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A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration / Daeyong Jin in Remote sensing, vol 13 n°10 (May-2 2021)
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Titre : A deep learning model using satellite ocean color and hydrodynamic model to estimate chlorophyll-a concentration Type de document : Article/Communication Auteurs : Daeyong Jin, Auteur ; Eojin Lee, Auteur ; Kyonghwan Kwon, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2003 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] apprentissage profond
[Termes IGN] chlorophylle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] Corée du sud
[Termes IGN] distribution spatiale
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] hydrodynamique
[Termes IGN] image COMS-GOCIRésumé : (auteur) In this study, we used convolutional neural networks (CNNs)—which are well-known deep learning models suitable for image data processing—to estimate the temporal and spatial distribution of chlorophyll-a in a bay. The training data required the construction of a deep learning model acquired from the satellite ocean color and hydrodynamic model. Chlorophyll-a, total suspended sediment (TSS), visibility, and colored dissolved organic matter (CDOM) were extracted from the satellite ocean color data, and water level, currents, temperature, and salinity were generated from the hydrodynamic model. We developed CNN Model I—which estimates the concentration of chlorophyll-a using a 48 × 27 sized overall image—and CNN Model II—which uses a 7 × 7 segmented image. Because the CNN Model II conducts estimation using only data around the points of interest, the quantity of training data is more than 300 times larger than that of CNN Model I. Consequently, it was possible to extract and analyze the inherent patterns in the training data, improving the predictive ability of the deep learning model. The average root mean square error (RMSE), calculated by applying CNN Model II, was 0.191, and when the prediction was good, the coefficient of determination (R2) exceeded 0.91. Finally, we performed a sensitivity analysis, which revealed that CDOM is the most influential variable in estimating the spatiotemporal distribution of chlorophyll-a. Numéro de notice : A2021-417 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102003 Date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102003 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97759
in Remote sensing > vol 13 n°10 (May-2 2021) . - n° 2003[article]Monitoring suspended particle matter using GOCI satellite data after the Tohoku (Japan) tsunami in 2011 / Audrey Minghelli in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 2 (February 2019)
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Titre : Monitoring suspended particle matter using GOCI satellite data after the Tohoku (Japan) tsunami in 2011 Type de document : Article/Communication Auteurs : Audrey Minghelli, Auteur ; Manchun Lei , Auteur ; Sabine Charmasson, Auteur ; Vincent Rey, Auteur ; Malik Chami, Auteur
Année de publication : 2019 Projets : AMORAD / Radakovitch, Olivier Article en page(s) : pp 567 - 576 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] détection de changement
[Termes IGN] image COMS-GOCI
[Termes IGN] Japon
[Termes IGN] particule
[Termes IGN] risque naturel
[Termes IGN] risque technologique
[Termes IGN] séisme
[Termes IGN] Sendaï
[Termes IGN] surveillance géologique
[Termes IGN] Tohoku (Japon)
[Termes IGN] tsunamiRésumé : (auteur) The Fukushima Daiichi nuclear disaster that occurred on March 11, 2011, was caused by the To̅hoku tsunami, which was itself triggered by the devastating 9.0 Mw moment magnitude earthquake. This study investigates spatial and temporal changes of the suspended particulate matter (SPM) content in the North-Eastern part of Japan (Pacific Ocean) using a geostationary ocean color sensor. The Geostationary Ocean Color Imager (GOCI), which is centered on the Korean peninsula but could also observe the Japanese area, is able to acquire eight images per day, thus allowing the analysis of rapid daily changes in water mass. The analysis of GOCI data shows that SPM concentration notably increased both along the coast and within the Bay of Sendaï shortly after the tsunami. Motionless patterns of SPM were observed at 2, 14, 25, and 37 km from the coast. It is shown that SPM concentration rapidly decreased one month later. The SPM concentration did not remain high the following year, contrary to what was observed for the Sumatra Tsunami in 2004. The origin of SPM is also investigated in this study. Our analysis suggests that some of the SPM originates from the resuspension of bottom sediments due to the reflection of the tsunami on the coastline that leads to the migration of marine particles toward the sea surface. The fate of the SPM concentration is then discussed based on the analysis of meteorological conditions, river discharge, and tsunami wave properties. Numéro de notice : A2019-628 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/JSTARS.2019.2894063 Date de publication en ligne : 22/02/2019 En ligne : https://doi.org/10.1109/JSTARS.2019.2894063 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95365
in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing > vol 12 n° 2 (February 2019) . - pp 567 - 576[article]