal component analysis (KPCA) and multiple linear regression (MLR), was proposed in this study for the retrieval of the UVR diffuse attenuation coefficient Kd(λ) from remote sensing reflectance Rrs(λ) in the global ocean. KPCA can be applied in all areas that principal components analysis (PCA) can be used. More importantly, KPCA can help mapping data into high dimensions and reducing the nonlinearity between inputs and outputs, which will improve the performance and robustness of algorithms when deriving large dynamic ranges parameters. Compared with SeaUVc, which is one of the most successful Kd(λ) retrieval algorithms in UVR, the results showed that KpcaUV (with R2 : 0.970 and RMSE: 14.0%) performed similar to SeaUVc (with R2 : 0.963 and RMSE: 15.6%) when implemented with high-quality data. Nevertheless, KpcaUV was more robust and consistent than SeaUVc when implemented on the satellite images with different levels of quality control. The RMSD of SeaUVc had a significant reduction from 26.8% (QA ≥ 0.6) to 12.7% (QA = 1.0), and the RMSD of KpcaUV varied less than SeaUVc from 14.6% (QA ≥ 0.6) to 10.1% (QA = 1). Hence, considering its good nonlinear-problem-solving ability and robustness when applied to multiple satellites, KpcaUV proposed by this study can be used to obtain Kd(380) for the continuous observation of the large area. Numéro de notice : A2021-421 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3020294 Date de publication en ligne : 16/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3020294 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97773 [article]