Search results for " SEVIRI."

showing 2 items of 2 documents

Surface soil water content estimation based on thermal inertia and Bayesian smoothing

2014

Soil water content plays a critical role in agro-hydrology since it regulates the rainfall partition between surface runoff and infiltration and, the energy partition between sensible and latent heat fluxes. Current thermal inertia models characterize the spatial and temporal variability of water content by assuming a sinusoidal behavior of the land surface temperature between subsequent acquisitions. Such behavior implicitly supposes clear sky during the whole interval between the thermal acquisitions; but, since this assumption is not necessarily verified even if sky is clear at the exact epoch of acquisition, , the accuracy of the model may be questioned due to spatial and temporal varia…

Soil Water Content Bayesian Smoothing Thermal Inertia MODIS SEVIRI.Meteorologymedia_common.quotation_subjectPolar orbitBayesian SmoothingLatent heatSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliElectrical and Electronic EngineeringWater contentImage resolutionRemote sensingmedia_commonSettore ING-INF/03 - TelecomunicazioniElectronic Optical and Magnetic MaterialSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaThermal InertiaComputer Science Applications1707 Computer Vision and Pattern RecognitionSEVIRICondensed Matter PhysicsApplied MathematicGeographyMODISSoil Water ContentSkyGeostationary orbitSurface runoffShortwaveSettore ICAR/06 - Topografia E CartografiaSPIE Proceedings
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Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications

2019

The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed …

Data records010504 meteorology & atmospheric sciencesData productsSciencemeteosat second generation (MSG); biophysical parameters (LAI; FVC; FAPAR); SEVIRI; climate data records (CDR); stochastic spectral mixture model (SSMM); Satellite Application Facility for Land Surface Analysis (LSA SAF)0211 other engineering and technologiesstochastic spectral mixture model (SSMM)02 engineering and technology01 natural sciencesFAPAR)climate data records (CDR)Leaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesQVegetationSEVIRIMixture modelSatellite Application Facility for Land Surface Analysis (LSA SAF)FVCbiophysical parameters (LAIPhotosynthetically active radiationGeostationary orbitGeneral Earth and Planetary SciencesEnvironmental sciencemeteosat second generation (MSG)SatelliteAlgorithmRemote Sensing; Volume 11; Issue 18; Pages: 2103
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