Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/57293
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dc.contributorSeñales, Sistemas y Telecomunicaciónes_ES
dc.contributor.authorDe Bernardis, Caleb G.-
dc.contributor.authorVicente-Guijalba, Fernando-
dc.contributor.authorMartínez Marín, Tomás-
dc.contributor.authorLopez-Sanchez, Juan M.-
dc.contributor.otherUniversidad de Alicante. Departamento de Física, Ingeniería de Sistemas y Teoría de la Señales_ES
dc.contributor.otherUniversidad de Alicante. Instituto Universitario de Investigación Informáticaes_ES
dc.date.accessioned2016-07-28T08:01:26Z-
dc.date.available2016-07-28T08:01:26Z-
dc.date.issued2016-07-20-
dc.identifier.citationDe Bernardis C, Vicente-Guijalba F, Martinez-Marin T, Lopez-Sanchez JM. Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images. Remote Sensing. 2016; 8(7):610. doi:10.3390/rs8070610es_ES
dc.identifier.issn2072-4292-
dc.identifier.urihttp://hdl.handle.net/10045/57293-
dc.description.abstractKnowing the current phenological state of an agricultural crop is a powerful tool for precision farming applications. In the past, it has been estimated with remote sensing data by exploiting time series of Normalised Difference Vegetation Index (NDVI), but always at the end of the campaign and only providing results for some key states. In this work, a new dynamical framework is proposed to provide real-time estimates in a continuous range of states, for which NDVI images are combined with a prediction model in an optimal way using a particle filter. The methodology is tested over a set of 8 to 13 rice parcels during 2008–2013, achieving a high determination factor R2=0.93 ( n=379 ) for the complete phenological range. This method is also used to predict the end of season date, obtaining a high accuracy with an anticipation of around 40–60 days. Among the key advantages of this approach, phenology is estimated each time a new observation is available, hence enabling the potential detection of anomalies in real-time during the cultivation. In addition, the estimation procedure is robust in the case of noisy observations, and it is not limited to a few phenological stages.es_ES
dc.description.sponsorshipThis work is supported by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P.es_ES
dc.languageenges_ES
dc.publisherMDPIes_ES
dc.rights© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/).es_ES
dc.subjectAgriculturees_ES
dc.subjectPhenologyes_ES
dc.subjectTime serieses_ES
dc.subjectNDVIes_ES
dc.subjectState space formulationes_ES
dc.subjectParticle filteres_ES
dc.subject.otherTeoría de la Señal y Comunicacioneses_ES
dc.titleParticle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Imageses_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.3390/rs8070610-
dc.relation.publisherversionhttp://dx.doi.org/10.3390/rs8070610es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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