Search results for "Vegetation optical depth"

showing 9 items of 19 documents

Time-variations of zeroth-order vegetation absorption and scattering at L-band

2021

Abstract Surface soil moisture and vegetation optical depth (VOD), as an indicator of vegetation wet biomass, from passive microwave remote sensing have been increasingly applied in global ecology and climate research. Both soil moisture and VOD are retrieved from satellite brightness temperature measurements assuming a zeroth order radiative transfer model, commonly known as the tau-omega model. In this model the emission of a vegetated surface is dependent on soil moisture, vegetation absorption and vegetation scattering. Vegetation scattering is normally represented by the single scattering albedo, ω, and is commonly assumed to be a time-invariant calibration parameter to achieve high ac…

LidarScatteringSingle-scattering albedoAttenuationeffective scattering albedoSoil ScienceGeologySoil scienceContext (language use)SMAPradiometryVegetationvegetation optical depthICESat-2L-bandAtmospheric radiative transfer codesBrightness temperaturerelative canopy scatteringEnvironmental scienceComputers in Earth SciencesAbsorption (electromagnetic radiation)relative canopy absorptionRemote sensingRemote Sensing of Environment
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Monitoring tropical forests under a functional perspective with satellite-based vegetation optical depth.

2020

Monitoring ecosystem functions in forests is a priority in a climate change scenario, as climate-induced events may initially alter the functions more than slow-changing attributes, such as biomass. The ecosystem functional properties (EFPs) are quantities that characterize key ecosystem processes. They can be derived by point observations of gas and energy exchanges between the ecosystems and the atmosphere that are collected globally at FLUXNET flux tower sites and upscaled at ecosystem level. The properties here considered describe the ability of ecosystems to optimize the use of resources for carbon uptake. They represent functional forest information, are dependent on environmental dri…

Settore ING-INF/02010504 meteorology & atmospheric sciencesClimate Changeecosystem functional properties0211 other engineering and technologiesFluxClimate changeGPPmax02 engineering and technologyForestsAtmospheric sciences01 natural sciencesvegetation optical depthTreesFluxNetClimate change scenarioLUEEnvironmental ChemistryEcosystemWater contentEcosystem021101 geological & geomatics engineering0105 earth and related environmental sciencesGeneral Environmental ScienceGlobal and Planetary ChangeBiomass (ecology)Ecology15. Life on landSouth America13. Climate actionAfricaEnvironmental scienceSatelliteGlobal change biologyREFERENCES
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Synergistic integration of optical and microwave satellite data for crop yield estimation

2019

Developing accurate models of crop stress, phenology and productivity is of paramount importance, given the increasing need of food. Earth observation (EO) remote sensing data provides a unique source of information to monitor crops in a temporally resolved and spatially explicit way. In this study, we propose the combination of multisensor (optical and microwave) remote sensing data for crop yield estimation and forecasting using two novel approaches. We first propose the lag between Enhanced Vegetation Index (EVI) derived from MODIS and Vegetation Optical Depth (VOD) derived from SMAP as a new joint metric combining the information from the two satellite sensors in a unique feature or des…

Signal Processing (eess.SP)FOS: Computer and information sciencesEarth observationCoefficient of determinationTeledetecció010504 meteorology & atmospheric sciencesEnhanced vegetation index0208 environmental biotechnologyFOS: Physical sciencesSoil Science02 engineering and technologyStatistics - Applications01 natural sciencesArticleModerate resolution imaging spectroradiometer (MODIS)Robustness (computer science)Machine learningLinear regressionFOS: Electrical engineering electronic engineering information engineeringFeature (machine learning)Kernel ridge regressionCrop yield estimationVegetation optical depthApplications (stat.AP)Electrical Engineering and Systems Science - Signal ProcessingComputers in Earth Sciences0105 earth and related environmental sciencesRemote sensingMathematics2. Zero hungerCrop yieldProcessos estocàsticsGeologyEnhanced vegetation indexAgro-ecosystems020801 environmental engineeringPhysics - Data Analysis Statistics and ProbabilityMetric (mathematics)Soil moisture active passive (SMAP)Data Analysis Statistics and Probability (physics.data-an)Imatges Processament
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Alternate Inrae-Bordeaux VOD Indices from SMOS, AMSR2 and ASCAT: Overview of Recent Developments

