Search results for " OPT"

showing 10 items of 13620 documents

Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

2019

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …

010504 meteorology & atmospheric sciencesFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTRE0208 environmental biotechnologySoil ScienceReview02 engineering and technologyPhotochemical Reflectance Index01 natural sciencesArticleGEO/11 - GEOFISICA APPLICATASIF retrieval methodsRadiative transfer modellingRadiative transfer910 Geography & travelComputers in Earth SciencesChlorophyll fluorescence1111 Soil Science1907 GeologyAirborne instruments0105 earth and related environmental sciencesRemote sensingStress detectionGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERA1903 Computers in Earth SciencesPrimary productionGeologyVegetationPassive optical techniquesField (geography)020801 environmental engineeringGEO/10 - GEOFISICA DELLA TERRA SOLIDA10122 Institute of GeographySun-induced fluorescenceRemote sensing (archaeology)Sun-induced fluorescence Steady-state photosynthesis Stress detection Radiative transfer modelling SIF retrieval methods. Satellite sensors Airborne instruments Applications Terrestrial vegetation Passive optical techniques. ReviewApplicationsTerrestrial vegetationEnvironmental scienceSatelliteSteady-state photosynthesisSatellite sensors
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Spectral alignment of multi-temporal cross-sensor images with automated kernel canonical correlation analysis

2015

In this paper we present an approach to perform relative spectral alignment between optical cross-sensor acquisitions. The proposed method aims at projecting the images from two different and possibly disjoint input spaces into a common latent space, in which standard change detection algorithms can be applied. The system relies on the regularized kernel canonical correlation analysis transformation (kCCA), which can accommodate nonlinear dependencies between pixels by means of kernel functions. To learn the projections, the method employs a subset of samples belonging to the unchanged areas or to uninteresting radiometric differences. Since the availability of ground truth information to p…

010504 meteorology & atmospheric sciencesFeature extraction0211 other engineering and technologiesRelative spectral alignment02 engineering and technology3107 Atomic and Molecular Physics and Optics01 natural sciencesCross-sensorCanonical correlation analysis1706 Computer Science Applications910 Geography & travelComputers in Earth SciencesEngineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsGround truthbusiness.industry1903 Computers in Earth SciencesKernel methodsPattern recognitionReal imageAtomic and Molecular Physics and OpticsComputer Science Applications10122 Institute of GeographyTransformation (function)Kernel methodChange detectionFeature extraction2201 Engineering (miscellaneous)Artificial intelligencebusinessCanonical correlationChange detectionCurse of dimensionalityISPRS Journal of Photogrammetry and Remote Sensing
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GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment

2019

Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. …

010504 meteorology & atmospheric sciencesGeoneutrinogeophysical uncertaintieInverse transform samplingFOS: Physical sciences01 natural sciencesBayesian methodUpper middle and lower crustStandard deviationNOSouth China BlockmiddlePhysics - GeophysicsMonte Carlo stochastic optimizationGOCE data gravimetric inversionGeophysical uncertaintiesGeochemistry and PetrologyEarth and Planetary Sciences (miscellaneous)Bayesian method; geophysical uncertainties; GOCE data gravimetric inversion; Monte Carlo stochastic optimization; South China Block; upper middle and lower crustImage resolution0105 earth and related environmental sciencesSubdivisionJiangmen Underground Neutrino Observatoryupper and middle and lower crustbusiness.industrySettore FIS/01 - Fisica SperimentaleCrustupperGeodesy[PHYS.PHYS.PHYS-GEN-PH]Physics [physics]/Physics [physics]/General Physics [physics.gen-ph]Geophysics (physics.geo-ph)and lower crustDepth soundingGeophysics13. Climate actionSpace and Planetary SciencebusinessGeologyBayesian method geophysical uncertainties GOCE data gravimetric inversion Monte Carlo stochastic optimization South China Blockupper and middle and lower crust
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Global modeling of the lower three polyads of PH_{3} Preliminary results

