Search results for "Remote Sensing"

showing 10 items of 1262 documents

Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset.

2013

Abstract: Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500 nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100 mu g cm(-2)), which are seldom es…

ChlorophyllSpecific leaf areaNitrogenBiophysicsRed edgeTreesAbsorbancesymbols.namesakeRadiology Nuclear Medicine and imagingGaussian processWater contentBiologyRemote sensingMathematicsRadiationRadiological and Ultrasound TechnologyPhysicsHyperspectral imagingWaterRegression analysisPlant LeavesChemistrySpectrometry FluorescencesymbolsCurve fittingAlgorithmsJournal of photochemistry and photobiology. B, Biology
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Sun-induced fluorescence - a new probe of photosynthesis: First maps from the imaging spectrometer HyPlant.

2015

Variations in photosynthesis still cause substantial uncertainties in predicting photosynthetic CO2 uptake rates and monitoring plant stress. Changes in actual photosynthesis that are not related to greenness of vegetation are difficult to measure by reflectance based optical remote sensing techniques. Several activities are underway to evaluate the sun-induced fluorescence signal on the ground and on a coarse spatial scale using space-borne imaging spectrometers. Intermediate-scale observations using airborne-based imaging spectroscopy, which are critical to bridge the existing gap between small-scale field studies and global observations, are still insufficient. Here we present the first …

Chlorophyllinduced fluorescencesunImaging spectrometer2306 Global and Planetary ChangeFluorescence2300 General Environmental SciencePhotosynthesiEnvironmental ChemistryAirborne measurement910 Geography & travelSpectral resolutionPhotosynthesisAbsorption (electromagnetic radiation)Spectroscopyairborne measurementsChlorophyll fluorescenceGeneral Environmental ScienceRemote sensingGlobal and Planetary ChangeSpectrometerEcology2300Remote sensingImaging spectroscopyVegetation monitoringFLEXImaging spectroscopy10122 Institute of GeographyGEO/10 - GEOFISICA DELLA TERRA SOLIDASpectrometry FluorescenceSun-induced fluorescence2304 Environmental ChemistryHyPlantRemote Sensing TechnologySunlightEnvironmental scienceSpatial variabilityChlorophyll fluorescence2303 EcologyGlobal change biology
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Mapping Actual Evapotranspiration by Combining Landsat TM and NOAA-AVHRR Images: Application to the Barrax Area, Albacete, Spain

1998

Abstract A method that permits determination of actual evapotranspiration, ET, in heterogeneous areas has been proposed. It is based on the relation ET = ET m − B ( T s − T sm ), which combines meteorological, National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA-AVHRR), and Landsat thematic mapper (TM) data. Thus, the maximum evapotranspiration for each crop, ETm, is obtained from in situ measurements carried out in a meteorological station; the temperature difference between each pixel and the pixel that has the maximum evapotranspiration, Ts−Tsm, is calculated for each crop from NOAA-AVHRR data; and the crop distribution in the area is known throu…

Climatic dataPixelThematic MapperAdvanced very-high-resolution radiometerEvapotranspirationSoil ScienceEnvironmental scienceGeologyTemperature differenceComputers in Earth SciencesZea maysMultispectral ScannerRemote sensingRemote Sensing of Environment
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Soil water content monitoring: a verification of thermal inertia approaches on low spatial, high temporal resolutions images

2013

Soil water content is directly connected with soil evaporation and plant transpiration processes; in particular, soil water content within the root zone, is readily available to evapotranspiration. Thus, in agricultural sciences, the assessment of the spatial distribution of soil water content could be of utmost importance in evaluating crop water requirement. In spite of limitations to applicability due to contingent cloud cover, water content of the upper part of the soil can be determined by applying the thermal inertia approach by coupling optical and thermal infrared images. The thermal inertia formulation, rigorously retrieved on bare soil, has been also verified on soils partially co…

Cloud coverSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaSoil scienceVegetationSoil water contentSpatial distributionPhase differenceSoil thermal propertiesGeographyMODISEvapotranspirationSoil waterSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliWater contentThermal inertiaSettore ICAR/06 - Topografia E CartografiaRemote sensingTranspirationRemote Sensing for Agriculture, Ecosystems, and Hydrology XV
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Critical analysis of the thermal inertia approach to map soil water content under sparse vegetation and changeable sky conditions

2012

The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax (Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Tecnica Aeroespacial) between the 11 th and 13 rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary prox…

Cloud coverSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaThermal inertia soil water content sparse vegetation cloudinessVegetationGeographySpectroradiometerSoil waterSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliShortwave radiationTransectSurface waterWater contentSettore ICAR/06 - Topografia E CartografiaRemote sensingSPIE Proceedings
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A study on recovering the cloud top-height from infra-red video sequences

