0000000001326877

AUTHOR

Francisco Javier García Haro

showing 3 related works from this author

Downstream Services for Rice Crop Monitoring in Europe: From Regional to Local Scale

2017

The ERMES agromonitoring system for rice cultivations integrates EO data at different resolutions, crop models, and user-provided in situ data in a unified system, which drives two operational downstream services for rice monitoring. The first is aimed at providing information concerning the behavior of the current season at regional/rice district scale, while the second is dedicated to provide farmers with field-scale data useful to support more efficient and environmentally friendly crop practices. In this contribution, we describe the main characteristics of the system, in terms of overall architecture, technological solutions adopted, characteristics of the developed products, and funct…

Atmospheric Sciencefood industryMonitoring010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesInformation Dissemination02 engineering and technology01 natural sciencesElectronic mailData modelingRemote SensingERMESremote sensingFood IndustryComputers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingDownstream (petroleum industry)agriculture2. Zero hungerData collectionEnd userbusiness.industryEnvironmental resource managementModelingAgriculturemodeling15. Life on landmonitoringAgriculturebusiness
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Proyecto de Innovación Docente PID19-1096780 Banco de preguntas para cuestionarios web

2020

Banco de preguntas el Proyecto de Innovación Docente PID19-1096780 (UV-SFPIE), "Desarrollo de cuestionarios web para la mejora de las habilidades de presentación de resultados científicos"

UNESCO::FÍSICA
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Gap Filling of Biophysical Parameter Time Series with Multi-Output Gaussian Processes

2018

In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophysical parameter variables under a gap filling setup. In particular, we focus on LAI and fAPAR over rice areas. We show how this problem cannot be solved with standard single-output (SO) GP models, and how the proposed MO-GP models are able to successfully predict these variables even in high missing data regimes, by implicitly performing an across-domain information transfer. CICYT TIN2015-64210-R In this work we evaluate multi-output (MO) Gaussian Process (GP) models based on the linear model of coregionalization (LMC) for estimation of biophy…

remote sensingTime seriesmachine learninggaussian processes:CIENCIAS DE LA TIERRA Y DEL ESPACIO [UNESCO]UNESCO::CIENCIAS DE LA TIERRA Y DEL ESPACIO
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