01083nam0 22002891i 450 UON0049647920231205105355.254978-89-303-1784-920190709d2018 |0itac50 bakorKR|||| 1||||Uri mal munpŏmnonKo Yŏng-gŭn, Ku Pon-gwanKyŏnggi-do P'aju-siChimmundang2018562 p.27 cmLingua coreanaGrammaticheUONC000555FIKRP'aju-si (Kyŏnggi-do)UONL005469COR II BCOREA - LINGUISTICA - GRAMMATICHEAKO Yŏng-gŭnUONV242287765585KU Pon-gwanUONV242290765586ChimmundangUONV265095650ITSOL20250523RICASIBA - SISTEMA BIBLIOTECARIO DI ATENEOUONSIUON00496479SIBA - SISTEMA BIBLIOTECARIO DI ATENEOSI COR II B 061 N SI 30752 7 061 N Uri mal munpŏmnon1556530UNIOR05158nam 2201213z- 450 991059506760332120220916(CKB)5680000000080856(oapen)https://directory.doabooks.org/handle/20.500.12854/92052(oapen)doab92052(EXLCZ)99568000000008085620202209d2022 |y 0engurmn|---annantxtrdacontentcrdamediacrrdacarrierRemote Sensing of Biophysical ParametersBasel20221 online resource (274 p.)3-0365-4902-1 3-0365-4901-3 Vegetation plays an essential role in the study of the environment through plant respiration and photosynthesis. Therefore, the assessment of the current vegetation status is critical to modeling terrestrial ecosystems and energy cycles. Canopy structure (LAI, fCover, plant height, biomass, leaf angle distribution) and biochemical parameters (leaf pigmentation and water content) have been employed to assess vegetation status and its dynamics at scales ranging from kilometric to decametric spatial resolutions thanks to methods based on remote sensing (RS) data.Optical RS retrieval methods are based on the radiative transfer processes of sunlight in vegetation, determining the amount of radiation that is measured by passive sensors in the visible and infrared channels. The increased availability of active RS (radar and LiDAR) data has fostered their use in many applications for the analysis of land surface properties and processes, thanks to their insensitivity to weather conditions and the ability to exploit rich structural and texture information. Optical and radar data fusion and multi-sensor integration approaches are pressing topics, which could fully exploit the information conveyed by both the optical and microwave parts of the electromagnetic spectrum.This Special Issue reprint reviews the state of the art in biophysical parameters retrieval and its usage in a wide variety of applications (e.g., ecology, carbon cycle, agriculture, forestry and food security).Research & information: generalbicssc6SVactive learningagricultureairborne laser scanning (ALS)artificial neural networksASD Field Specbiophysical parameters (LAIburn severitycanopy chlorophyll contentcanopy losscanopy water contentCCCclimate data records (CDR)clumping index (CI)Discrete Anisotropic Radiative Transfer (DART) modelEnMAPequivalent water thicknessFAPARFAPAR)fluorescenceforestfraction of photosynthetically active radiation absorbed by vegetation (FPAR)FVCGPRhyperspectralin vivoINFORMinvasive vegetationLAILandsat 8LaSRCLCClead ionsleaf area indexleaf area index (LAI)LEDAPSmachine learningmeteosat second generation (MSG)Moderate Resolution Imaging Spectroradiometer (MODIS)MODISmultispectral sensorNDVIPROSAILrapeseed cropremote sensing indicesriparianSAILSatellite Application Facility for Land Surface Analysis (LSA SAF)Sentinel-2SEVIRIsite-specific farmingsoil albedospaceborne laser scanning (SLS)spectrometryspectroscopySREMstochastic spectral mixture model (SSMM)surface reflectanceterrestrial laser scanning (TLS)the fraction of radiation absorbed by photosynthetic components (FAPARgreen)three-dimensional radiative transfer model (3D RTM)triple-sourceuncertainty assessmentunmanned aircraft vehiclevegetation indicesvegetation radiative transfer modelvertical foliage profile (VFP)wildfirewoody area index (WAI)Research & information: generalGarcía-Haro Francisco Javieredt1324803Fang HongliangedtCampos-Taberner ManueledtGarcía-Haro Francisco JavierothFang HongliangothCampos-Taberner ManuelothBOOK9910595067603321Remote Sensing of Biophysical Parameters3036325UNINA