Search results for "15"

showing 10 items of 8669 documents

Comparison of SMOS and SMAP soil moisture retrieval approaches using tower-based radiometer data over a vineyard field

2014

International audience; The objective of this study was to compare several approaches to soil moisture (SM) retrieval using l-band microwave radiometry. The comparison was based on a brightness temperature (TB) data set acquired since 2010 by the L-band radiometer ELBARA-II over a vineyard field at the Valencia Anchor Station (VAS) site. ELBARA-II, provided by the European Space Agency (ESA) within the scientific program of the SMOS (Soil Moisture and Ocean Salinity) mission, measures multiangular TB data at horizontal and vertical polarization for a range of incidence angles (30°–60°). Based on a three year data set (2010–2012), several SM retrieval approaches developed for spaceborne miss…

010504 meteorology & atmospheric sciencesMean squared errorMeteorology[SDE.MCG]Environmental Sciences/Global Changes0211 other engineering and technologiesSoil Science02 engineering and technologyAstrophysics::Cosmology and Extragalactic Astrophysics01 natural sciencesPhysics::Geophysics14. Life underwaterComputers in Earth SciencesTime series021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingAtmospheric soundingValencia Anchor StationRadiometerGeologyInversion (meteorology)SMAP15. Life on landBrightness temperatureSoil waterEnvironmental scienceRadiometrySoil moisture retrievalELBARA[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingSMOSRemote Sensing of Environment
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Empirical and physical estimation of Canopy Water Content from CHRIS/PROBA data

2013

20 páginas, 4 tablas, 7 figuras.

010504 meteorology & atmospheric sciencesMean squared errorScience0211 other engineering and technologies02 engineering and technologyCHRIS/PROBA01 natural sciencescanopy water content;model inversion;neural networks;look up tables;empirical up-scalingmodel inversionEmpirical up-scalingAtmospheric radiative transfer codeslook up tablesRadiative transferModel inversion021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingArtificial neural networkCanopy water contentQHyperspectral imagingInversion (meteorology)Sigmoid functionSpectral bandsempirical up-scaling15. Life on landneural networks[SDE]Environmental SciencesGeneral Earth and Planetary SciencesLook up tablescanopy water contentNeural networkscanopy water content; model inversion; neural networks; look up tables; empirical up-scaling; CHRIS/PROBA
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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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2016

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) gran…

010504 meteorology & atmospheric sciencesMeteorologyFLUXNET0208 environmental biotechnology0207 environmental engineeringlcsh:Life02 engineering and technologySensible heatAtmospheric sciences7. Clean energy01 natural sciencesFlux (metallurgy)FluxNetMachine learning; Carbon fluxes; Energy fluxes; FLUXNET; Remote sensing; FLUXCOMlcsh:QH540-549.5Latent heatMachine learningCarbon fluxes020701 environmental engineeringEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEarth-Surface ProcessesFLUXCOMMultivariate adaptive regression splineslcsh:QE1-996.5Empirical modellingPrimary production15. Life on landRemote sensingEnergy fluxes020801 environmental engineeringlcsh:Geologylcsh:QH501-531Kernel method13. Climate actionEnvironmental sciencelcsh:EcologyBiogeosciences
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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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Retrieval of daily gross primary production over Europe and Africa from an ensemble of SEVIRI/MSG products

2018

The main goal of this paper is to derive a method for a daily gross primary production (GPP) product over Europe and Africa taking the full advantage of the SEVIRI/MSG satellite products from the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) sensors delivered from the Satellite Application Facility for Land Surface Analysis (LSA SAF) system. Special attention is paid to model the daily GPP response from an optimized Montheith's light use efficiency model under dry conditions by controlling water shortage limitations from the actual evapotranspiration and the potential evapotranspiration (PET). The PET was parameterized using the mean daily air temperatur…

010504 meteorology & atmospheric sciencesMeteorologySettore AGR/05 - ASSESTAMENTO FORESTALE E SELVICOLTURAWater stressBiome0211 other engineering and technologiesEddy covarianceDaily02 engineering and technologyManagement Monitoring Policy and LawAtmospheric sciences01 natural sciencesLight-Use EfficiencyEvapotranspirationComputers in Earth SciencesMSG021101 geological & geomatics engineering0105 earth and related environmental sciencesEarth-Surface ProcessesGlobal and Planetary ChangeConsistency analysisRelative biasPrimary production15. Life on landGeographyPhysical Geography13. Climate actionLSA SAFForest vegetationSatelliteLight-use efficiencyGPP
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Abiotic and biotic controls on methane formation down to 2.5 km depth within the Precambrian Fennoscandian Shield

