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Development of an earth observation processing chain for crop biophysical parameters at local and global scale

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Development of an earth observation processing chain for crop biophysical parameters at local and global scale

dc.contributor.advisor	García-Haro, Francisco Javier
dc.contributor.advisor	Camps-Valls, Gustau
dc.contributor.author	Campos Taberner, Manuel
dc.contributor.other	Departament de Física de la Terra i Termodinàmica	es_ES
dc.date.accessioned	2017-06-23T10:02:50Z
dc.date.available	2017-06-23T10:02:50Z
dc.date.issued	2017	es_ES
dc.date.submitted	17-07-2017	es_ES
dc.identifier.uri	http://hdl.handle.net/10550/59170
dc.description.abstract	This thesis’ topics embrace remote sensing for Earth observation, specifically in Earth vegetation monitoring. The Thesis’ main objective is to develop and implement an operational processing chain for crop biophysical parameters estimation at both local and global scales from remote sensing data. Conceptually, the components of the chain are the same at both scales: First, a radiative transfer model is run in forward mode to build a database composed by simulations of vegetation surface reflectance and concomitant biophysical parameters associated to those spectrum. Secondly, the simulated database is used for training and testing nonlinear and non-parametric machine learning regression algorithms. The best model in terms of accuracy, bias and goodness-of-fit is then selected to be used in the operational retrieval chain. Once the model is trained, remote sensing surface reflectance data is fed into the trained model as input in the inversion process to retrieve the biophysical parameters of interest at both local and global scales depending on the inputs spatial resolution and coverage. Eventually, the validation of the leaf area index estimates is performed at local scale by a set of ground measurements conducted during coordinated field campaigns in three countries during 2015 and 2016 European rice seasons. At global scale, the validation is performed through intercomparison with the most relevant and widely validated reference biophysical products. The work elaborated in this Thesis is structured in six chapters including an introduction of remote sensing for Earth observation, the developed processing chain at local scale, the ground LAI measurements acquired with smartphones, the developed chain at global scale, a chapter discussing the conclusions of the work, and a chapter which includes an extended abstract in Valencian. The Thesis is completed by an annex which include a compendium of peer-reviewed publications in remote sensing international journals.	es_ES
dc.format.extent	180 p.	es_ES
dc.language.iso	en	es_ES
dc.subject	Biophysical parameters	es_ES
dc.subject	Vegetation	es_ES
dc.subject	Earth	es_ES
dc.subject	Machine learning	es_ES
dc.subject	Remote sensing	es_ES
dc.title	Development of an earth observation processing chain for crop biophysical parameters at local and global scale	es_ES
dc.type	doctoral thesis	es_ES
dc.subject.unesco	UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO::Geología::Teledetección (geología)	es_ES
dc.subject.unesco	UNESCO::CIENCIAS TECNOLÓGICAS::Tecnología del espacio ::Satélites artificiales	es_ES
dc.embargo.terms	3 months	es_ES