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Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network / Walaiporn Phonphan in Geocarto international, vol 29 n° 7 - 8 (November - December 2014)  

Public [article]  inGeocarto international > vol 29 n° 7 - 8 (November - December 2014) . - pp 842 - 859 Titre : Modelling electrical conductivity of soil from backscattering coefficient of microwave remotely sensed data using artificial neural network Type de document : Article/Communication Auteurs : Walaiporn Phonphan, Auteur ; Nitin Kumar Tripathi, Auteur ; Taravudh Tipdecho, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 842 - 859 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] image ALOS-PALSAR [Termes IGN] micro-onde [Termes IGN] rétrodiffusion [Termes IGN] salinité Résumé : (Auteur) Soil salinity is one of the main agricultural problems which expand to larger areas. Soil scientists categorize salinity level by electrical conductivity (EC) measurement. However, field measurements of EC require extensive time, cost and experiences. Remote sensing is one suitable option to investigate and collect spatial data in larger areas. Many researches estimated soil moisture through microwave, but there are fewer studies which mentioned about direct relationship between EC and backscattering coefficient (BC). Thus, this study aims to propose the estimation of EC directly from BC of microwave. The relationship between EC obtained from field survey and BC from microwave is non-linear, artificial neural network (ANN) is one technique proposed in this study to figure out EC and BC relationship. ANN uses multilayer of interconnected processing resulting in EC value with high accuracy which is acceptable. For this reason, ANN model can be successfully utilized as an effective tool for EC estimation from microwave. Numéro de notice : A2014-469 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2013.868040 En ligne : https://doi.org/10.1080/10106049.2013.868040 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74046 [article]

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