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Hyperspectral analysis of soil polluted with four types of hydrocarbons / Laura A. Reséndez-Hernández in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 925 - 942 Titre : Hyperspectral analysis of soil polluted with four types of hydrocarbons Type de document : Article/Communication Auteurs : Laura A. Reséndez-Hernández, Auteur ; Daniel Prudencio-Csapek, Auteur ; Diego Fabian Lozano Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 925 - 942 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de mélange spectral d’extrémités multiples [Termes IGN] analyse spectrale [Termes IGN] classification Spectral angle mapper [Termes IGN] hydrocarbure [Termes IGN] pétrole [Termes IGN] pollution des sols [Termes IGN] réflectance spectrale [Termes IGN] spectroradiomètre Résumé : (auteur) In this study, a high spectral resolution GER-2600 spectroradiometer was used to obtain the spectral data of soil samples that were polluted with four different types of petroleum–hydrocarbons products: Diesel, Gasoline, Crude Oil and Fuel Oil. The polluted soil samples were prepared in the laboratory at five concentrations levels: unpolluted soil, 2500, 100,000, 250,000 ppm and pure pollutant. Spectral data were pre-processed and then analysed with various approaches: Principal Components Transformation and ANOVA, Spectral Angle Mapper (SAM), Hydrocarbon Index (HI) and Spectral Mixture Analysis (SMA). The results showed that it was possible to determine the different spectral response between clean soil and some of the polluted soils: crude oil at concentrations higher than 100,000 ppm were the easiest to recognize; while samples polluted with gasoline at concentrations below 250,000 ppm were the most difficult to distinguish from non-polluted samples. Numéro de notice : A2019-516 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1451921 Date de publication en ligne : 28/03/2019 En ligne : https://doi.org/10.1080/10106049.2018.1451921 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93870 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data / P. Kumar in Geocarto international, vol 34 n° 9 ([15/06/2019])  

Public [article]  inGeocarto international > vol 34 n° 9 [15/06/2019] . - pp 1022-1041 Titre : Comprehensive evaluation of soil moisture retrieval models under different crop cover types using C-band synthetic aperture radar data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; A. Choudhary, Auteur ; D. K. Gupta, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 1022-1041 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] bande C [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] couvert végétal [Termes IGN] échantillonnage d'image [Termes IGN] humidité du sol [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] image Sentinel-SAR [Termes IGN] modèle de régression [Termes IGN] polarisation [Termes IGN] réseau neuronal artificiel [Termes IGN] Uttar Pradesh (Inde ; état) Résumé : (auteur) In the present study, random forest regression (RFR), support vector regression (SVR) and artificial neural network regression (ANNR) models were evaluated for the retrieval of soil moisture covered by winter wheat, barley and corn crops. SVR with radial basis function kernel was provided the highest adj. R2 (0.95) value for soil moisture retrieval covered by the wheat crop at VV polarization. However, RFR provided the adj. R2 (0.94) value for soil moisture retrieval covered by barley crop at VV polarization using Sentinel-1A satellite data. The adj. R2 (0.94) values were found for the soil moisture covered by corn crop at VV polarization using RFR, SVR linear and radial basis function kernels. The least performance was reported using ANNR model for almost all the crops under investigation. The soil moisture retrieval outcomes were found better at VV polarization in comparison to VH polarization using three different models. Numéro de notice : A2019-517 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2018.1464601 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/10106049.2018.1464601 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93876 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2019091	RAB	Revue	Centre de documentation	En réserve L003	Disponible