Search results for " Index"

showing 10 items of 4978 documents

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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A multisensor fusion approach to improve LAI time series

2011

International audience; High-quality and gap-free satellite time series are required for reliable terrestrial monitoring. Moderate resolution sensors provide continuous observations at global scale for monitoring spatial and temporal variations of land surface characteristics. However, the full potential of remote sensing systems is often hampered by poor quality or missing data caused by clouds, aerosols, snow cover, algorithms and instrumentation problems. A multisensor fusion approach is here proposed to improve the spatio-temporal continuity, consistency and accuracy of current satellite products. It is based on the use of neural networks, gap filling and temporal smoothing techniques. …

010504 meteorology & atmospheric sciencesMeteorologytélédétectionsatellite0211 other engineering and technologiesSoil Scienceréseau neuronal02 engineering and technology01 natural sciencessuivi de culturesInstrumentation (computer programming)Computers in Earth SciencesLeaf area index021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingVegetationGeologyVegetationData fusionLAI time seriesSensor fusionMissing dataLAI time series;Vegetation;Modis;Temporal smoothing;Gap filling;Data fusionqualité des données13. Climate actionAutre (Sciences de l'ingénieur)Gap filling[SDE]Environmental SciencesEnvironmental scienceSatelliteModisTemporal smoothingScale (map)Smoothing
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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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Influencia del ángulo de observación en la estimación del índice de área foliar (LAI) mediante imágenes PROBA/CHRIS

2016

La estimación de variables biofísicas como el Índice de Área Foliar (LAI) mediante técnicas de teledetección es objeto de numerosos estudios, ya que de su conocimiento se puede extraer valiosa información sobre el estado de la vegetación. En este trabajo se estudia la estimación del LAI mediante imágenes multiangulares PROBA/CHRIS, analizando el comportamiento de la reflectividad medida en sus 5 ángulos de observación, en las longitudes de onda de 665 y 705 nm correspondientes a la banda de absorción de la clorofila y la reflectividad de la vegetación en el Red-Edge, respectivamente. El Índice de Diferencia Normalizada (NDI) calculado en estas longitudes de onda, mostró una buena correlació…

010504 meteorology & atmospheric sciencesRed-EdgeGeography Planning and Development0211 other engineering and technologieslcsh:G1-92202 engineering and technologyViewing angle01 natural sciencesReflectivityNDILAIPROBA/CHRISGeographyEarth and Planetary Sciences (miscellaneous)multiangularLeaf area indexSentinel-2lcsh:Geography (General)021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingRevista de Teledetección
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Radiance-based NIRv as a proxy for GPP of corn and soybean

2020

Abstract Substantial uncertainty exists in daily and sub-daily gross primary production (GPP) estimation, which dampens accurate monitoring of the global carbon cycle. Here we find that near-infrared radiance of vegetation (NIRv,Rad), defined as the product of observed NIR radiance and normalized difference vegetation index, can accurately estimate corn and soybean GPP at daily and half-hourly time scales, benchmarked with multi-year tower-based GPP at three sites with different environmental and irrigation conditions. Overall, NIRv,Rad explains 84% and 78% variations of half-hourly GPP for corn and soybean, respectively, outperforming NIR reflectance of vegetation (NIRv,Ref), enhanced vege…

010504 meteorology & atmospheric sciencesRenewable Energy Sustainability and the EnvironmentPublic Health Environmental and Occupational HealthPrimary productionEnhanced vegetation index010501 environmental sciencesAtmospheric sciences01 natural sciencesNormalized Difference Vegetation IndexCarbon cycleNir reflectanceLinear relationshipPhotosynthetically active radiationRadianceEnvironmental science0105 earth and related environmental sciencesGeneral Environmental ScienceEnvironmental Research Letters
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Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review

2020

Abstract Green fractional vegetation cover ( f c ) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of f c via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a compre…

010504 meteorology & atmospheric sciencesResilient Livelihoods0211 other engineering and technologies02 engineering and technologyForests01 natural sciencesNormalized Difference Vegetation IndexArticleVegetation coverAbundance (ecology)Computers in Earth SciencesAdaptationEngineering (miscellaneous)Image resolution021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingMathematicsEstimationVegetationBiodiversity15. Life on landAtomic and Molecular Physics and OpticsComputer Science ApplicationsRemote sensing (archaeology)Vegetation IndexAlgorithm
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Comparison of cloud-reconstruction methods for time series of composite NDVI data

