Search results for "SOI"

showing 10 items of 4823 documents

Integrating Domain Knowledge in Data-Driven Earth Observation With Process Convolutions

2022

The modelling of Earth observation data is a challenging problem, typically approached by either purely mechanistic or purely data-driven methods. Mechanistic models encode the domain knowledge and physical rules governing the system. Such models, however, need the correct specification of all interactions between variables in the problem and the appropriate parameterization is a challenge in itself. On the other hand, machine learning approaches are flexible data-driven tools, able to approximate arbitrarily complex functions, but lack interpretability and struggle when data is scarce or in extrapolation regimes. In this paper, we argue that hybrid learning schemes that combine both approa…

FOS: Computer and information sciencesComputer Science - Machine LearningEarth observationAdvanced microwave scanning radiometer-2 (AMSR-2)moderate resolution imaging spectroradiometer (MODIS)Computer scienceleaf area index (LAI)0211 other engineering and technologiesExtrapolationMachine Learning (stat.ML)02 engineering and technologycomputer.software_genreMachine Learning (cs.LG)Data-drivenConvolutionsymbols.namesakeadvanced scatterometer (ASCAT)Statistics - Machine Learningordinary differential equation (ODE)Electrical and Electronic EngineeringGaussian processsoil moisture and ocean salinity (SMOS)021101 geological & geomatics engineeringInterpretabilityForcing (recursion theory)machine learning (ML)soil moisture (SM)time series analysisgaussian process (GP)symbolsGeneral Earth and Planetary SciencesDomain knowledgeData mininggap fillingphysicscomputerfraction of absorbed photosynthetically active radiation (faPAR)IEEE Transactions on Geoscience and Remote Sensing
researchProduct

Randomized kernels for large scale Earth observation applications

2020

Abstract Current remote sensing applications of bio-geophysical parameter estimation and image classification have to deal with an unprecedented big amount of heterogeneous and complex data sources. New satellite sensors involving a high number of improved time, space and wavelength resolutions give rise to challenging computational problems. Standard physical inversion techniques cannot cope efficiently with this new scenario. Dealing with land cover classification of the new image sources has also turned to be a complex problem requiring large amount of memory and processing time. In order to cope with these problems, statistical learning has greatly helped in the last years to develop st…

FOS: Computer and information sciencesEarth observationComputer Science - Machine Learning010504 meteorology & atmospheric sciencesComputer scienceRemote sensing application0211 other engineering and technologiesSoil Science02 engineering and technologycomputer.software_genre01 natural sciencesMachine Learning (cs.LG)Computers in Earth Sciences021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingContextual image classificationEstimation theoryHyperspectral imagingGeology15. Life on landKernel methodKernel regressionData miningComputational problemcomputerRemote Sensing of Environment
researchProduct

Estimating crop primary productivity with Sentinel-2 and Landsat 8 using machine learning methods trained with radiative transfer simulations

2019

Abstract Satellite remote sensing has been widely used in the last decades for agricultural applications, both for assessing vegetation condition and for subsequent yield prediction. Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely on empirical or semi-empirical approaches, which tend to over-simplify photosynthetic mechanisms. In this work, we take advantage of all parallel developments in mechanistic photosynthesis modeling and satellite data availability for an advanced monitoring of crop productivity. In particular, we combine process-based modeling with …

FOS: Computer and information sciencesLandsat 8Earth observation010504 meteorology & atmospheric sciencesComputer Vision and Pattern Recognition (cs.CV)0208 environmental biotechnologyComputer Science - Computer Vision and Pattern RecognitionSoil Science02 engineering and technologyGross primary productivity (GPP)Sentinel-2 (S2)Machine learningcomputer.software_genre01 natural sciencesRadiative transfer modeling (RTM)Atmospheric radiative transfer codesSoil-canopy-observation of photosynthesis and the energy balance (SCOPE)Computers in Earth SciencesC3 crops0105 earth and related environmental sciencesRemote sensing2. Zero hungerArtificial neural networkbusiness.industryEmpirical modellingNeural networks (NN)GeologyVegetationMachine learning (ML)15. Life on landHybrid approach22/4 OA procedure020801 environmental engineeringVariable (computer science)ITC-ISI-JOURNAL-ARTICLEEnvironmental scienceSatelliteArtificial intelligenceScale (map)businesscomputerRemote sensing of environment
researchProduct

Visual Parameter Selection for Spatial Blind Source Separation.

