Search results for "Biotechnology"

showing 10 items of 5678 documents

Comparison of gap-filling techniques applied to the CCI soil moisture database in Southern Europe

2021

Abstract Soil moisture (SM) is a key variable that plays an important role in land-atmosphere interactions. Monitoring SM is crucial for many applications and can help to determine the impact of climate change. Therefore, it is essential to have continuous and long-term databases for this variable. Satellite missions have contributed to this; however, the continuity of the series is compromised due to the data gaps derived by different factors, including revisit time, presence of seasonal ice or Radio Frequency Interference (RFI) contamination. In this work, the applicability of different gap-filling techniques is evaluated on the ESA Climate Change Initiative (CCI) SM combined product, whi…

010504 meteorology & atmospheric sciencesDatabaseCorrelation coefficient0208 environmental biotechnologySoil ScienceGeology02 engineering and technologycomputer.software_genre01 natural sciencesNormalized Difference Vegetation Index020801 environmental engineeringRandom forestSupport vector machineAutoregressive modelPrincipal component analysisPotential evaporationComputers in Earth Sciencescomputer0105 earth and related environmental sciencesMathematicsInterpolationRemote sensingRemote Sensing of Environment
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Remote sensing of solar-induced chlorophyll fluorescence (SIF) in vegetation: 50 years of progress

2019

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is a rapidly advancing front in terrestrial vegetation science, with emerging capability in space-based methodologies and diverse application prospects. Although remote sensing of SIF – especially from space – is seen as a contemporary new specialty for terrestrial plants, it is founded upon a multi-decadal history of research, applications, and sensor developments in active and passive sensing of chlorophyll fluorescence. Current technical capabilities allow SIF to be measured across a range of biological, spatial, and temporal scales. As an optical signal, SIF may be assessed remotely using high-resolution spectral sensors in …

010504 meteorology & atmospheric sciencesFIS/06 - FISICA PER IL SISTEMA TERRA E PER IL MEZZO CIRCUMTERRESTRE0208 environmental biotechnologySoil ScienceReview02 engineering and technologyPhotochemical Reflectance Index01 natural sciencesArticleGEO/11 - GEOFISICA APPLICATASIF retrieval methodsRadiative transfer modellingRadiative transfer910 Geography & travelComputers in Earth SciencesChlorophyll fluorescence1111 Soil Science1907 GeologyAirborne instruments0105 earth and related environmental sciencesRemote sensingStress detectionGEO/12 - OCEANOGRAFIA E FISICA DELL'ATMOSFERA1903 Computers in Earth SciencesPrimary productionGeologyVegetationPassive optical techniquesField (geography)020801 environmental engineeringGEO/10 - GEOFISICA DELLA TERRA SOLIDA10122 Institute of GeographySun-induced fluorescenceRemote sensing (archaeology)Sun-induced fluorescence Steady-state photosynthesis Stress detection Radiative transfer modelling SIF retrieval methods. Satellite sensors Airborne instruments Applications Terrestrial vegetation Passive optical techniques. ReviewApplicationsTerrestrial vegetationEnvironmental scienceSatelliteSteady-state photosynthesisSatellite sensors
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Validation of HF radar sea surface currents in the Malta-Sicily Channel

2019

Abstract A network of High-Frequency radar (HFR) stations runs operationally in the Malta-Sicily Channel (MSC), Central Mediterranean Sea, providing sea surface current maps with high temporal (1 h) and spatial (3 × 3 km) resolutions since August 2012. Comparisons with surface drifter data and near-surface Acoustic Doppler Current Profiler (ADCP) observations, as well as radar site-to-site baseline analyses, provide quantitative assessments of HFR velocities accuracy. Twenty-two drifters were deployed within the HFR domain of coverage between December 2012 and October 2013. Additionally, six ADCP vertical current profiles were collected at selected positions during a dedicated field survey.…

010504 meteorology & atmospheric sciencesFrequency band0208 environmental biotechnologySoil Science02 engineering and technologySurface current01 natural scienceslaw.inventionAcoustic Doppler current profilerlawCurrent meter measurementHfr cellComputers in Earth SciencesRadar0105 earth and related environmental sciencesRemote sensingHF radarSettore ICAR/02 - Costruzioni Idrauliche E Marittime E IdrologiaData qualityGeology020801 environmental engineeringCurrent (stream)DrifterDrifter measurementSettore ICAR/06 - Topografia E CartografiaGeologyCommunication channelInterpolationRemote Sensing of Environment
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High spatio- temporal resolution land surface temperature mission - a copernicus candidate mission in support of agricultural monitoring

