Search results for "error"

showing 10 items of 1643 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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Exploitation of SAR and Optical Sentinel Data to Detect Rice Crop and Estimate Seasonal Dynamics of Leaf Area Index

2017

This paper presents and evaluates multitemporal LAI estimates derived from Sentinel-2A data on rice cultivated area identified using time series of Sentinel-1A images over the main European rice districts for the 2016 crop season. This study combines the information conveyed by Sentinel-1A and Sentinel-2A into a high-resolution LAI retrieval chain. Rice crop was detected using an operational multi-temporal rule-based algorithm, and LAI estimates were obtained by inverting the PROSAIL radiative transfer model with Gaussian process regression. Direct validation was performed with in situ LAI measurements acquired in coordinated field campaigns in three countries (Italy, Spain and Greece). Res…

010504 meteorology & atmospheric sciencesMean squared errorScienceleaf area index (LAI)0211 other engineering and technologies02 engineering and technology01 natural sciencesCropAtmospheric radiative transfer codesConsistency (statistics)KrigingSpatial consistencyArròs Malalties i plaguesSentinel-1ALeaf area indexmappingSentinel021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2. Zero hungerLeaf Area IndexSentinel-2AQCiències de la terrarice mapGeneral Earth and Planetary SciencesEnvironmental sciencerice map; leaf area index (LAI); Sentinel-1A; Sentinel-2A; Gaussian process regressionRice cropGaussian process regressionRemote Sensing
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Quantifying geological uncertainty in metamorphic phase equilibria modelling; a Monte Carlo assessment and implications for tectonic interpretations

2016

AbstractPseudosection modelling is rapidly becoming an essential part of a petrologist's toolkit and often forms the basis of interpreting the tectonothermal evolution of a rock sample, outcrop, or geological region. Of the several factors that can affect the accuracy and precision of such calculated phase diagrams, “geological” uncertainty related to natural petrographic variation at the hand sample- and/or thin section-scale is rarely considered. Such uncertainty influences the sample's bulk composition, which is the primary control on its equilibrium phase relationships and thus the interpreted pressure–temperature (P–T) conditions of formation. Two case study examples—a garnet–cordierit…

010504 meteorology & atmospheric sciencesMetamorphic rockMonte Carlo methodMineralogyPseudosectionEarth and Planetary Sciences(all)3705 Geologysub-05010502 geochemistry & geophysics01 natural sciencesKyaniteGeological uncertaintyMatrix (geology)ErrorPetrographyMonte Carlo0105 earth and related environmental sciencesMnNCKFMASHTOlcsh:QE1-996.5Schist37 Earth Scienceslcsh:GeologyTectonicsvisual_artStaurolitevisual_art.visual_art_mediumGeneral Earth and Planetary Sciences3706 GeophysicsGeologyGeoscience Frontiers
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Sun-Induced Chlorophyll Fluorescence I: Instrumental Considerations for Proximal Spectroradiometers

2019

Growing interest in the proximal sensing of sun-induced chlorophyll fluorescence (SIF) has been boosted by space-based retrievals and up-coming missions such as the FLuorescence EXplorer (FLEX). The European COST Action ES1309 “Innovative optical tools for proximal sensing of ecophysiological processes” (OPTIMISE, ES1309; https://optimise.dcs.aber.ac.uk/) has produced three manuscripts addressing the main current challenges in this field. This article provides a framework to model the impact of different instrument noise and bias on the retrieval of SIF; and to assess uncertainty requirements for the calibration and characterization of state-of-the-art SIF-oriented spectroradiom…

010504 meteorology & atmospheric sciencesUFSP13-8 Global Change and BiodiversitySensor model0211 other engineering and technologiesEarth and Planetary Sciences(all)02 engineering and technology01 natural sciencesErrorsensor modelSpectroradiometerSun-induced chlorophyll fluorescencesun-induced chlorophyll fluorescence; spectroradiometer; sensor model; uncertainty; errorCalibrationCost actionuncertaintylcsh:ScienceChlorophyll fluorescencesun-induced chlorophyll fluorescence/dk/atira/pure/subjectarea/asjc/1900021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingNoise (signal processing)1900 General Earth and Planetary SciencesUncertaintySensor modelReflectivityerror3. Good healthValidation methodsSpectroradiometerspectroradiometerEnvironmental science570 Life sciences; biologyGeneral Earth and Planetary Scienceslcsh:QRemote Sensing
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Airborne-laser-scanning-derived auxiliary information discriminating between broadleaf and conifer trees improves the accuracy of models for predicti…

2020

Managing forests for ecosystem services and biodiversity requires accurate and spatially explicit forest inventory data. A major objective of forest management inventories is to estimate the standing timber volume for certain forest areas. In order to improve the efficiency of an inventory, field based sample-plots can be statistically combined with remote sensing data. Such models usually incorporate auxiliary variables derived from canopy height models. The inclusion of forest type variables, which quantify broadleaf and conifer volume proportions, has been shown to further improve model performance. Currently, the most common way of quantifying broadleaf and conifer forest types is by ca…