2022

Analysis of spatial multivariate data, i.e., measurements at irregularly-spaced locations, is a challenging topic in visualization and statistics alike. Such data are inteGral to many domains, e.g., indicators of valuable minerals are measured for mine prospecting. Popular analysis methods, like PCA, often by design do not account for the spatial nature of the data. Thus they, together with their spatial variants, must be employed very carefully. Clearly, it is preferable to use methods that were specifically designed for such data, like spatial blind source separation (SBSS). However, SBSS requires two tuning parameters, which are themselves complex spatial objects. Setting these parameter…

FOS: Computer and information sciencesgeographic visualizationvisualisointiComputer Science - Human-Computer Interactionhuman-centered computingvisualisointitekniikatmuuttujatanalyysimenetelmätgeostatistiikkaComputer Graphics and Computer-Aided Designvisualization techniqueskompleksisuusHuman-Computer Interaction (cs.HC)datamaantieteellinen visualisointiComputer graphics forum : journal of the European Association for Computer Graphics
researchProduct

Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect

2021

Common reporting styles for statistical results in scientific articles, such as $p$ p -values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the $p$ p -value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recom…

FOS: Computer and information sciencesvisualisointiBayesian inferencetilastomenetelmätComputer Science - Human-Computer Interactiontulkinta02 engineering and technologyBayesian inferenceluottamustasotHuman-Computer Interaction (cs.HC)cliff effectData visualizationhypothesis testing0202 electrical engineering electronic engineering information engineeringStatistical inferencevisualizationconfidence intervalsStatistical hypothesis testingpäättelybusiness.industrybayesilainen menetelmäOther Statistics (stat.OT)Multilevel model020207 software engineeringtilastografiikkaComputer Graphics and Computer-Aided DesignConfidence intervalStatistics - Other StatisticsSignal ProcessingComputer Vision and Pattern RecognitionbusinessPsychologyNull hypothesisValue (mathematics)SoftwareCognitive psychologystatistical inference
researchProduct

Applications of sinusoidal phase modulation in temporal optics to highlight some properties of the Fourier transform

2019

International audience; Fourier analysis plays a major role in the analysis and understanding of many phenomena in physics and contemporary engineering. However, students, who have often discovered this notion through numerical tools, do not necessarily understand all the richness that can be derived from joint analysis in the temporal and spectral domains, particularly in the field of optics. As part of the second year of the Master's degree in Physics Lasers and Materials at the University of Burgundy, we have set up a set of experiments to highlight these concepts and to show, on a non-trivial example of periodic phase modulation, the precautions to be taken in the interpretation of the …

FOS: Physical sciencesGeneral Physics and Astronomy01 natural sciencesSession (web analytics)Interpretation (model theory)symbols.namesakeOpticsPhysics Education (physics.ed-ph)0103 physical sciencesoptical spectrum010306 general physicsSet (psychology)Telecommunications equipmentsignal processingPhysics[PHYS.PHYS.PHYS-OPTICS]Physics [physics]/Physics [physics]/Optics [physics.optics]business.industry05 social sciencesPhysics - Physics Education050301 educationSinusoidal phase modulationField (geography)Fourier transformFourier analysissymbolsFourier transformbusiness0503 educationPhase modulationPhysics - OpticsOptics (physics.optics)
researchProduct

Use of functional genes to quantify denitrifiers in the environment.

