0000000000352927

AUTHOR

Lei Fan

showing 10 related works from this author

Assessment and inter-comparison of recently developed/reprocessed microwave satellite soil moisture products using ISMN ground-based measurements

2019

Soil moisture (SM) is a key state variable in understanding the climate system through its control on the land surface energy, water budget partitioning, and the carbon cycle. Monitoring SM at regional scale has become possible thanks to microwave remote sensing. In the past two decades, several satellites were launched carrying on board either radiometer (passive) or radar (active) or both sensors in different frequency bands with various spatial and temporal resolutions. Soil moisture algorithms are in rapid development and their improvements/revisions are ongoing. The latest SM retrieval products and versions of products that have been recently released are not yet, to our knowledge, com…

TechnologyPassive microwave remote sensing010504 meteorology & atmospheric sciences0208 environmental biotechnologyActive microwave remote sensingReview02 engineering and technology01 natural sciences7. Clean energylaw.inventionRemote SensinglawRadarEvaluationComputingMilieux_MISCELLANEOUSevaluationGeologypassive microwave remote sensingDATA SETSLife Sciences & Biomedicineactive microwave remote sensingSMOSLAND SURFACESreviewSoil ScienceClimate changeEnvironmental Sciences & EcologyLand coverVALIDATIONRETRIEVALSInternational soil moisture networkComputers in Earth SciencesImaging Science & Photographic Technology[SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces environment0105 earth and related environmental sciencesRemote sensingScience & TechnologyRadiometerAMSR-ESMAPScatterometerinternational soil moisture network020801 environmental engineeringCLIMATEASCAT13. Climate actionSoil waterEnvironmental scienceSpatial variabilitySatelliteSoil moisturesoil moistureEnvironmental SciencesL-BANDRemote Sensing of Environment
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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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Calibration strategy of the JUNO experiment

2021

We present the calibration strategy for the 20 kton liquid scintillator central detector of the Jiangmen Underground Neutrino Observatory (JUNO). By utilizing a comprehensive multiple-source and multiple-positional calibration program, in combination with a novel dual calorimetry technique exploiting two independent photosensors and readout systems, we demonstrate that the JUNO central detector can achieve a better than 1% energy linearity and a 3% effective energy resolution, required by the neutrino mass ordering determination. [Figure not available: see fulltext.]

Nuclear and High Energy PhysicsPhysics - Instrumentation and DetectorsPhysics::Instrumentation and Detectorsmeasurement methodsscintillation counter: liquidenergy resolutionFOS: Physical sciencesPhotodetectorScintillator53001 natural sciencesNOHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)hal-03022811PE2_2Optics0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Calibrationlcsh:Nuclear and particle physics. Atomic energy. Radioactivityddc:530[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]010306 general physicsAstrophysiqueJiangmen Underground Neutrino ObservatoryPhysicsJUNOliquid [scintillation counter]010308 nuclear & particles physicsbusiness.industrySettore FIS/01 - Fisica SperimentaleDetectorAstrophysics::Instrumentation and Methods for AstrophysicsLinearityInstrumentation and Detectors (physics.ins-det)calibrationNeutrino Detectors and Telescopes (experiments)lcsh:QC770-798High Energy Physics::ExperimentNeutrinobusinessEnergy (signal processing)Journal of High Energy Physics
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The Design and Sensitivity of JUNO's scintillator radiopurity pre-detector OSIRIS

2021

The European physical journal / C 81(11), 973 (2021). doi:10.1140/epjc/s10052-021-09544-4

Liquid scintillatorPhysics - Instrumentation and DetectorsPhysics and Astronomy (miscellaneous)Physics::Instrumentation and Detectorsscintillation counter: liquidmeasurement methodsQC770-798Astrophysics01 natural sciencesthorium: nuclidedesign [detector]neutrinoRadioactive purityPhysicsLow energy neutrinoJUNOliquid [scintillation counter]biologySettore FIS/01 - Fisica SperimentaleDetectorInstrumentation and Detectors (physics.ins-det)3. Good healthQB460-466Physics::Space Physicsnuclide [uranium]FOS: Physical sciencesScintillatornuclide [thorium]530NONuclear physicsPE2_2uranium: nuclideNuclear and particle physics. Atomic energy. Radioactivity0103 physical sciencesddc:530Sensitivity (control systems)[PHYS.PHYS.PHYS-INS-DET]Physics [physics]/Physics [physics]/Instrumentation and Detectors [physics.ins-det]010306 general physicsJUNO neutrino physics liquid scintillatorEngineering (miscellaneous)background: radioactivitydetector: designMeasurement method010308 nuclear & particles physicsradioactivity [background]biology.organism_classificationsensitivityHigh Energy Physics::ExperimentReactor neutrinoOsiris
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Radioactivity control strategy for the JUNO detector

2021

JUNO is a massive liquid scintillator detector with a primary scientific goal of determining the neutrino mass ordering by studying the oscillated anti-neutrino flux coming from two nuclear power plants at 53 km distance. The expected signal anti-neutrino interaction rate is only 60 counts per day, therefore a careful control of the background sources due to radioactivity is critical. In particular, natural radioactivity present in all materials and in the environment represents a serious issue that could impair the sensitivity of the experiment if appropriate countermeasures were not foreseen. In this paper we discuss the background reduction strategies undertaken by the JUNO collaboration…

