Search results for " data"

showing 10 items of 7516 documents

Mapping Vegetation Density in a Heterogeneous River Floodplain Ecosystem Using Pointable CHRIS/PROBA Data

2012

River floodplains in the Netherlands serve as water storage areas, while they also have the function of nature rehabilitation areas. Floodplain vegetation is therefore subject to natural processes of vegetation succession. At the same time, vegetation encroachment obstructs the water flow into the floodplains and increases the flood risk for the hinterland. Spaceborne pointable imaging spectroscopy has the potential to quantify vegetation density on the basis of leaf area index (LAI) from a desired view zenith angle. In this respect, hyperspectral pointable CHRIS data were linked to the ray tracing canopy reflectance model FLIGHT to retrieve vegetation density estimates over a heterogeneous…

010504 meteorology & atmospheric sciencesFloodplainWater flowpointable sensors; CHRIS/PROBA; leaf area index (LAI); inversion; radiative transfer (RT) model; FLIGHT; river floodplain ecosystem; vegetation density; hydraulic roughnessleaf area index (LAI)0211 other engineering and technologiesClimate change02 engineering and technologyCHRIS/PROBA01 natural sciencesforestinversionLaboratory of Geo-information Science and Remote SensingLaboratorium voor Geo-informatiekunde en Remote SensingLeaf area indexcoverlcsh:ScienceZenithriver floodplain ecosystem021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensinggeographychris-proba datahyperspectral brdf datageography.geographical_feature_categoryFLIGHTFlood mythrhine basinradiative-transfer modelHyperspectral imagingEnhanced vegetation index15. Life on landpointable sensorsPE&RCradiative transfer (RT) modelsugar-beetclimate-changeGeneral Earth and Planetary SciencesEnvironmental sciencehydraulic roughnesslcsh:Qflow resistanceleaf-area indexvegetation densityRemote 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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Applications of a new set of methane line parameters to the modeling of Titan's spectrum in the 1.58 μm window

2012

International audience; In this paper we apply a recently released set of methane line parameters (Wang et al., 2011) to the modeling of Titan spectra in the 1.58 mu m window at both low and high spectral resolution. We first compare the methane absorption based on this new set of methane data to that calculated from the methane absorption coefficients derived in situ from DISR/Huygens (Tomasko et al., 2008a; Karkoschka and Tomasko, 2010) and from the band models of Irwin et al. (2006) and Karkoschka and Tomasko (2010). The Irwin et al. (2006) band model clearly underestimates the absorption in the window at temperature-pressure conditions representative of Titan's troposphere, while the Ka…

010504 meteorology & atmospheric sciencesInfraredCASSINI VIMSHUYGENS PROBEMONODEUTERATED METHANEAtmospheric sciences01 natural sciences7. Clean energyMethaneSpectral lineTropospherechemistry.chemical_compoundsymbols.namesake0103 physical sciencesSpectral resolutionSpectroscopy010303 astronomy & astrophysicsCLOUD STRUCTURE0105 earth and related environmental sciencesPhysics[PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph][PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics][ PHYS.PHYS.PHYS-OPTICS ] Physics [physics]/Physics [physics]/Optics [physics.optics]Astronomy and Astrophysics9500 CM(-1)SPECTROSCOPIC DATABASEM TRANSPARENCY WINDOWComputational physicsAerosolchemistry[ PHYS.PHYS.PHYS-AO-PH ] Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]TEMPERATURE-DEPENDENCE13. Climate actionSpace and Planetary SciencesymbolsSHIFT COEFFICIENTSOUTER SOLAR-SYSTEMTitan (rocket family)
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Top-of-Atmosphere Retrieval of Multiple Crop Traits Using Variational Heteroscedastic Gaussian Processes within a Hybrid Workflow.

2021

In support of cropland monitoring, operational Copernicus Sentinel-2 (S2) data became available globally and can be explored for the retrieval of important crop traits. Based on a hybrid workflow, retrieval models for six essential biochemical and biophysical crop traits were developed for both S2 bottom-of-atmosphere (BOA) L2A and S2 top-of-atmosphere (TOA) L1C data. A variational heteroscedastic Gaussian process regression (VHGPR) algorithm was trained with simulations generated by the combined leaf-canopy reflectance model PROSAILat the BOA scale and further combined with the Second Simulation of a Satellite Signal in the Solar Spectrum (6SV) atmosphere model at the TOA scale. Establishe…

010504 meteorology & atmospheric sciencesMean squared errorScienceReference data (financial markets)MathematicsofComputing_GENERAL0211 other engineering and technologieshybrid model02 engineering and technologyAtmospheric model01 natural sciencessymbols.namesaketop-of-atmosphere reflectanceKrigingLeaf area indexGaussian process021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensing2. Zero hungerQbiophysical and biochemical traits; top-of-atmosphere reflectance; Sentinel-2; variational heteroscedastic Gaussian process regression; hybrid modelvariational heteroscedastic Gaussian process regressionVegetation15. Life on landsymbolsGeneral Earth and Planetary Sciencesbiophysical and biochemical traitsSentinel-2Scale (map)Remote 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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A multisensor fusion approach to improve LAI time series

