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dc.contributor.authorDelalieux, Stephanie
dc.contributor.authorZarco-Tejada, Pablo J.
dc.contributor.authorTits, Laurent
dc.contributor.authorJimenez Bello, Miguel Angel
dc.contributor.authorIntrigliolo, Diego S.
dc.contributor.authorSomers, Ben
dc.date.accessioned2017-06-01T10:11:44Z
dc.date.available2017-06-01T10:11:44Z
dc.date.issued2014
dc.identifier.citationDelalieux, Stephanie, Zarco-Tejada, P. J., Tits, Laurent, Jimenez Bello, Miguel Angel, Intrigliolo, Diego S., Somers, Ben (2014). Unmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress. Ieee Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 7(6), 2571-2582.
dc.identifier.issn1939-1404
dc.identifier.urihttp://hdl.handle.net/20.500.11939/5119
dc.description.abstractMany applications require a timely acquisition of high spatial and spectral resolution remote sensing data. This is often not achievable since spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while airborne sensors mounted on a manned aircraft are too expensive to acquire a high temporal resolution. This gap between information needs and data availability inspires research on using Remotely Piloted Aircraft Systems (RPAS) to capture the desired high spectral and spatial information, furthermore providing temporal flexibility. Present hyperspectral imagers on board lightweight RPAS are still rare, due to the operational complexity, sensor weight, and instability. This paper looks into the use of a hyperspectral-hyperspatial fusion technique for an improved biophysical parameter retrieval and physiological assessment in agricultural crops. First, a biophysical parameter extraction study is performed on a simulated citrus orchard. Subsequently, the unmixing-based fusion is applied on a real test case in commercial citrus orchards with discontinuous canopies, in which a more efficient and accurate estimation of water stress is achieved by fusing thermal hyperspatial and hyperspectral (APEX) imagery. Narrowband reflectance indices that have proven their effectiveness as previsual indicators of water stress, such as the Photochemical Reflectance Index (PRI), show a significant increase in tree water-stress detection when applied on the fused dataset compared to the original hyperspectral APEX dataset (R-2 = 0.62, p 0.1). Maximal R-2 values of 0.93 and 0.86 are obtained by a linear relationship between the vegetation index and the resp., water and chlorophyll, parameter content maps.
dc.language.isoen
dc.titleUnmixing-Based Fusion of Hyperspatial and Hyperspectral Airborne Imagery for Early Detection of Vegetation Stress
dc.typearticle
dc.authorAddressInstituto Valenciano de Investigaciones Agrarias (IVIA), Carretera CV-315, Km. 10’7, 46113 Moncada (Valencia), Españaes
dc.date.issuedFreeFormJUN
dc.entidadIVIACentro para el Desarrollo de la Agricultura Sostenible
dc.entidadIVIAServicio de Tecnología del Riego
dc.identifier.doi10.1109/JSTARS.2014.2330352
dc.journal.issueNumber6
dc.journal.titleIeee Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.journal.volumeNumber7
dc.page.final2582
dc.page.initial2571
dc.rights.accessRightsopenAccess
dc.source.typeImpreso
dc.type.hasVersionacceptedVersion


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