Search results for "image resolution"

showing 10 items of 377 documents

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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Contribution of environmental factors to temperature distribution at different resolution levels on the forefield of the Loven Glaciers (Svalbard)

2007

ABSTRACTThe climate and its components (temperature and precipitation) are organised according to different spatial scales that are structured hierarchically. The aim of this paper is to explore the dependence between temperature and deterministic factors at different scales on a 10 km2 study area on the northwestern coast of Svalbard. A GIS was developed which contained three sources of information: temperature, remotely sensed imagery and digital elevation models (DEM), and derived raster data layers. The first layer, temperatures, was acquired at regularly observed temporal intervals from 53 stations. The second layer comprised remotely sensed images (aerial photography and SPOT imagery)…

010504 meteorology & atmospheric sciencesEcology[SHS.GEO] Humanities and Social Sciences/GeographyGeography Planning and Development0207 environmental engineeringElevation02 engineering and technology[SHS.GEO]Humanities and Social Sciences/Geography15. Life on land01 natural sciences[ SHS.GEO ] Humanities and Social Sciences/GeographyRaster dataAerial photography13. Climate actionLinear regressionSpatial ecologyGeneral Earth and Planetary Sciences020701 environmental engineeringDigital elevation modelScale (map)Image resolutionGeology0105 earth and related environmental sciencesRemote sensing
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Estudio de bofedales en los Andes ecuatorianos a través de la comparación de imágenes Landsat-8 y Sentinel-2

2019

[EN] The objective of the present study was to compare the Landsat-8 and Sentinel-2 images to calculate the wetland´s extension, distribution and degree of conservation, in Reserva de Producción de Fauna Chinborazo (RPFCH) protected area located in the Andean region of Ecuador. This process was developed with in situ work in 16 wetlands, distributed in different conservation levels. The Landsat-8 and Sentinel-2 images were processed through a radiometric calibration (restoration of lost lines or píxels and correction of the stripe of the image) and an atmospheric correction (conversion of the digital levels to radiance values), to later calculate the Vegetation spectral indexes: NDVI, SAVI …

010504 meteorology & atmospheric sciencesGeography Planning and Development0211 other engineering and technologiesRed edgeWetland02 engineering and technology01 natural sciencesNormalized Difference Vegetation IndexLandsat-8Earth and Planetary Sciences (miscellaneous)Red EdgeImage resolutionBofedal021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsgeographyRandom Forestgeography.geographical_feature_categoryPixelAtmospheric correctionForestryVegetationRadianceSentinel-2Revista de Teledetección
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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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Estimating high resolution evapotranspiration from disaggregated thermal images

2016

Abstract Accurate evapotranspiration (ET) estimations based on surface energy balance from remote sensing require information in the thermal infrared (TIR) domain, normally provided with an insufficient spatial resolution. In order to estimate ET in heterogeneous agricultural areas, we inspect in this paper the use of disaggregation techniques applied to two different sensors, such as MODIS (daily revisit cycle and 1 km spatial resolution in the TIR domain) and Spot 5 (5 days revisit cycle and 10 m spatial resolution in the VNIR bands but no TIR band). Spot 5 images were used as a proxy for upcoming Sentinel-2. The Simplified Two-Source Energy Balance (STSEB) model was used for the estimati…

010504 meteorology & atmospheric sciencesLand surface temperatureMeteorology0211 other engineering and technologiesEnergy balanceSoil ScienceHigh resolutionGeology02 engineering and technologySensible heat01 natural sciencesVNIREvapotranspirationThermalEnvironmental scienceComputers in Earth SciencesImage resolution021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources

2020

The ESA’s forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global monitoring of the vegetation’s chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. In order to properly interpret the fluorescence signal in relation to photosynthetic activity, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem with Sentinel-3 (S3), which conveys the Ocean and Land Colour Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll…

010504 meteorology & atmospheric sciencesMean squared error0208 environmental biotechnologyImaging spectrometerSoil ScienceGeology02 engineering and technologyVegetationSpectral bands15. Life on land01 natural sciencesArticle020801 environmental engineeringPhotosynthetically active radiationKrigingEnvironmental scienceComputers in Earth SciencesLeaf area indexImage resolution0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrie…

2021

In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial reso…

010504 meteorology & atmospheric sciencesMean squared error0208 environmental biotechnologySoil Science02 engineering and technology01 natural sciencesArticleWorldView-3Radiative transferComputers in Earth SciencesImage resolution0105 earth and related environmental sciencesRemote sensingFractional vegetation coverForest fireGeologyInversion (meteorology)15. Life on landEcología. Medio ambienteRadiative transfer modeling020801 environmental engineering13. Climate actionGround-penetrating radarEnvironmental scienceSatelliteSpatial variabilitySentinel-2Scale (map)Remote Sensing of Environment
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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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Enhancing the retrieval of stream surface temperature from Landsat data

2019

International audience; Thermal images of water bodies often show a radiance gradient perpendicular to the banks. This effect is frequently due to mixed land and water thermal pixels. In the case of the Landsat images, radiance mixing can also affect pure water pixels due the cubic convolution resampling of the native thermal measurements. Some authors recommended a general-purpose margin of two thermal pixels to the banks or a minimum river width of three pixels, to avoid near bank effects in water temperature retrievals. Given the relatively course spatial resolution of satellite thermal sensors, the three pixel margin severely restricts their application to temperature mapping in many ri…

010504 meteorology & atmospheric sciencesPixel0208 environmental biotechnologySoil ScienceGeologyImage processing02 engineering and technology01 natural sciencesSubpixel rendering6. Clean water020801 environmental engineering[SDE]Environmental SciencesThermalRadianceEnvironmental scienceSatelliteSatellite imageryComputers in Earth SciencesRiver surface temperature Landsat 8 thermal band Thermal spatial resolution Cubic convolution resampling Thermal impact Mequinenza reservoir Ebro river Thermal stratificationImage resolution0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review

2020

Abstract Green fractional vegetation cover ( f c ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of f c via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a compre…

010504 meteorology & atmospheric sciencesResilient Livelihoods0211 other engineering and technologies02 engineering and technologyForests01 natural sciencesNormalized Difference Vegetation IndexArticleVegetation coverAbundance (ecology)Computers in Earth SciencesAdaptationEngineering (miscellaneous)Image resolution021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingMathematicsEstimationVegetationBiodiversity15. Life on landAtomic and Molecular Physics and OpticsComputer Science ApplicationsRemote sensing (archaeology)Vegetation IndexAlgorithm
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