0000000000726853

AUTHOR

Juan Pablo Rivera Caicedo

showing 2 related works from this author

SCOPE-Based Emulators for Fast Generation of Synthetic Canopy Reflectance and Sun-Induced Fluorescence Spectra

2017

Progress in advanced radiative transfer models (RTMs) led to an improved understanding of reflectance (R) and sun-induced chlorophyll fluorescence (SIF) emission throughout the leaf and canopy. Among advanced canopy RTMs that have been recently modified to deliver SIF spectral outputs are the energy balance model SCOPE and the 3D models DART and FLIGHT. The downside of these RTMs is that they are computationally expensive, which makes them impractical in routine processing, such as scene generation and retrieval applications. To bypass their computational burden, a computationally effective technique has been proposed by only using a limited number of model runs, called emulation. The idea …

spectroscopy010504 meteorology & atmospheric sciencesComputer sciencesun-induced fluorescence0211 other engineering and technologiesEnergy balanceemulation02 engineering and technology01 natural scienceschemistry.chemical_compoundradiative transfer modellingSCOPERadiative transferlcsh:Sciencescene generationChlorophyll fluorescence021101 geological & geomatics engineering0105 earth and related environmental sciencesEmulationArtificial neural networkFluorescencemachine learningLatin hypercube samplingchemistryChlorophyllGeneral Earth and Planetary Scienceslcsh:QAlgorithmRemote Sensing
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Approximating Empirical Surface Reflectance Data through Emulation: Opportunities for Synthetic Scene Generation

2019

Collection of spectroradiometric measurements with associated biophysical variables is an essential part of the development and validation of optical remote sensing vegetation products. However, their quality can only be assessed in the subsequent analysis, and often there is a need for collecting extra data, e.g., to fill in gaps. To generate empirical-like surface reflectance data of vegetated surfaces, we propose to exploit emulation, i.e., reconstruction of spectral measurements through statistical learning. We evaluated emulation against classical interpolation methods using an empirical field dataset with associated hyperspectral spaceborne CHRIS and airborne HyMap reflectance spectra…

Emulationspectroscopy010504 meteorology & atmospheric sciencesComputer scienceScienceQ0211 other engineering and technologiesHyperspectral imagingemulation02 engineering and technology01 natural sciencesReflectivityinterpolationData cubemachine learningscene simulationGeneral Earth and Planetary Sciencesemulation; machine learning; interpolation; spectroscopy; scene simulationSpectral resolutionSpectroscopyHyMap021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingInterpolationRemote Sensing
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