Search results for "Geomatics"

showing 10 items of 495 documents

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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Validation of SMAP surface soil moisture products with core validation sites

2017

Abstract The NASA Soil Moisture Active Passive (SMAP) mission has utilized a set of core validation sites as the primary methodology in assessing the soil moisture retrieval algorithm performance. Those sites provide well-calibrated in situ soil moisture measurements within SMAP product grid pixels for diverse conditions and locations. The estimation of the average soil moisture within the SMAP product grid pixels based on in situ measurements is more reliable when location specific calibration of the sensors has been performed and there is adequate replication over the spatial domain, with an up-scaling function based on analysis using independent estimates of the soil moisture distributio…

010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesSoil Science02 engineering and technology01 natural scienceslaw.inventionlawValidationCalibrationComputers in Earth SciencesRadarSpatial domainWater content021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRadiometerPixelGeologySMAP22/4 OA procedureITC-ISI-JOURNAL-ARTICLEEnvironmental scienceSatelliteSoil moisture
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Hyperspectral dimensionality reduction for biophysical variable statistical retrieval

2017

Abstract Current and upcoming airborne and spaceborne imaging spectrometers lead to vast hyperspectral data streams. This scenario calls for automated and optimized spectral dimensionality reduction techniques to enable fast and efficient hyperspectral data processing, such as inferring vegetation properties. In preparation of next generation biophysical variable retrieval methods applicable to hyperspectral data, we present the evaluation of 11 dimensionality reduction (DR) methods in combination with advanced machine learning regression algorithms (MLRAs) for statistical variable retrieval. Two unique hyperspectral datasets were analyzed on the predictive power of DR + MLRA methods to ret…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologies02 engineering and technologycomputer.software_genre01 natural sciencessymbols.namesakeLinear regressionComputers in Earth SciencesEngineering (miscellaneous)Gaussian processHyMap021101 geological & geomatics engineering0105 earth and related environmental sciencesData stream miningbusiness.industryDimensionality reductionHyperspectral imagingPattern recognitionAtomic and Molecular Physics and OpticsComputer Science ApplicationsKernel (statistics)symbolsData miningArtificial intelligencebusinesscomputerISPRS Journal of Photogrammetry and Remote Sensing
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Gaussian Processes Retrieval of LAI from Sentinel-2 Top-of-Atmosphere Radiance Data

2020

Abstract Retrieval of vegetation properties from satellite and airborne optical data usually takes place after atmospheric correction, yet it is also possible to develop retrieval algorithms directly from top-of-atmosphere (TOA) radiance data. One of the key vegetation variables that can be retrieved from at-sensor TOA radiance data is leaf area index (LAI) if algorithms account for variability in atmosphere. We demonstrate the feasibility of LAI retrieval from Sentinel-2 (S2) TOA radiance data (L1C product) in a hybrid machine learning framework. To achieve this, the coupled leaf-canopy-atmosphere radiative transfer models PROSAIL-6SV were used to simulate a look-up table (LUT) of TOA radi…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesAtmospheric correctionFOS: Physical sciences02 engineering and technology15. Life on land01 natural sciencesAtomic and Molecular Physics and OpticsArticleComputer Science ApplicationsPhysics - Atmospheric and Oceanic PhysicsAtmospheric radiative transfer codesKrigingAtmospheric and Oceanic Physics (physics.ao-ph)RadianceSatelliteComputers in Earth SciencesLeaf area indexScale (map)Engineering (miscellaneous)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing
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Optimizing Gaussian Process Regression for Image Time Series Gap-Filling and Crop Monitoring

2020

Image processing entered the era of artificial intelligence, and machine learning algorithms emerged as attractive alternatives for time series data processing. Satellite image time series processing enables crop phenology monitoring, such as the calculation of start and end of season. Among the promising algorithms, Gaussian process regression (GPR) proved to be a competitive time series gap-filling algorithm with the advantage of, as developed within a Bayesian framework, providing associated uncertainty estimates. Nevertheless, the processing of time series images becomes computationally inefficient in its standard per-pixel usage, mainly for GPR training rather than the fitting step. To…

010504 meteorology & atmospheric sciencesMean squared errorComputer science0211 other engineering and technologiesImage processing02 engineering and technologycomputer.software_genre01 natural scienceslcsh:AgricultureKrigingTime series021101 geological & geomatics engineering0105 earth and related environmental sciences2. Zero hungerHyperparameterPixelSeries (mathematics)lcsh:SGaussian processes regressionSatellite Image Time SeriesData miningtime seriesSentinel-2optimizationAgronomy and Crop Sciencecomputercrop monitoringphenology indicatorsAgronomy
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Potential of Automated Digital Hemispherical Photography and Wireless Quantum Sensors for Routine Canopy Monitoring and Satellite Product Validation

2021

To better characterize the temporal dynamics of vegetation biophysical variables, a variety of automated in situ measurement techniques have been developed in recent years. In this study, we investigated automated digital hemispherical photography (DHP) and wireless quantum sensors, which were installed at two sites under the Copernicus Ground Based Observations for Validation (GBOV) project. Daily estimates of plant area index (PAI) and the fraction of absorbed photosynthetically active radiation (FAPAR) were obtained, which realistically described expected vegetation dynamics. Good correspondence with manual DHP and LAI-2000 data (RMSE = 0.39 to 0.90 for PAI, RMSE = 0.07 for FAPAR) provid…

010504 meteorology & atmospheric sciencesMean squared errorHemispherical photographyPhotographyQuantum sensor0211 other engineering and technologies02 engineering and technologyVegetation01 natural sciencesPhotosynthetically active radiationEnvironmental scienceSatelliteWireless sensor network021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensing2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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Vicarious Calibration of the Landsat 7 Thermal Infrared Band and LST Algorithm Validation of the ETM+ Instrument Using Three Global Atmospheric Profi…

2017

Due to problems in the thermal infrared sensor on-board the Landsat-8 satellite, Landsat-7 (L7) can be an interesting alternative source of thermal data because it is the only source of well-calibrated, free, high-resolution data. To contribute to the quality of thermal data, a vicarious calibration (VC) of the enhanced thematic mapper instrument and a validation of the single-channel general equation and the water vapor approach algorithm in conjunction with an inversion of the radiative transfer equation (RTE) have been performed during 2013–2015 over two Spanish test sites. For this purpose, three global atmospheric profile data sets were used to better characterize the error due to atmo…

010504 meteorology & atmospheric sciencesMean squared errorMeteorology0211 other engineering and technologiesAtmospheric correction02 engineering and technologyAtmospheric model01 natural sciencesThematic MapperRadiative transferGeneral Earth and Planetary SciencesEnvironmental scienceRadiometrySatelliteElectrical and Electronic EngineeringAlgorithmWater vapor021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingIEEE Transactions on Geoscience and Remote Sensing
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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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