2009

International audience; In order to model the high-resolution infrared spectrum of the phosphine molecule in the 3 mu m region, a global approach involving the lower three polyads of the molecule (Dyad, Pentad and Octad) as been applied using an effective hamiltonian in the form of irreducible tensors. This model allowed to describe all the 15 vibrational states involved and to consider explicitly all relevant ro-vibrational interactions that cannot be accounted for by conventional perturbation approaches. 2245 levels (up to J=14) observed through transitions arising from 34 cold and hot bands including all available existing data as well as new experimental data have been fitted simultaneo…

010504 meteorology & atmospheric sciencesInfraredNear infraredPositionsHigh resolutionPerturbation (astronomy)01 natural sciencessymbols.namesakeGlobal modeling0103 physical sciencesMoleculePH_{3}Physical and Theoretical Chemistry33.20.Ea 33.20.VqSpectroscopy0105 earth and related environmental sciences[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]Physics010304 chemical physicsNear-infrared spectroscopyAtomic and Molecular Physics and OpticsDipole[ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]IntensitiessymbolsVibrational polyadsAtomic physicsHigh-resolutionHamiltonian (quantum mechanics)Global modelingPhosphine
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Radiometric calibration of SeaWIFS in the near infrared

2005

The radiometric calibration of the Sea-Viewing Wide-Field-of-View Sensor (SeaWiFS) in the near infrared (band 8, centered on 865 nm) is evaluated by use of ground-based radiometer measurements of solar extinction and sky radiance in the Sun's principal plane at two sites, one located 13 km off Venice, Italy, and the other on the west coast of Lanai Island, Hawaii. The aerosol optical thickness determined from solar extinction is used in an iterative scheme to retrieve the pseudo aerosol phase function, i.e., the product of single-scattering albedo and phase function, in which sky radiance is corrected for multiple scattering effects. No assumption about the aerosol model is required. The ae…

010504 meteorology & atmospheric sciencesMaterials Science (miscellaneous)Solar zenith angle01 natural sciencesIndustrial and Manufacturing Engineering010309 optics[SDU.STU.CL] Sciences of the Universe [physics]/Earth Sciences/ClimatologyOptics0103 physical sciences14. Life underwaterBusiness and International ManagementRadiometric calibration0105 earth and related environmental sciencesRemote sensingRadiometerbusiness.industryAtmospheric correctionAerosolSeaWiFS13. Climate action[SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/ClimatologyInfrared windowRadianceEnvironmental science[ SDU.STU.CL ] Sciences of the Universe [physics]/Earth Sciences/Climatologybusiness
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Multi-Crop Green LAI Estimation with a New Simple Sentinel-2 LAI Index (SeLI)

2019

The spatial quantification of green leaf area index (LAIgreen), the total green photosynthetically active leaf area per ground area, is a crucial biophysical variable for agroecosystem monitoring. The Sentinel-2 mission is with (1) a temporal resolution lower than a week, (2) a spatial resolution of up to 10 m, and (3) narrow bands in the red and red-edge region, a highly promising mission for agricultural monitoring. The aim of this work is to define an easy implementable LAIgreen index for the Sentinel-2 mission. Two large and independent multi-crop datasets of in situ collected LAIgreen measurements were used. Commonly used LAIgreen indices applied on the Sentinel-2 10 m &times

010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesRed edge02 engineering and technologylcsh:Chemical technology01 natural sciencesBiochemistryArticleAnalytical Chemistryremote sensingred-edgelcsh:TP1-1185Sensitivity (control systems)Electrical and Electronic EngineeringLeaf area indexInstrumentationImage resolution021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerPixelleaf area indexVegetation15. Life on landcropsAtomic and Molecular Physics and OpticsTemporal resolutionvegetation indicesSentinel-2Sensors
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Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