2018

International audience; Evolution in the Copernicus Space Component (CSC) is foreseen in the mid-2020s to meet priority Copernicus user needs not addressed by the existing infrastructure, and/or to reinforce services by monitoring capability in the thematic domains of CO 2 , polar, and agriculture/forestry. This evolution will be synergetic with the enhanced continuity of services for the next generation of CSC. The “High Spatio-Temporal Resolution Land Surface Temperature Monitoring (LSTM) Mission”, identified as one of the CSC Expansion High Priority Candidate Missions (HPCM), currently undergoes an ESA preparatory phase (phase A/B1) study to establish mission feasibility. The LSTM missio…

010504 meteorology & atmospheric sciencesLand surface temperatureComputer sciencebusiness.industry[SDV]Life Sciences [q-bio]0208 environmental biotechnology02 engineering and technology01 natural sciences020801 environmental engineeringWater resourcesThematic map13. Climate actionAgricultureComponent (UML)Temporal resolution[SDE]Environmental SciencesSystems engineeringbusiness0105 earth and related environmental sciencesCopernicus
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Half a century of forest cover change along the Latvian-Russian border captured by object-based image analysis of Corona and Landsat TM/OLI data

2020

Abstract After 1991, major events, such as the collapse of socialism and the transition to market economies, caused land use change across the former USSR and affected forests in particular. However, major land use changes may have occurred already during Soviet rule, but those are largely unknown and difficult to map for large areas because 30-m Landsat data is not available prior to the 1980s. Our goal was to analyze the rates and determinants of forest cover change from 1967 to 2015 along the Latvian-Russian border, and to develop an object-based image analysis approach to compare forest cover based on declassified Corona spy satellite images from 1967 with that derived from Landsat 5 TM…

010504 meteorology & atmospheric sciencesLand usebusiness.industryIntensive farming0208 environmental biotechnologySoil ScienceGeology02 engineering and technologyLand cover01 natural sciences020801 environmental engineeringGeographyAgricultureAfforestationLand use land-use change and forestryPhysical geographyComputers in Earth SciencesAgricultural productivityScale (map)business0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Quantifying vegetation biophysical variables from the Sentinel-3/FLEX tandem mission: Evaluation of the synergy of OLCI and FLORIS data sources

2020

The ESA’s forthcoming FLuorescence EXplorer (FLEX) mission is dedicated to the global monitoring of the vegetation’s chlorophyll fluorescence by means of an imaging spectrometer, FLORIS. In order to properly interpret the fluorescence signal in relation to photosynthetic activity, essential vegetation variables need to be retrieved concomitantly. FLEX will fly in tandem with Sentinel-3 (S3), which conveys the Ocean and Land Colour Instrument (OLCI) that is designed to characterize the atmosphere and the terrestrial vegetation at a spatial resolution of 300 m. In this work we present the retrieval models of four essential biophysical variables: (1) Leaf Area Index (LAI), (2) leaf chlorophyll…

010504 meteorology & atmospheric sciencesMean squared error0208 environmental biotechnologyImaging spectrometerSoil ScienceGeology02 engineering and technologyVegetationSpectral bands15. Life on land01 natural sciencesArticle020801 environmental engineeringPhotosynthetically active radiationKrigingEnvironmental scienceComputers in Earth SciencesLeaf area indexImage resolution0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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Hybrid inversion of radiative transfer models based on high spatial resolution satellite reflectance data improves fractional vegetation cover retrie…

2021

In forest landscapes affected by fire, the estimation of fractional vegetation cover (FVC) from remote sensing data using radiative transfer models (RTMs) enables to evaluate the ecological impact of such disturbance across plant communities at different spatio-temporal scales. Even though, when landscapes are highly heterogeneous, the fine-scale ground spatial variation might not be properly captured if FVC products are provided at moderate or coarse spatial scales, as typical of most of operational Earth observing satellite missions. The objective of this study was to evaluate the potential of a RTM inversion approach for estimating FVC from satellite reflectance data at high spatial reso…

