Search results for "Sens"

showing 10 items of 13828 documents

Validation of the Sentinel-3 Ocean and Land Colour Instrument (OLCI) Terrestrial Chlorophyll Index (OTCI): Synergetic Exploitation of the Sentinel-2 …

2018

Continuity to the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) will be provided by the Sentinel-3 Ocean and Land Colour Instrument (OLCI), and to ensure its utility in a wide range of operational applications, validation efforts are required. In the past, these activities have been constrained by the need for costly airborne hyperspectral data acquisition, but the Sentinel-2 Multispectral Instrument (MSI) now offers a promising alternative. In this paper, we explore the synergetic use of Sentinel-2 MSI data for validation of the Sentinel-3 OLCI Terrestrial Chlorophyll Index (OTCI) over the Valencia Anchor Station, a large agricultural site in the Valen…

010504 meteorology & atmospheric sciencesAgricultural siteMultispectral image0211 other engineering and technologiesImaging spectrometerHyperspectral imaging02 engineering and technology01 natural sciencesValencian communityMedium resolutionChlorophyll indexData acquisitionEnvironmental science021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingIGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium
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Machine learning regression algorithms for biophysical parameter retrieval: Opportunities for Sentinel-2 and -3

2012

Abstract ESA's upcoming satellites Sentinel-2 (S2) and Sentinel-3 (S3) aim to ensure continuity for Landsat 5/7, SPOT-5, SPOT-Vegetation and Envisat MERIS observations by providing superspectral images of high spatial and temporal resolution. S2 and S3 will deliver near real-time operational products with a high accuracy for land monitoring. This unprecedented data availability leads to an urgent need for developing robust and accurate retrieval methods. Machine learning regression algorithms may be powerful candidates for the estimation of biophysical parameters from satellite reflectance measurements because of their ability to perform adaptive, nonlinear data fitting. By using data from …

010504 meteorology & atmospheric sciencesArtificial neural networkMean squared errorbusiness.industryComputer science0211 other engineering and technologiesSoil ScienceGeology02 engineering and technologyMachine learningcomputer.software_genre01 natural sciencesRegressionSupport vector machineTemporal resolutionGround-penetrating radarCurve fittingArtificial intelligenceComputers in Earth SciencesbusinessImage resolutioncomputer021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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A Search for Ultra-high-energy Neutrinos from TXS 0506+056 Using the Pierre Auger Observatory

2020

Results of a search for ultra-high-energy neutrinos with the Pierre Auger Observatory from the direction of the blazar TXS 0506+056 are presented. They were obtained as part of the follow-up that stemmed from the detection of high-energy neutrinos and gamma rays with IceCube, Fermi-LAT, MAGIC, and other detectors of electromagnetic radiation in several bands. The Pierre Auger Observatory is sensitive to neutrinos in the energy range from 100 PeV to 100 EeV and in the zenith-angle range from θ = 60° to θ = 95°, where the zenith angle is measured from the vertical direction. No neutrinos from the direction of TXS 0506+056 have been found. The results were analyzed in three periods: One of 6 m…

010504 meteorology & atmospheric sciencesAstronomyAstrophysicspower spectrum7. Clean energy01 natural sciencesIceCubeObservatoryMAGIC (telescope)UHE Cosmic Rays010303 astronomy & astrophysicsHigh energy astrophysics Neutrino astrony Blazars Transient sources Active galaxiesHigh Energy Astrophysical Phenomena (astro-ph.HE)Physicsastro-ph.HEOBSERVATÓRIOSAstrophysics::Instrumentation and Methods for Astrophysicsneutrino: UHEUHE [neutrino]AugerobservatoryHigh energy astrophysics; Neutrino astronomy; Blazars; Transient sources; Active galaxiesNeutrino detectorNeutrino astronomyNeutrinoAstrophysics - High Energy Astrophysical PhenomenaHigh energy astrophysicsradiation: electromagneticHigh-energy astronomyAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesGLASTblazar0103 physical sciencesNeutrinoHigh Energy PhysicsZenithAstrophysique0105 earth and related environmental sciencesPierre Auger ObservatoryFísicaAstronomy and AstrophysicsAstronomiesensitivityMAGICTransient sourcesSciences de l'espaceelectromagnetic [radiation]13. Climate actionSpace and Planetary Sciencegamma rayExperimental High Energy PhysicsActive galaxiesddc:520spectralNeutrino astronomy[PHYS.ASTR]Physics [physics]/Astrophysics [astro-ph]Blazars
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A 3-Year Sample of Almost 1,600 Elves Recorded Above South America by the Pierre Auger Cosmic-Ray Observatory