2017

Abstract Despite a geological history characterised by high temperature and pressure processes and organic carbon deprived crystalline bedrock, large amounts of hydrocarbons are found in deep groundwaters within Precambrian continental shields. In many sites, methane comprises more that 80% of the dissolved gas phase reaching concentrations of tens of mmol l −1 . In this study, we used isotopic methods to study the carbon isotope systematics and sources of crustal methane within the Fennoscandian Shield. The main study sites were the Outokumpu Deep Drill Hole and the Pyhasalmi mine in Finland, both of which allow groundwater sampling down to 2.5 km depth and have been previously studied for…

010504 meteorology & atmospheric sciencesMethanogenesista1171GeochemistryMineralogychemistry.chemical_element010502 geochemistry & geophysicsmetaani01 natural sciencesMethanechemistry.chemical_compoundPrecambrianPyhäsalmikalsiittiGeochemistry and PetrologyNatural gasgrafiitti0105 earth and related environmental sciencesTotal organic carbongraphitebusiness.industrymethane15. Life on landethaneOutokumpuchemistryvetycarbon isotopes13. Climate actionIsotopes of carbonhydrogenFennoscandian shieldbusinesscalciteCarbonGroundwaterGeologyGeochimica et Cosmochimica Acta
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2018

The Radar Vegetation Index (RVI) is a well-established microwave metric of vegetation cover. The index utilizes measured linear scattering intensities from co- and cross-polarization and is normalized to ideally range from 0 to 1, increasing with vegetation cover. At long wavelengths (L-band) microwave scattering does not only contain information coming from vegetation scattering, but also from soil scattering (moisture & roughness) and therefore the standard formulation of RVI needs to be revised. Using global level SMAP L-band radar data, we illustrate that RVI runs up to 1.2, due to the pre-factor in the standard formulation not being adjusted to the scattering mechanisms at these lo…

010504 meteorology & atmospheric sciencesMoistureScattering0211 other engineering and technologiesPolarimetry02 engineering and technology15. Life on land01 natural scienceslaw.inventionlawSurface roughnessmedicineGeneral Earth and Planetary SciencesLeaf area indexRadarmedicine.symptomVegetation (pathology)Water content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRemote Sensing
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Effects of climate change and land use intensification on regional biological soil crust cover and composition in southern Africa

2022

Biological soil crusts (biocrusts) form a regular and relevant feature in drylands, as they stabilize the soil, fix nutrients, and influence water cycling. However, biocrust forming organisms have been shown to be dramatically vulnerable to climate and land use change occurring in these regions. In this study, we used Normalized Difference Vegetation Index (NDVI) data of biocrust-dominated pixels (NDVIbiocrust) obtained from hyperspectral and LANDSAT-7 data to analyse biocrust development over time and to forecast future NDVIbiocrust development under different climate change and livestock density scenarios in southern Africa. We validated these results by analysing the occurrence and compo…

010504 meteorology & atmospheric sciencesNDVISoil ScienceLibrary science01 natural sciencesGermanRegional developmentEffects of global warmingPolitical science11. SustainabilityNobel laureateBiocrustmedia_common.cataloged_instanceSpatial distributionEuropean union0105 earth and related environmental sciencesmedia_common2. Zero hungerLand useEuropean researchLivestock density04 agricultural and veterinary sciences15. Life on landRemote sensingEcologíaSpace-for-time studylanguage.human_languageEarth system modelDrylands soils13. Climate action040103 agronomy & agriculturelanguage0401 agriculture forestry and fisheriesChristian ministryMulti-temporal Landsat imageryGeoderma
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Modelling nitrous oxide emissions from cropland at the regional scale

2006

Arable soils are a large source of nitrous oxide (N2O) emissions, making up half of the biogenic emissions worldwide. Estimating their source strength requires methods capable of capturing the spatial and temporal variability of N2O emissions, along with the effects of crop management. Here, we applied a process-based model, CERES, with geo-referenced input data on soils, weather, and land use to map N2O emissions from wheat-cropped soils in three agriculturally intensive regions in France. Emissions were mostly controlled by soil type and local climate conditions, and only to a minor extent by the doses of fertilizer nitrogen applied. As a result, the direct emission factors calculated at …

010504 meteorology & atmospheric sciencesNITROUS OXIDElcsh:TP670-699Atmospheric sciences01 natural sciencesBiochemistryREGIONAL SCALE[SDV.IDA]Life Sciences [q-bio]/Food engineeringAGRONOMIENitrogen cycleComputingMilieux_MISCELLANEOUS0105 earth and related environmental sciences2. Zero hungerLand useIntensive farmingARABLE CROPSMODELLING04 agricultural and veterinary sciences[SDV.IDA] Life Sciences [q-bio]/Food engineering15. Life on landSoil type13. Climate actionGreenhouse gasSoil water040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceSpatial variabilitylcsh:Oils fats and waxesArable landFood Science
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