Search results for "Landsat 8"

showing 5 items of 5 documents

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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Tracking marine alien macroalgae in the mediterranean sea: The contribution of citizen science and remote sensing

2021

The accelerating rate of the introduction of non-indigenous species (NIS) and the magnitude of shipping traffic make the Mediterranean Sea a hotspot of biological invasions. For the effective management of NIS, early detection and intensive monitoring over time and space are essential. Here, we present an overview of possible applications of citizen science and remote sensing in monitoring alien seaweeds in the Mediterranean Sea. Citizen science activities, involving the public (e.g., tourists, fishermen, divers) in the collection of data, have great potential for monitoring NIS. The innovative methodologies, based on remote sensing techniques coupled with in situ/laboratory advanced sampli…

0106 biological sciencesMonitoringOcean EngineeringAlienCitizen science010603 evolutionary biology01 natural scienceslcsh:OceanographyMediterranean sealcsh:VM1-989Citizen scienceMediterranean Sealcsh:GC1-1581Landsat 8 OLIWater Science and TechnologyCivil and Structural EngineeringRemote sensingnon-indigenous specie010604 marine biology & hydrobiologySettore BIO/02 - Botanica Sistematicalcsh:Naval architecture. Shipbuilding. Marine engineeringRemote sensingManagingHotspot (Wi-Fi)GeographyHabitatRemote sensing (archaeology)Sustainable managementSettore BIO/03 - Botanica Ambientale E ApplicataNon-indigenous speciesMarine protected area
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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
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Multi-Season Phenology Mapping of Nile Delta Croplands Using Time Series of Sentinel-2 and Landsat 8 Green LAI

2022

Space-based cropland phenology monitoring substantially assists agricultural managing practices and plays an important role in crop yield predictions. Multitemporal satellite observations allow analyzing vegetation seasonal dynamics over large areas by using vegetation indices or by deriving biophysical variables. The Nile Delta represents about half of all agricultural lands of Egypt. In this region, intensifying farming systems are predominant and multi-cropping rotations schemes are increasing, requiring a high temporal and spatial resolution monitoring for capturing successive crop growth cycles. This study presents a workflow for cropland phenology characterization and mapping based on…

Landsat 8Land surface phenologyGreen leaf area indexgreen leaf area index; Sentinel-2; Landsat 8; land surface phenology; Gaussian Process Regression (GPR); time series analysisGaussian Process Regression (GPR)Time series analysisGeneral Earth and Planetary SciencesMatemática AplicadaSentinel-2Remote Sensing
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Monitoring Coastal Lagoon Water Quality Through Remote Sensing: The Mar Menor as a Case Study

2019

The Mar Menor is a hypersaline coastal lagoon located in the southeast of Spain. This fragile ecosystem is suffering several human pressures, such as nutrient and sediment inputs from agriculture and other activities and decreases in salinity. Therefore, the development of an operational system to monitor its evolution is crucial to know the cause-effect relationships and preserve the natural system. The evolution and variability of the turbidity and chlorophyll-a levels in the Mar Menor water body were studied here through the joint use of remote sensing techniques and in situ data. The research was undertaken using Operational Land Imager (OLI) images on Landsat 8 and two SPOT images, bec…

Landsat 8lcsh:Hydraulic engineering010504 meteorology & atmospheric sciences2410.05 Ecología HumanaGeography Planning and DevelopmentMultispectral imageSpatio-temporal variability3308 Ingeniería y Tecnología del Medio Ambientespatio-temporal variability010501 environmental sciencesAquatic Science01 natural sciencesBiochemistryOperational systemlcsh:Water supply for domestic and industrial purposeslcsh:TC1-978EcosystemTurbidityTecnologías del Medio Ambiente0105 earth and related environmental sciencesWater Science and TechnologyRemote sensinglcsh:TD201-500Mar MenorWater bodyRemote sensing (archaeology)Environmental scienceSatelliteWater qualityEcologíalIngeniería HidráulicaWater
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