010504 meteorology & atmospheric sciencesMean squared error0208 environmental biotechnologySoil Science02 engineering and technology01 natural sciencesArticleWorldView-3Radiative transferComputers in Earth SciencesImage resolution0105 earth and related environmental sciencesRemote sensingFractional vegetation coverForest fireGeologyInversion (meteorology)15. Life on landEcología. Medio ambienteRadiative transfer modeling020801 environmental engineering13. Climate actionGround-penetrating radarEnvironmental scienceSatelliteSpatial variabilitySentinel-2Scale (map)Remote Sensing of Environment
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Crop specific algorithms trained over ground measurements provide the best performance for GAI and fAPAR estimates from Landsat-8 observations

2021

Abstract Estimation of Green Area Index (GAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) from decametric satellites was investigated in this study using a large database of ground measurements over croplands. It covers six main crop types including rice, corn, wheat and barley, sunflower, soybean and other types of crops. Ground measurements were completed using either digital hemispherical cameras, LAI-2000 or AccuPAR devices over sites representative of a decametric pixel. Sites were spread over the globe and the data collected at several growth stages concurrently to the acquisition of Landsat-8 images. Several machine learning techniques were investigated to re…

010504 meteorology & atmospheric sciencesMean squared errorArtificial neural networkCalibration (statistics)0208 environmental biotechnologyEmpirical modellingSoil ScienceGeology02 engineering and technology01 natural sciencesNormalized Difference Vegetation Index020801 environmental engineeringSupport vector machineData pointKrigingComputers in Earth SciencesAlgorithm0105 earth and related environmental sciencesRemote sensingMathematicsRemote Sensing of Environment
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2016

Gianluca Tramontana was supported by the GEOCARBON EU FP7 project (GA 283080). Dario Papale, Martin Jung and Markus Reichstein acknowledge funding from the EU FP7 project GEOCARBON (grant agreement no. 283080) and the EU H2020 BACI project (grant agreement no. 640176). Gustau Camps-Valls wants to acknowledge the support by an ERC Consolidator Grant with grant agreement 647423 (SEDAL). Kazuhito Ichii was supported by Environment Research and Technology Development Funds (2-1401) from the Ministry of the Environment of Japan and the JAXA Global Change Observation Mission (GCOM) project (no. 115). Christopher R. Schwalm was supported by National Aeronautics and Space Administration (NASA) gran…

010504 meteorology & atmospheric sciencesMeteorologyFLUXNET0208 environmental biotechnology0207 environmental engineeringlcsh:Life02 engineering and technologySensible heatAtmospheric sciences7. Clean energy01 natural sciencesFlux (metallurgy)FluxNetMachine learning; Carbon fluxes; Energy fluxes; FLUXNET; Remote sensing; FLUXCOMlcsh:QH540-549.5Latent heatMachine learningCarbon fluxes020701 environmental engineeringEcology Evolution Behavior and Systematics0105 earth and related environmental sciencesEarth-Surface ProcessesFLUXCOMMultivariate adaptive regression splineslcsh:QE1-996.5Empirical modellingPrimary production15. Life on landRemote sensingEnergy fluxes020801 environmental engineeringlcsh:Geologylcsh:QH501-531Kernel method13. Climate actionEnvironmental sciencelcsh:EcologyBiogeosciences
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Enhancing the retrieval of stream surface temperature from Landsat data

2019

International audience; Thermal images of water bodies often show a radiance gradient perpendicular to the banks. This effect is frequently due to mixed land and water thermal pixels. In the case of the Landsat images, radiance mixing can also affect pure water pixels due the cubic convolution resampling of the native thermal measurements. Some authors recommended a general-purpose margin of two thermal pixels to the banks or a minimum river width of three pixels, to avoid near bank effects in water temperature retrievals. Given the relatively course spatial resolution of satellite thermal sensors, the three pixel margin severely restricts their application to temperature mapping in many ri…

010504 meteorology & atmospheric sciencesPixel0208 environmental biotechnologySoil ScienceGeologyImage processing02 engineering and technology01 natural sciencesSubpixel rendering6. Clean water020801 environmental engineering[SDE]Environmental SciencesThermalRadianceEnvironmental scienceSatelliteSatellite imageryComputers in Earth SciencesRiver surface temperature Landsat 8 thermal band Thermal spatial resolution Cubic convolution resampling Thermal impact Mequinenza reservoir Ebro river Thermal stratificationImage resolution0105 earth and related environmental sciencesRemote sensingRemote Sensing of Environment
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