2021

International audience; Vegetation optical depth (VOD) is used to parameterize microwave extinction effects within the vegetation layer. Many studies have showed VOD presents interesting features for applications in ecology, water and carbon cycles, and VOD is only marginally impacted by signal disturbances and artefacts from atmospheric, cloud and sun illumination effects. As soil moisture (and not VOD) has generally been the main factor of interest in retrieval studies from microwave observations, there is room for improvement in the retrieved VOD products. In this context, INRAE Bordeaux recently developed alternate VOD products from the SMOS, AMSR2 and ASCAT sensors, by addressing speci…

Spatial correlationVegetation optical depth[SDE.IE]Environmental Sciences/Environmental EngineeringEnvironmental scienceContext (language use)VegetationRemote sensingRadiometryMoistureRemote sensing2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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First Retrievals of ASCAT-IB VOD (Vegetation Optical Depth) at Global Scale

2021

Global and long-term vegetation optical depth (VOD) dataset are very useful to monitor the dynamics of the vegetation features, climate and environmental changes. In this study, the radar-based global ASCAT (Advanced SCATterometer) IB (INRAE-BORDEAUX) VOD was retrieved using a model which was recently calibrated over Africa. In order to assess the performance of IB VOD, the Saatchi biomass and three other VOD datasets (ASCAT V16, AMSR2 LPRM V5 and VODCA LPRM V6) derived from C-band observations were used in the comparison. The preliminary results show that IB VOD has a promising ability to predict biomass $(\mathrm{R}=0.74,\ \text{RMSE} =44.82\ \text{Mg}\ \text{ha}^{-1})$ , which is better …

Vegetation optical depth010504 meteorology & atmospheric sciencesvegetation mapping0211 other engineering and technologiesScale (descriptive set theory)02 engineering and technology01 natural sciencesCombinatoricsremote sensingvegetationoptical sensorC-bandComputingMilieux_MISCELLANEOUSattenuation021101 geological & geomatics engineering0105 earth and related environmental sciencesMathematicsprediction algorithmbiomassOrder (ring theory)15. Life on landPrediction algorithmsASCAT13. Climate action[SDE]Environmental SciencesVegetation optical DepthScatterometerBiomedical optical imagingRadar Measurement
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A combined optical-microwave method to retrieve soil moisture over vegetated areas

2011

A simple approach for correcting for the effect of vegetation in the estimation of the surface soil moisture (wS) from L-band passive microwave observations is presented in this study. The approach is based on semi-empirical relationships between soil moisture and the polarized reflectivity including the effect of the vegetation optical depth which is parameterized as a function of the normalized vegetation difference index (NDVI). The method was tested against in situ measurements collected over a grass site from 2004 to 2007 (SMOSREX experiment). Two polarizations (horizontal/vertical) and five incidence angles (20◦, 30◦, 40◦, 50◦, and 60◦) were considered in the analysis. The best wS est…

Vegetation optical depthL band010504 meteorology & atmospheric sciencesNDVItélédétection0211 other engineering and technologiesSoil science02 engineering and technologyMicrowave methodsurface temperature01 natural sciencesNormalized Difference Vegetation Index[SDV.EE.ECO]Life Sciences [q-bio]/Ecology environment/EcosystemsNDVI;LAI;LEAF AREA INDEX;SURFACE TEMPERATURE;SOIL MOISTURE;L-BAND medicineTraitement du signal et de l'imagenormalized vegetation difference index (NDVI)Electrical and Electronic EngineeringWater contentComputingMilieux_MISCELLANEOUS021101 geological & geomatics engineering0105 earth and related environmental sciencesRemote sensingSignal and Image processingsurface temperature.soil moisture (SM)Enhanced vegetation index15. Life on landLAIL-bandSOIL MOISTUREGeneral Earth and Planetary SciencesEnvironmental sciencemicrowave radiometrymedicine.symptomLEAF AREA INDEXVegetation (pathology)[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processingMicrowave
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Global Long-Term Brightness Temperature Record from L-Band SMOS and Smap Observations