Nuclear and High Energy PhysicsPhysics - Instrumentation and DetectorsPhysics::Instrumentation and DetectorsNuclear engineeringMonte Carlo methodControl (management)measurement methodsFOS: Physical sciencesQC770-798Scintillator7. Clean energy01 natural sciencesNOPE2_2Nuclear and particle physics. Atomic energy. Radioactivity0103 physical sciences[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]ddc:530Sensitivity (control systems)010306 general physicsPhysicsJUNOliquid [scintillation counter]010308 nuclear & particles physicsbusiness.industryDetectorSettore FIS/01 - Fisica Sperimentaleradioactivity [background]suppression [background]Instrumentation and Detectors (physics.ins-det)Monte Carlo [numerical calculations]Nuclear powerthreshold [energy]sensitivityNeutrino Detectors and Telescopes (experiments)GEANTNeutrinobusinessEnergy (signal processing)
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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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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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JUNO sensitivity to low energy atmospheric neutrino spectra

2021

Atmospheric neutrinos are one of the most relevant natural neutrino sources that can be exploited to infer properties about cosmic rays and neutrino oscillations. The Jiangmen Underground Neutrino Observatory (JUNO) experiment, a 20 kton liquid scintillator detector with excellent energy resolution is currently under construction in China. JUNO will be able to detect several atmospheric neutrinos per day given the large volume. A study on the JUNO detection and reconstruction capabilities of atmospheric $\nu_e$ and $\nu_\mu$ fluxes is presented in this paper. In this study, a sample of atmospheric neutrino Monte Carlo events has been generated, starting from theoretical models, and then pro…

Physics and Astronomy (miscellaneous)Physics::Instrumentation and Detectorsscintillation counter: liquidenergy resolutionAtmospheric neutrinoQC770-798Astrophysics7. Clean energy01 natural sciencesneutrino: fluxHigh Energy Physics - ExperimentHigh Energy Physics - Experiment (hep-ex)particle source [neutrino]neutrinoneutrino: atmosphere[PHYS.HEXP]Physics [physics]/High Energy Physics - Experiment [hep-ex]Cherenkovneutrino/e: particle identificationenergy: low [neutrino]Jiangmen Underground Neutrino ObservatoryPhysicsJUNOphotomultiplierliquid [scintillation counter]primary [neutrino]neutrino: energy spectrumDetectoroscillation [neutrino]neutrinosMonte Carlo [numerical calculations]atmosphere [neutrino]QB460-466observatorycosmic radiationComputer Science::Mathematical Softwareproposed experimentNeutrinonumerical calculations: Monte CarloComputer Science::Machine LearningParticle physicsdata analysis methodAstrophysics::High Energy Astrophysical PhenomenaFOS: Physical sciencesCosmic rayScintillatorComputer Science::Digital LibrariesNOStatistics::Machine LearningPE2_2neutrino: primaryneutrino: spectrumNuclear and particle physics. Atomic energy. Radioactivity0103 physical sciencesddc:530structure010306 general physicsNeutrino oscillationEngineering (miscellaneous)Cherenkov radiationparticle identification [neutrino/mu]Scintillationneutrino/mu: particle identificationflavordetectorparticle identification [neutrino/e]010308 nuclear & particles physicsneutrino: energy: lowHigh Energy Physics::Phenomenologyspectrum [neutrino]resolutionenergy spectrum [neutrino]flux [neutrino]neutrino: particle source13. Climate actionHigh Energy Physics::Experimentneutrino: oscillationneutrino detector
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Interannual Variability of Biomass (SMOS Vegetation Optical Depth) Over the Contiguous United States

2021

Interannual variability in biomass represented by SMOS vegetation optical depth (VOD) and precipitation was assessed over the Contiguous United States. The greatest interannual variability in both VOD and precipitation occurred in shrubs and herbaceous (grasslands), with forests the least variable. At a continental scale, VOD was strongly correlated with annual precipitation. Results showed a significant correlation coefficient (∼ 0.93) between interannual variability of precipitation and biomass, indicating that the interannual variability of precipitation could be a good predictor of the interannual variability of biomass.

Biomass (ecology)Vegetation optical depthCorrelation coefficientfood and beveragesEnvironmental sciencePrecipitationVegetationHerbaceous plantAtmospheric sciencescomplex mixtures2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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Global Scale IB AMSR2 Vegetation Optical Depth at X-Band

2021

Vegetation Optical Depth (VOD) plays an increasingly important role in studying global carbon, water and energy transformation [1], [2]. This study explores the performance of the X-MEB (X-band microwave emission of the biosphere) model at global scale. Similar to the L-MEB model, the X-MEB model, built by INRAE (Institut national de recherche pour l'agriculture, l'alimentation et l'environnement) Bordeaux, aims to retrieve VOD (referred to as IB X-VOD) at X-band. To avoid the ill-posed problem caused by retrieving two parameters of interest (soil moisture (SM) and VOD) from mono-angular and dual-polarized observations (AMSR2), which are strongly correlated, we used the ERA5 SM product as a…

Biomass (ecology)Scale (ratio)BiosphereEnvironmental scienceVegetationLeaf area indexAlbedoAtmospheric sciencesWater contentNormalized Difference Vegetation Index2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
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