2020

The time and location of the 1,598 verified and reconstructed elves, used for the analysis showcased in this paper, are publicly available on the website of the Pierre Auger Observatory (https://www.auger.org/ index.php/science/data). We wish to thank the World Wide Lightning Location Network (http://wwlln.net), a collaboration among over 50 universities and institutions, for providing the lightning location data used in this paper. We acknowledge Robert Marshall for providing one of the most advanced elve simulations to the public, a key tool in understanding the elves observed by the Pierre Auger Observatory. The successful installation, commissioning, and operation of the Pierre Auger Ob…

010504 meteorology & atmospheric sciencesAstronomyField of view010502 geochemistry & geophysics01 natural sciences7. Clean energyAugerlcsh:QB1-991ObservatoryultravioletStormddc:550UHE Cosmic Raystime resolutionCosmic-ray observatoryPhysicslcsh:QE1-996.5astro-ph.GeologyAugerwidth [beam]IonosphereField of viewGeologylcsh:AstronomyUHE [cosmic radiation]Environmental Science (miscellaneous)horizonLightningddc:530High Energy PhysicsIonosphereCosmic-ray observatory0105 earth and related environmental sciencesfluorescence [detector]backgroundFísicaAstronomyStormsensitivityLightningopticslcsh:GeologyElves UV fluorescence detectorsThunderstorm13. Climate actionExperimental High Energy PhysicsnetworkThunderstormGeneral Earth and Planetary SciencesElvesObservatory
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Smap-based retrieval of vegetation opacity and albedo

2020

Over land the vegetation canopy affects the microwave brightness temperature by emission, scattering and attenuation of surface soil emission. The questions addressed in this study are: 1) what is the transparency of the vegetation canopy for different biomes around the Globe at the low-frequency L-band?, 2) what is the seasonal amplitude of vegetation microwave optical depth for different biomes?, 3) what is the effective scattering at this frequency for different vegetation types?, 4) what is the impact of imprecise characterization of vegetation microwave properties on retrieval of soil surface conditions? These questions are addressed based on the recently completed one full annual cycl…

010504 meteorology & atmospheric sciencesBiome0211 other engineering and technologiesFOS: Physical sciences02 engineering and technology15. Life on landAlbedoAnnual cycle01 natural sciencesGeophysics (physics.geo-ph)Physics - GeophysicsMicrowave imaging13. Climate actionBrightness temperaturemedicineEnvironmental sciencemedicine.symptomVegetation (pathology)Water contentOptical depth021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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Evaluating roughness effects on C-band AMSR-E observations

2014

International audience; The usefulness of microwave remote sensing to retrieve near-surface soil moisture has already been demonstrated in many studies. However, obtaining high quality estimates of soil moisture is influenced by many effects from soil, vegetation and atmosphere; one of the key parameters is surface roughness. This research focusses on a semi-empirical method to evaluate the roughness effects from space borne observations. Global maps of roughness effects are evaluated at C-band from AMSR-E measurements.

010504 meteorology & atmospheric sciencesC band[SDE.MCG]Environmental Sciences/Global Changes0211 other engineering and technologiessoil surface roughnessAMSR-E02 engineering and technologySurface finish01 natural sciences13. Climate actionEnvironmental sciencesoil moisture[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2014 IEEE Geoscience and Remote Sensing Symposium
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Vegetation vulnerability to drought in Spain

2014

[EN] Frequency of climatic extremes like long duration droughts has increased in Spain over the last century.The use of remote sensing observations for monitoring and detecting drought is justified on the basis that vegetation vigor is closely related to moisture condition. We derive satellite estimates of bio-physical variables such as fractional vegetation cover (FVC) from MODIS/EOS and SEVIRI/MSG time series. The study evaluates the strength of temporal relationships between precipitation and vegetation condition at time-lag and cumulative rainfall intervals. From this analysis, it was observed that the climatic disturbances affected both the growing season and the total amount of vegeta…