2006

During the last decade, application of molecular methods using cultivation-independent approaches has provided new insights into the composition and structure of denitrifying communities in various environments. However, little is known about their abundance, and quantification is still performed using cultivation-based approaches, which are not only biased by the inability to cultivate of many micro-organisms but also fastidious and time-consuming. Two types of cultivation-independent approaches have recently been developed to quantify denitrifiers. The first type, which is based on the hybridization technique, comprises the use of Southern hybridization and DNA arrays. The second type, ba…

Fastidious organismNitratesEcologyNucleic Acid HybridizationFunctional genesComputational biologyBiologyEnvironmentBiochemistryPolymerase Chain ReactionCompetitive pcrlaw.inventionlawMost probable numberGenes BacterialPolymerase chain reactionNitritesSoil MicrobiologySouthern blotOligonucleotide Array Sequence AnalysisBiochemical Society transactions
researchProduct

Assisted phytostabilization of soil from a former military area with mineral amendments.

2019

Abstract Due to the presence of toxic pollutants, soils in former military areas need remedial actions with environmentally friendly methods. Greenhouse experiments were conducted to investigate the aided phytostabilization of multi-heavy metals (HMs), i.e. Cd, Cr, Cu, Ni, Pb, Zn, in post-military soil by Festuca rubra and three mineral amendments (diatomite, dolomite and halloysite). The amendments were applied at 0 and 3.0% to each pot filled with 5 kg of polluted soil. After seven weeks of the phytostabilization, selected soil properties, biomass yield of F. rubra and immobilization of HMs by their accumulation in plant and redistribution among individual fractions in soil were determine…

FestucaHealth Toxicology and MutagenesisDolomite0211 other engineering and technologies02 engineering and technology010501 environmental sciencesengineering.material01 natural sciencesHalloysitePlant RootsCalcium CarbonateSoilMetals HeavyMilitary FacilitiesEcotoxicologySoil PollutantsMagnesiumBiomassEnvironmental Restoration and Remediation0105 earth and related environmental sciencesPollutant021110 strategic defence & security studiesbiologyChemistryPublic Health Environmental and Occupational HealthGeneral Medicinebiology.organism_classificationPollutionSoil conditionerRemedial actionBiodegradation EnvironmentalEnvironmental chemistrySoil waterengineeringClayFestuca rubraEcotoxicology and environmental safety
researchProduct

Soil Moisture Effect on Thermal Infrared (8–13-μm) Emissivity

2010

Thermal infrared (TIR) emissivities of soils with different textures were measured for several soil moisture (SM) contents under controlled conditions using the Box method and a high-precision multichannel TIR radiometer. The results showed a common increase of emissivity with SM at water contents lower than the field capacity. However, this dependence is negligible for higher water contents. The highest emissivity variations were observed in sandy soils, particularly in the 8-9-μm range due to water adhering to soil grains and decreasing the reflectance in the 8-9-μm quartz doublet region. Thus, in order to model the emissivity dependence on soil water content, different approaches were st…

Field capacityMaterials scienceRadiometerMoistureSoil textureSoil waterEmissivityGeneral Earth and Planetary SciencesSoil classificationSoil scienceElectrical and Electronic EngineeringWater contentIEEE Transactions on Geoscience and Remote Sensing
researchProduct

Analitical deriving of the field capacity through soil bundle model

2015

The concept of field capacity as soil hydraulic parameter is widely used in many hydrological applications. Althought its recurring usage, its definition is not univocal. Traditionally, field capacity has been related to the amount of water that remains in the soil after the excess water has drained away and the water downward movement experiences a significant decresase. Quantifying the drainage of excess of water may be vague and several definitions, often subjective, have been proposed. These definitions are based on fixed thresholds either of time, pressure, or flux to which the field capacity condition is associated. The flux­based definition identifies the field capacity as the soil m…

Field capacitysoil bundle modelanalytical approach
researchProduct