Crop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategy

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dc.contributorSeñales, Sistemas y Telecomunicaciónes
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
dc.contributor.otherUniversidad de Alicante. Instituto Universitario de Investigación Informáticaes
dc.identifier.citationIEEE Geoscience and Remote Sensing Letters. 2014, 11(6): 1081-1085. doi:10.1109/LGRS.2013.2286214es
dc.identifier.issn1545-598X (Print)-
dc.identifier.issn1558-0571 (Online)-
dc.description.abstractIn this letter, a new approach for crop phenology estimation with remote sensing is presented. The proposed methodology is aimed to exploit tools from a dynamical system context. From a temporal sequence of images, a geometrical model is derived, which allows us to translate this temporal domain into the estimation problem. The evolution model in state space is obtained through dimensional reduction by a principal component analysis, defining the state variables, of the observations. Then, estimation is achieved by combining the generated model with actual samples in an optimal way using a Kalman filter. As a proof of concept, an example with results obtained with this approach over rice fields by exploiting stacks of TerraSAR-X dual polarization images is
dc.description.sponsorshipThis project was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) and in part by EU FEDER under Project
dc.rights© Copyright 2014 IEEEes
dc.subjectKalman filteres
dc.subjectSynthetic aperture radar (SAR)es
dc.subject.otherTeoría de la Señal y Comunicacioneses
dc.titleCrop Phenology Estimation Using a Multitemporal Model and a Kalman Filtering Strategyes
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