2010

Land cover change can be assessed from ground measurements or remotely sensed data. As regards remotely sensed data, such as NDVI (Normalized Difference Vegetation Index) parameter, the presence of atmospherically contaminated data in the time series introduces some noise that may blur the change analysis. Several methods have already been developed to reconstruct NDVI time series, although most methods have been dedicated to reconstruction of acquired time series, while publicly available databases are usually composited over time. This paper presents the IDR (iterative Interpolation for Data Reconstruction) method, a new method designed to approximate the upper envelope of the NDVI time s…

010504 meteorology & atmospheric sciencesSeries (mathematics)0211 other engineering and technologiesSoil ScienceGeology02 engineering and technologyLand cover15. Life on land01 natural sciencesNormalized Difference Vegetation IndexBruit13. Climate actionCompositingmedicineEnvironmental scienceSatellite imageryNoise (video)Computers in Earth Sciencesmedicine.symptom021101 geological & geomatics engineering0105 earth and related environmental sciencesInterpolationRemote sensingRemote Sensing of Environment
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Children’s exposure to polycyclic aromatic hydrocarbons in the Valencian Region (Spain): Urinary levels, predictors of exposure and risk assessment

2021

Polycyclic aromatic hydrocarbons (PAHs) are pollutants that are released into the environment during incomplete combustion of organic matter and which can have a negative effect on human health. PAHs enter the human body mostly through ingestion of food or inhalation of tobacco smoke. The purpose of the present study is to evaluate the internal levels of PAHs that children living in the Valencian Region (Spain) are exposed to. In total, we measured eleven biomarkers of exposure to naphthalene, fluorene, phenanthrene, pyrene, and benzo(a)pyrene in the urine of 566 children aged 5-12. The analytical method was based on a liquid-liquid extraction of the PAH metabolites from the urine samples, …

010504 meteorology & atmospheric sciencesUrine010501 environmental sciencesFluoreneUrineRisk Assessment01 natural sciencesTobacco smokeReference valueschemistry.chemical_compoundEnvironmental healthBenzo(a)pyreneHumansMedicineIngestionGE1-350ChildChildren0105 earth and related environmental sciencesGeneral Environmental Sciencebusiness.industryPhenanthrenePolycyclic aromatic hydrocarbonsHuman biomonitoringEnvironmental scienceschemistrySpainPyreneEnvironmental PollutantsbusinessRisk assessmentBody mass indexBiomarkersEnvironment International
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Analysis of drought and vulnerability in the North Darfur region of Sudan

2018

North Darfur of Sudan is located on the edge of the Sahara Desert and endures frequent droughts due to water shortages and high summer temperatures. Monitoring and understanding drought characteristics are essential for integrated drought risk mitigation and prevetion of land degradation. This study evaluates drought conditions in North Darfur by analyzing the spatiotemporal distribution of drought using three drought indices (Standardized Precipitation Index, Vegetation Condition Index, and Soil Moisture Content Index) and their combined drought index (CDI) from 2004 to 2013. Biophysical and socioeconomic indicators are further used to measure vulnerability to drought risk and its three co…

010504 meteorology & atmospheric sciencesVulnerability index0208 environmental biotechnologyVulnerabilitySoil Sciencedrought02 engineering and technologyDevelopmentMonsoon01 natural sciencesremote sensingCondition indexparasitic diseasesEnvironmental Chemistrymeteorology0105 earth and related environmental sciencesGeneral Environmental ScienceAdaptive capacityfungifood and beveragesVegetationBodemfysica en LandbeheerPE&RC020801 environmental engineeringSoil Physics and Land ManagementGeographyvulnerability indexLand degradationNorth Darfur regionRisk assessmentWater resource managementLand Degradation & Development
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A high-resolution, integrated system for rice yield forecasting at district level

2019

Abstract To meet the growing demands from public and private stakeholders for early yield estimates, a high-resolution (2 km × 2 km) rice yield forecasting system based on the integration of the WARM model and remote sensing (RS) technologies was developed. RS was used to identify rice-cropped area and to derive spatially distributed sowing dates, and for the dynamic assimilation of RS-derived leaf area index (LAI) data within the crop model. The system—tested for the main European rice production districts in Italy, Greece, and Spain—performed satisfactorily; >66% of the inter-annual yield variability was explained in six out of eight combinations of ecotype × district, with a maximum of 8…

010504 meteorology & atmospheric sciencesYield (finance)Agricultural engineering01 natural sciencesCropremote sensingWARM modelOryza sativa L.CultivarLeaf area indexBlast disease0105 earth and related environmental sciences2. Zero hungerassimilationSowing04 agricultural and veterinary sciencesRemote sensingblast diseaseBlast diseaseAssimilation040103 agronomy & agriculture0401 agriculture forestry and fisheriesEnvironmental scienceAnimal Science and ZoologyAgronomy and Crop ScienceDistrict level
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