2004

In this paper we present some preliminary results on an opticalfow based technique aimed at recovering the cloud-top height from infra-red image sequences. The recovery of the cloud-top height from satellite infra-red images is an important topic in meteorological studies, and is traditionally based on the analysis of the temperature maps. In this work we explore the feasibility for this problem of a technique based on a robust multi-resolution opticalfow algorithm. The robustness is achieved adopting a Least Median of Squares paradigm. The algorithm has been tested on semi-synthetic data (i.e. real data that have been synthetically warped in order to have a reliable ground truth for the mo…

Cloud properties space imagery infrared window method satellite viewing system disparity fieldSettore INF/01 - InformaticaInfraredbusiness.industryVideo sequenceCloud computingbusinessGeologyRemote sensing
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Comparison of Metrics for the Classification of Soils Under Variable Geometrical Conditions Using Hyperspectral Data

2008

International audience; The objective of this letter is to find a distance metric between reflectance spectra that is not sensitive to the variations on the soil reflectance induced by the geometry of solar-view angles. This is motivated by the fact that differences between spectra measured for the same soil under different observation and illumination configurations can lead to misclassifications. Using 26 soils of different compositions simulated with Hapke’s model and 92 soils of different compositions measured under 28 solarview angle geometries in laboratory conditions, we tested three metrics, namely, root-mean-square error, spectral angle mapper, and R2 (the coefficient of determinat…

Coefficient of determination010504 meteorology & atmospheric sciencesMean squared error0211 other engineering and technologiesSOIL IDENTIFICATION02 engineering and technologySolid modeling01 natural sciencesSpectral lineCLASSIFICATION[SPI]Engineering Sciences [physics]HYPERSPECTRALSurface roughnessElectrical and Electronic EngineeringComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsRemote sensingHyperspectral imagingSoil classificationGeotechnical Engineering and Engineering GeologySOLAR-VIEW ANGLESoil waterSPECTRAL LIBRARYDISTANCE METRIC[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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Using Aerial Platforms in Predicting Water Quality Parameters from Hyperspectral Imaging Data with Deep Neural Networks

2020

In near future it is assumable that automated unmanned aerial platforms are coming more common. There are visions that transportation of different goods would be done with large planes, which can handle over 1000 kg payloads. While these planes are used for transportation they could similarly be used for remote sensing applications by adding sensors to the planes. Hyperspectral imagers are one this kind of sensor types. There is need for the efficient methods to interpret hyperspectral data to the wanted water quality parameters. In this work we survey the performance of neural networks in the prediction of water quality parameters from remotely sensed hyperspectral data in freshwater basin…

Coefficient of determinationArtificial neural networkRemote sensing applicationvesien tilaspektrikuvausHyperspectral imagingneuroverkotvedenlaatuConvolutional neural networkwater qualityPearson product-moment correlation coefficientsymbols.namesakeremote sensinghyperspectralilmakuvakartoitusMultilayer perceptronconvolutional neural networkssymbolsEnvironmental scienceWater qualitykaukokartoitusRemote sensing
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Comparison of metrics to remove the influence of geometrical conditions on soil reflectance

2007

The objective of this work is to find the best metric to ignore the variations of soil reflectance induced by the solar-view angles geometry. Differences between spectra measured for the same soil under different observation and illumination configurations can leads to misclassifications. Using ninety two soils of different composition measured under twenty eight solar- view angles geometries, we tested 3 metrics : RMSE, SAM, R2 (the coefficient of determination) and we compared their performances. The best metric seems to be the coefficient of determination with 93 % of good classifications.

Coefficient of determinationMean squared errorSoil waterMultispectral imageMetric (mathematics)Surface roughnessHyperspectral imagingReflectivityRemote sensingMathematics2007 IEEE International Geoscience and Remote Sensing Symposium
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Prediction of soil texture distributions using VNIR-SWIR reflectance spectroscopy

2013

Abstract Reflectance spectroscopy provides an alternate method to non-destructively characterize key soil properties. Different approaches, including chemometrics techniques or specific absorption features, have been proposed to estimate soil properties from visible and near-infrared (VNIR, 400-1200 nm) and shortwave infrared (SWIR, 1200-2500 nm) reflectance domains. The main goal of this study was to test the performance of two distinct methods for soil texture estimation by VNIR-SWIR reflectance measurements: i) the Continuum Removal (CR) technique that was used to correlate specific spectral absorption features with clay, silt and sand content, and ii) the Partial Least-Squares Regressio…

Coefficient of determinationSoil testPartial Least Squares RegressionSoil textureReflectance spectroscopySettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaMineralogySiltVNIRChemometricsContinuum RemovalSpectroradiometerSoil texturePartial least squares regressionGeneral Earth and Planetary SciencesEnvironmental scienceSettore AGR/08 - Idraulica Agraria E Sistemazioni Idraulico-ForestaliGeneral Environmental ScienceRemote sensing
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