2017

Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologies02 engineering and technologycomputer.software_genre01 natural sciencessymbols.namesakeLinear regressionComputers in Earth SciencesEngineering (miscellaneous)Gaussian processHyMap021101 geological & geomatics engineering0105 earth and related environmental sciencesData stream miningbusiness.industryDimensionality reductionHyperspectral imagingPattern recognitionAtomic and Molecular Physics and OpticsComputer Science ApplicationsKernel (statistics)symbolsData miningArtificial intelligencebusinesscomputerISPRS Journal of Photogrammetry and Remote Sensing
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Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data

2020

Abstract Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radi…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesAtmospheric correctionFOS: Physical sciences02 engineering and technology15. Life on land01 natural sciencesAtomic and Molecular Physics and OpticsArticleComputer Science ApplicationsPhysics - Atmospheric and Oceanic PhysicsAtmospheric radiative transfer codesKrigingAtmospheric and Oceanic Physics (physics.ao-ph)RadianceSatelliteComputers in Earth SciencesLeaf area indexScale (map)Engineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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Towards a long-term dataset of ELBARA-II measurements assisting SMOS level-3 land product and algorithm validation at the Valencia Anchor Station

2015

[EN] The Soil Moisture and Ocean Salinity (SMOS) mission was launched on 2nd November 2009 with the objective of providing global estimations of soil moisture and sea salinity. The main activity of the Valencia Anchor Station (VAS) is currently to assist in a long-term validation of SMOS land products. This study focus on a level 3 SMOS data validation with in situ measurements carried out in the period 2010-2012 over the VAS. ELBARA-II radiometer is placed in the VAS area, observing a vineyard field considered as representative of a major proportion of an area of 50×50 km, enough to cover a SMOS footprint. Brightness temperatures (TB) acquired by ELBARA-II have been compared to those obser…

010504 meteorology & atmospheric sciencesMeteorologyGeography Planning and Development0211 other engineering and technologiesData validationlcsh:G1-92202 engineering and technology01 natural sciencesVineyardSoil roughnessFootprintEarth and Planetary Sciences (miscellaneous)Vegetation optical depth14. Life underwaterPrecipitationWater content021101 geological & geomatics engineering0105 earth and related environmental sciencesRadiometerHumedad del suelobrightness temperature ELBARA-II L-MEB SMOS SMOS level 3 data soil moisture soil roughness Valencia Anchor Station vegetation optical depth15. Life on landEspesor óptico de la vegetaciónTerm (time)GeographyL-MEB13. Climate actionBrightness temperatureRugosidad del sueloTemperatura de brilloSoil moistureBrightness temperaturelcsh:Geography (General)
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The 2009 Edition of the GEISA Spectroscopic Database

2011

The updated 2009 edition of the spectroscopic database GEISA (Gestion et Etude des Informations Spectroscopiques Atmosphériques; Management and Study of Atmospheric Spectroscopic Information) is described in this paper. GEISA is a computer-accessible system comprising three independent sub-databases devoted, respectively, to: line parameters, infrared and ultraviolet/visible absorption cross-sections, microphysical and optical properties of atmospheric aerosols. In this edition, 50 molecules are involved in the line parameters sub-database, including 111 isotopologues, for a total of 3,807,997 entries, in the spectral range from 10-6 to 35,877.031cm-1.The successful performances of the new …

010504 meteorology & atmospheric sciencesMeteorologyTélédétectionPhysique atomique et moléculaireMolecular spectroscopyInfrared atmospheric sounding interferometercomputer.software_genre01 natural sciencesLine parametersAtmospheric radiative transfer0103 physical sciences010303 astronomy & astrophysicsSpectroscopy0105 earth and related environmental sciencesRemote sensingWeb site[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]RadiationSpectroscopic database[ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]DatabaseGEISAOptically activeAtmospheric aerosolsMolecular spectroscopyAtomic and Molecular Physics and Optics[CHIM.THEO]Chemical Sciences/Theoretical and/or physical chemistryOn boardSpectroscopie [électromagnétisme optique acoustique][ CHIM.THEO ] Chemical Sciences/Theoretical and/or physical chemistryEarth's and planetary atmospheresEnvironmental scienceAtmospheric absorptionAtmospheric absorptionCross-sectionscomputer
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