2021

Passive microwave remote sensing observations at L-band provide key and global information on surface soil moisture (SM) and vegetation optical depth (VOD), which are related to the Earth water and carbon cycles. Only two spaceborne L-band sensors are currently operating: SMOS, launched end of 2009 and thus providing now a 11-year global dataset and SMAP, launched beginning of 2015. To ensure SM and L-VOD data continuity in the event of failure of one of the space-borne SMOS or SMAP sensors, we developed a consistent brightness temperature (TB) record by first producing consistent 40° SMOS and SMAP TB estimates based on SMOS-IC and SMAP enhanced data resp., and then fusing them via linear f…

Vegetation optical depthL bandData continuityBrightness temperatureEnvironmental scienceMicrowave remote sensingOptical polarizationSensor fusionTerm (time)Remote sensing2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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SMAP Multi-Temporal vegetation optical depth retrieval as an indicator of crop yield trends and crop composition

2017

Vegetation Optical Depth (VOD) is related to Vegetation Water Content (VWC). This provides new and highly valuable information for ecological and agricultural studies. In this work, VOD from the Soil Moisture Active-Passive (SMAP) satellite has been retrieved with the new Multi-Temporal Dual-Channel Algorithm (MT-DCA). Then, it has been applied to the study of crop yield trends and crop composition. The increase on VOD (¿VOD) during crop development has been compared to yield data in two selected regions located in the United States. The first region presents a heterogeneous crop composition and weak ¿VOD-yield relationship (r2=0.21). The second region presents a highly homogenous cover and…

YieldTeledetecció010504 meteorology & atmospheric sciencesAgricultural engineering0211 other engineering and technologiesCropsSoil science:Enginyeria agroalimentària [Àrees temàtiques de la UPC]02 engineering and technology01 natural sciencesCropYield (wine)Enginyeria agronòmicaVegetation optical depthWater content021101 geological & geomatics engineering0105 earth and related environmental sciences2. Zero hungerbusiness.industrySaturation (genetic)Crop yieldPlant densitySMAPRemote sensing15. Life on land:Enginyeria de la telecomunicació::Radiocomunicació i exploració electromagnètica::Teledetecció [Àrees temàtiques de la UPC]AgricultureEnvironmental scienceSatelliteComposition (visual arts)business2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)
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SMOS-IC: An Alternative SMOS Soil Moisture and Vegetation Optical Depth Product

2017

© 2017 by the authors. The main goal of the Soil Moisture and Ocean Salinity (SMOS) mission over land surfaces is the production of global maps of soil moisture (SM) and vegetation optical depth (τ) based on multi-angular brightness temperature (TB) measurements at L-band. The operational SMOS Level 2 and Level 3 soil moisture algorithms account for different surface effects, such as vegetation opacity and soil roughness at 4 km resolution, in order to produce global retrievals of SM and τ. In this study, we present an alternative SMOS product that was developed by INRA (Institut National de la Recherche Agronomique) and CESBIO (Centre d'Etudes Spatiales de la BIOsphère). One of the main go…

environmental_sciencesL bandVegetation optical depth010504 meteorology & atmospheric sciencesNDVI[SDV]Life Sciences [q-bio]Science0211 other engineering and technologiesWeather forecasting0207 environmental engineeringSoil science02 engineering and technologycomputer.software_genre01 natural sciencesSMOS; L-band; Level 3; ECMWF; SMOS-IC; soil moisture; vegetation optical depth; MODIS; NDVINormalized Difference Vegetation IndexECMWFvegetation optical depthtempératurehumidité du solluminosity14. Life underwater020701 environmental engineeringWater content021101 geological & geomatics engineeringRemote sensing0105 earth and related environmental sciencessalinité des océansQBiosphereluminositéVegetationAlbedoL-bandSpectroradiometerMODIS13. Climate actionBrightness temperatureProduct (mathematics)General Earth and Planetary SciencesEnvironmental sciencesoil moistureSMOS;L-band;level 3;ECMWF;SMOS-IC;soil moisture;vegetation optical depth;MODIS;NDVISMOS-ICcomputerLevel 3SMOSRemote Sensing
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