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Weights based decision level data fusion of landsat-8 and sentinel-1 for soil moisture content estimation

Yahia, Oualid, Guida, Raffaella and Iervolino, Pasquale (2019) Weights based decision level data fusion of landsat-8 and sentinel-1 for soil moisture content estimation In: The International Geoscience and Remote Sensing Symposium (IGARSS 2018), the 38th annual symposium of the IEEE Geoscience and Remote Sensing Society (GRSS), 22-27 Jul 2018, Valencia, Spain.

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Abstract

A novel decision level data fusion algorithm for soil moisture content estimation is proposed in this paper. Firstly, individual estimations are determined, respectively, from the inversion of the Integral Equation Model (IEM) for Sentinel-1 and from the Temperature Vegetation Dryness Index (TVDI) for LANDSAT-8. Then, a feature level fusion of these methods is performed using an Artificial Neural Network (ANN). Finally, all estimations including the feature level fusion estimation are fused at the decision level using a novel weights based estimation. The area of interest for this study is Blackwell Farms, Guildford, United Kingdom and datasets were taken on 17/11/2017 for both Landsat-8 and Sentinel-1. Estimation from the proposed decision level fusion method produces a Root Mean Square Error RMSE (1.090%) which is lower than RMSE of the individual estimations of each sensor as well as that of the feature level fusion estimation.

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 : 2019
DOI : 10.1109/IGARSS.2018.8518027
Copyright Disclaimer : © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords : Data fusion; Weights based; Integral equation model; Temperature vegetation dryness index; Artificial Neural Network
Related URLs :
Depositing User : Clive Harris
Date Deposited : 02 Aug 2018 08:06
Last Modified : 08 Aug 2019 13:30
URI: http://epubs.surrey.ac.uk/id/eprint/848828

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