0106 biological sciencesCanopysekametsätMean squared errorForest managementBiodiversityClimate changeairborne laser scanningManagement Monitoring Policy and Law010603 evolutionary biology01 natural sciencesforest type mapStatisticscanopy height modelimage-based point cloudsNature and Landscape ConservationForest inventorymetsäsuunnitteluForestryPercentage pointmetsänarviointipuutavaranmittausOrdinary least squaresordinary least squares regression modelsEnvironmental sciencemixed and heterogeneously structured forestkaukokartoitushigh-precision forest inventorymetsänhoitobest fit modelsmerchantable timber volumelaserkeilaus010606 plant biology & botanyForest Ecology and Management
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Simple learning rules to cope with changing environments

2008

10 pages; International audience; We consider an agent that must choose repeatedly among several actions. Each action has a certain probability of giving the agent an energy reward, and costs may be associated with switching between actions. The agent does not know which action has the highest reward probability, and the probabilities change randomly over time. We study two learning rules that have been widely used to model decision-making processes in animals-one deterministic and the other stochastic. In particular, we examine the influence of the rules' 'learning rate' on the agent's energy gain. We compare the performance of each rule with the best performance attainable when the agent …

0106 biological sciencesError-driven learningExploitComputer scienceEnergy (esotericism)Biomedical EngineeringBiophysicsBioengineeringanimal behavior010603 evolutionary biology01 natural sciencesBiochemistryMulti-armed banditModels Biologicaldecision makingBiomaterials03 medical and health sciences[ INFO.INFO-BI ] Computer Science [cs]/Bioinformatics [q-bio.QM][ SDV.EE.IEO ] Life Sciences [q-bio]/Ecology environment/SymbiosisAnimalsLearningComputer Simulation[ SDV.BIBS ] Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]multi-armed banditEcosystem030304 developmental biologySimple (philosophy)0303 health sciences[ SDE.BE ] Environmental Sciences/Biodiversity and Ecologybusiness.industrydynamic environmentslearning rulesdecision-making[SDV.BIBS]Life Sciences [q-bio]/Quantitative Methods [q-bio.QM]Unlimited periodRange (mathematics)Action (philosophy)Artificial intelligence[SDE.BE]Environmental Sciences/Biodiversity and Ecology[INFO.INFO-BI]Computer Science [cs]/Bioinformatics [q-bio.QM]businessBiotechnologyResearch Article[SDV.EE.IEO]Life Sciences [q-bio]/Ecology environment/Symbiosis
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Colour patch size and measurement error using reflectance spectrophotometry

2017

1 - Over the past twenty years, portable and relatively affordable spectrophotometers have greatly advanced the study of animal coloration. However, the small size of many colour patches poses methodological challenges that have not, to date, been assessed in the literature. Here, we tackle this issue for a reflectance spectrophotometry set-up widely used in ecology and evolution (the beam method). 2 - We reviewed the literature on animal coloration reporting the use of reflectance spectrophotometry to explore how the minimum measurable size of a colour patch is determined. We then used coloured plastic sheets to create artificial colour patches, and quantify the relationship between colour…

0106 biological sciencesObservational errorgenetic structuresmedicine.diagnostic_testEcological Modeling05 social sciencesAnimal coloration010603 evolutionary biology01 natural sciencesReflectivitySpectrophotometrymedicine0501 psychology and cognitive sciences050102 behavioral science & comparative psychologyBiological systemEcology Evolution Behavior and SystematicsMathematicsMethods in Ecology and Evolution
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Sexing birds using discriminant function analysis: a critical appraisal.

2011

9 pages; International audience; Discriminant function analysis (DFA) based on morphological measurements is a quick, inexpensive, and efficient method for sex determination in field studies on cryptically monomorphic bird species. However, behind the apparent standardization and relative simplicity of DFA lie subtle differences and pitfalls that have been neglected in some studies. Most of these concerns directly affect assessment of the discriminant performance, a parameter of crucial importance in practice because it provides a measure of the quality of an equation that may be used in later field studies. Using results from 141 published studies and simulations based on a large data set …

0106 biological sciencesZenaida auritaZenaida auritaZenaida dovesSexing[SDV.BID]Life Sciences [q-bio]/Biodiversitysample size effect010603 evolutionary biology01 natural sciencescross-validationCross-validation010605 ornithologyDiscriminant function analysisStatisticsEcology Evolution Behavior and Systematics[ SDV.BID ] Life Sciences [q-bio]/Biodiversity[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology[STAT.AP]Statistics [stat]/Applications [stat.AP]biology[ STAT.AP ] Statistics [stat]/Applications [stat.AP]biology.organism_classificationmorphological measurementsDFADiscriminantSample size determinationsexual dimorphismAnimal Science and Zoology[SDE.BE]Environmental Sciences/Biodiversity and EcologyJackknife resamplingmeasurement errors
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