LEADER 05158nam 2201213z- 450 001 9910595067603321 005 20220916 035 $a(CKB)5680000000080856 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/92052 035 $a(oapen)doab92052 035 $a(EXLCZ)995680000000080856 100 $a20202209d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRemote Sensing of Biophysical Parameters 210 $aBasel$d2022 215 $a1 online resource (274 p.) 311 08$a3-0365-4902-1 311 08$a3-0365-4901-3 330 $aVegetation 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). 606 $aResearch & information: general$2bicssc 610 $a6SV 610 $aactive learning 610 $aagriculture 610 $aairborne laser scanning (ALS) 610 $aartificial neural networks 610 $aASD Field Spec 610 $abiophysical parameters (LAI 610 $aburn severity 610 $acanopy chlorophyll content 610 $acanopy loss 610 $acanopy water content 610 $aCCC 610 $aclimate data records (CDR) 610 $aclumping index (CI) 610 $aDiscrete Anisotropic Radiative Transfer (DART) model 610 $aEnMAP 610 $aequivalent water thickness 610 $aFAPAR 610 $aFAPAR) 610 $afluorescence 610 $aforest 610 $afraction of photosynthetically active radiation absorbed by vegetation (FPAR) 610 $aFVC 610 $aGPR 610 $ahyperspectral 610 $ain vivo 610 $aINFORM 610 $ainvasive vegetation 610 $aLAI 610 $aLandsat 8 610 $aLaSRC 610 $aLCC 610 $alead ions 610 $aleaf area index 610 $aleaf area index (LAI) 610 $aLEDAPS 610 $amachine learning 610 $ameteosat second generation (MSG) 610 $aModerate Resolution Imaging Spectroradiometer (MODIS) 610 $aMODIS 610 $amultispectral sensor 610 $aNDVI 610 $aPROSAIL 610 $arapeseed crop 610 $aremote sensing indices 610 $ariparian 610 $aSAIL 610 $aSatellite Application Facility for Land Surface Analysis (LSA SAF) 610 $aSentinel-2 610 $aSEVIRI 610 $asite-specific farming 610 $asoil albedo 610 $aspaceborne laser scanning (SLS) 610 $aspectrometry 610 $aspectroscopy 610 $aSREM 610 $astochastic spectral mixture model (SSMM) 610 $asurface reflectance 610 $aterrestrial laser scanning (TLS) 610 $athe fraction of radiation absorbed by photosynthetic components (FAPARgreen) 610 $athree-dimensional radiative transfer model (3D RTM) 610 $atriple-source 610 $auncertainty assessment 610 $aunmanned aircraft vehicle 610 $avegetation indices 610 $avegetation radiative transfer model 610 $avertical foliage profile (VFP) 610 $awildfire 610 $awoody area index (WAI) 615 7$aResearch & information: general 700 $aGarcía-Haro$b Francisco Javier$4edt$01324803 702 $aFang$b Hongliang$4edt 702 $aCampos-Taberner$b Manuel$4edt 702 $aGarcía-Haro$b Francisco Javier$4oth 702 $aFang$b Hongliang$4oth 702 $aCampos-Taberner$b Manuel$4oth 906 $aBOOK 912 $a9910595067603321 996 $aRemote Sensing of Biophysical Parameters$93036325 997 $aUNINA