010504 meteorology & atmospheric sciencesClimateGeography Planning and Development0211 other engineering and technologiesSPIGrowing seasonlcsh:G1-92202 engineering and technology01 natural sciencesSequíaVegetation coverTropical vegetationEarth and Planetary Sciences (miscellaneous)medicineTeledetecciónPrecipitation021101 geological & geomatics engineering0105 earth and related environmental sciencesSequíasMoistureDroughtÍndices meteorológicos de sequíaVegetaciónVegetation cover15. Life on landRemote sensingVegetation dynamicsAridGeography13. Climate actionClimatologyClimamedicine.symptomVegetation (pathology)lcsh:Geography (General)
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Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud

2020

Remote sensing optical sensors onboard operational satellites cannot have high spectral, spatial and temporal resolutions simultaneously. In addition, clouds and aerosols can adversely affect the signal contaminating the land surface observations. We present a HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm to combine multispectral images of different sensors to reduce noise and produce monthly gap free high resolution (30 m) observations over land. Our approach uses images from the Landsat (30 m spatial resolution and 16 day revisit cycle) and the MODIS missions, both from Terra and Aqua platforms (500 m spatial resolution and daily revisit cycle). We implem…

010504 meteorology & atmospheric sciencesComputer science0208 environmental biotechnologyMultispectral imageSoil Science02 engineering and technology01 natural sciencesArticleComputers in Earth SciencesImage resolution0105 earth and related environmental sciencesRemote sensingPropagation of uncertaintyNoise (signal processing)GeologyKalman filterData fusionSensor fusion020801 environmental engineeringMODIS13. Climate actionScalabilityGap fillingKalman filterLandsatSmoothingSmoothingRemote Sensing of Environment
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Recent Advances in Techniques for Hyperspectral Image Processing

2009

International audience; Imaging spectroscopy, also known as hyperspectral imaging, has been transformed in less than thirty years from being a sparse research tool into a commodity product available to a broad user community. Currently, there is a need for standardized data processing techniques able to take into account the special properties of hyperspec- tral data. In this paper, we provide a seminal view on recent advances in techniques for hyperspectral image processing. Our main focus is on the design of techniques able to deal with the high-dimensional nature of the data, and to integrate the spa- tial and spectral information. Performance of the discussed techniques is evaluated in …

010504 meteorology & atmospheric sciencesComputer science0211 other engineering and technologiesSoil ScienceImage processing02 engineering and technologyMachine learningcomputer.software_genre01 natural sciences[INFO.INFO-TS]Computer Science [cs]/Signal and Image ProcessingComputer visionComputers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingData processingContextual image classificationbusiness.industryHyperspectral imagingGeologyImaging spectroscopyInformation extractionKernel methodSnapshot (computer storage)Artificial intelligencebusinesscomputer[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Edge-Based Missing Data Imputation in Large-Scale Environments

2021

Smart cities leverage large amounts of data acquired in the urban environment in the context of decision support tools. These tools enable monitoring the environment to improve the quality of services offered to citizens. The increasing diffusion of personal Internet of things devices capable of sensing the physical environment allows for low-cost solutions to acquire a large amount of information within the urban environment. On the one hand, the use of mobile and intermittent sensors implies new scenarios of large-scale data analysis

010504 meteorology & atmospheric sciencesComputer scienceDistributed computingUrban sensingMobile sensingContext (language use)Information technology02 engineering and technology01 natural sciences[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI]Smart cityEdge intelligence11. Sustainability0202 electrical engineering electronic engineering information engineeringLeverage (statistics)Edge computingVoronoi tessellation0105 earth and related environmental sciencesSmart cityOut-of-order executionSettore INF/01 - InformaticaMulti-agent systemMissing data imputation020206 networking & telecommunicationsT58.5-58.64Variety (cybernetics)Multi-agent system[INFO.INFO-MA]Computer Science [cs]/Multiagent Systems [cs.MA]Mobile deviceInformation Systems
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