2011

International audience; High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. …

010504 meteorology & atmospheric sciencesMeteorologytélédétectionsatellite0211 other engineering and technologiesSoil Scienceréseau neuronal02 engineering and technology01 natural sciencessuivi de culturesInstrumentation (computer programming)Computers in Earth SciencesLeaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingVegetationGeologyVegetationData fusionLAI time seriesSensor fusionMissing dataLAI time series;Vegetation;Modis;Temporal smoothing;Gap filling;Data fusionqualité des données13. Climate actionAutre (Sciences de l'ingénieur)Gap filling[SDE]Environmental SciencesEnvironmental scienceSatelliteModisTemporal smoothingScale (map)Smoothing
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Inflight Radiometric Calibration of New Horizons' Multispectral Visible Imaging Camera (MVIC)

2017

© 2016 Elsevier Inc. We discuss two semi-independent calibration techniques used to determine the inflight radiometric calibration for the New Horizons’ Multi-spectral Visible Imaging Camera (MVIC). The first calibration technique compares the measured number of counts (DN) observed from a number of well calibrated stars to those predicted using the component-level calibration. The ratio of these values provides a multiplicative factor that allows a conversation between the preflight calibration to the more accurate inflight one, for each detector. The second calibration technique is a channel-wise relative radiometric calibration for MVIC's blue, near-infrared and methane color channels us…

010504 meteorology & atmospheric sciencesMultispectral imageFOS: Physical sciencesField of view01 natural sciencesOptics0103 physical sciencesCalibration010303 astronomy & astrophysicsRadiometric calibrationInstrumentation and Methods for Astrophysics (astro-ph.IM)0105 earth and related environmental sciencesRemote sensingEarth and Planetary Astrophysics (astro-ph.EP)Pixelbusiness.industryDetectorAstrophysics::Instrumentation and Methods for AstrophysicsAstronomy and AstrophysicsPlanetary Data SystemPanchromatic filmSpace and Planetary ScienceEnvironmental scienceAstrophysics::Earth and Planetary AstrophysicsAstrophysics - Instrumentation and Methods for AstrophysicsbusinessAstrophysics - Earth and Planetary Astrophysics
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ARES. III. Unveiling the Two Faces of KELT-7 b with HST WFC3

2020

We present the analysis of the hot-Jupiter KELT-7b using transmission and emission spectroscopy from the Hubble Space Telescope (HST), both taken with the Wide Field Camera 3 (WFC3). Our study uncovers a rich transmission spectrum which is consistent with a cloud-free atmosphere and suggests the presence of H2O and H-. In contrast, the extracted emission spectrum does not contain strong absorption features and, although it is not consistent with a simple blackbody, it can be explained by a varying temperature-pressure profile, collision induced absorption (CIA) and H-. KELT-7 b had also been studied with other space-based instruments and we explore the effects of introducing these additiona…

010504 meteorology & atmospheric sciencesOpacityFOS: Physical sciencesEFFICIENTTransmission spectroscopy; Exoplanet atmospheres; Astronomy data analysisAstrophysics::Cosmology and Extragalactic AstrophysicsAstrophysicsAstronomy & Astrophysics01 natural sciencesAtmosphereHubble space telescope0103 physical sciencesTransmission spectroscopyEMISSION-SPECTRUMWATERBlack-body radiationEmission spectrumAbsorption (electromagnetic radiation)010303 astronomy & astrophysicsAstrophysics::Galaxy Astrophysics0105 earth and related environmental sciencesEarth and Planetary Astrophysics (astro-ph.EP)PhysicsScience & TechnologyHOT JUPITERSAstronomy and AstrophysicsBIASESEXOPLANETSTransmission (telecommunications)13. Climate actionSpace and Planetary SciencePhysical SciencesAstronomy data analysisHD 209458BAstrophysics::Earth and Planetary AstrophysicsATMOSPHERESWide Field Camera 3Astrophysics - Earth and Planetary AstrophysicsExoplanet atmospheresThe Astronomical Journal
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Evaluation of deep learning algorithms for national scale landslide susceptibility mapping of Iran

2021

The identification of landslide-prone areas is an essential step in landslide hazard assessment and mitigation of landslide-related losses. In this study, we applied two novel deep learning algorithms, the recurrent neural network (RNN) and convolutional neural network (CNN), for national-scale landslide susceptibility mapping of Iran. We prepared a dataset comprising 4069 historical landslide locations and 11 conditioning factors (altitude, slope degree, profile curvature, distance to river, aspect, plan curvature, distance to road, distance to fault, rainfall, geology and land-sue) to construct a geospatial database and divided the data into the training and the testing dataset. We then d…

010504 meteorology & atmospheric sciencesReceiver operating characteristicbusiness.industryDeep learningSpatial databaselcsh:QE1-996.5Deep learningLandslideIranLandslide susceptibility010502 geochemistry & geophysicsRNN01 natural sciencesConvolutional neural networklcsh:GeologyLandslideRecurrent neural networkGeneral Earth and Planetary SciencesArtificial intelligenceScale (map)businessAlgorithmCNNGeology0105 earth and related environmental sciencesGeoscience Frontiers
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