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Sentinel-1 and Landsat-8 feature level fusion for soil moisture content estimation

Yahia, Oualid, Guida, Raffaella and Iervolino, Pasquale (2018) Sentinel-1 and Landsat-8 feature level fusion for soil moisture content estimation In: 12th European Conference on Synthetic Aperture Radar (EUSAR 2018), 04-06 Jun 2018, Aachen, Germany.

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Abstract

A novel methodology is proposed for soil moisture content (SMC) estimation using the feature level fusion of Senti-nel-1 and Landsat-8 satellite datasets. This fusion consists of concatenating Temperature Vegetation Dryness Index (TVDI) to the feature vector (radar and physical features) of the inversion of the Integral Equation Model (IEM) through Artificial Neural Networks (ANN) to reduce vegetation effects on Sentinel-1 estimation. This methodology is applied on Blackwell farms, Guildford, United Kingdom, where ground truth and satellite data were collected dur-ing 2017. The preliminary SMC estimation results show lower RMSE errors (by 0.474%) and less bias than the IEM inversion method.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering
Authors :
NameEmailORCID
Yahia, Oualido.yahia@surrey.ac.uk
Guida, RaffaellaR.Guida@surrey.ac.uk
Iervolino, Pasqualep.iervolino@surrey.ac.uk
Date : 8 June 2018
Related URLs :
Depositing User : Clive Harris
Date Deposited : 20 Jun 2018 07:41
Last Modified : 20 Jun 2018 07:41
URI: http://epubs.surrey.ac.uk/id/eprint/847076

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