LEADER 04346nam 2201225z- 450 001 9910637779903321 005 20221206 010 $a3-0365-5484-X 035 $a(CKB)5470000001631739 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94550 035 $a(oapen)doab94550 035 $a(EXLCZ)995470000001631739 100 $a20202212d2022 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aRemote Sensing in Agriculture: State-of-the-Art 210 $aBasel$cMDPI - Multidisciplinary Digital Publishing Institute$d2022 215 $a1 online resource (220 p.) 311 08$a3-0365-5483-1 330 $aThe Special Issue on "Remote Sensing in Agriculture: State-of-the-Art" gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue. 517 $aRemote Sensing in Agriculture 606 $aEnvironmental science, engineering and technology$2bicssc 606 $aHistory of engineering and technology$2bicssc 606 $aTechnology: general issues$2bicssc 610 $aalpha angle 610 $aanisotropy 610 $aapple orchard damage 610 $abiomass 610 $aCDL 610 $acorn 610 $acrop height 610 $acrop management 610 $acrop Monitoring 610 $acrop water stress monitoring 610 $acrop yield prediction 610 $across-scale 610 $adata blending 610 $adigital number (DN) 610 $aDJI Phantom 4 Multispectral (P4M) 610 $aeconomic loss 610 $aentropy 610 $afeature selection 610 $afield phenotyping 610 $agap-filling 610 $aHidden Markov Random Field 610 $aHMRF 610 $ahyperspectral imaging 610 $ainsurance support 610 $aLandsat 610 $alodging 610 $aMODIS 610 $anorthern Mongolia 610 $aoasis crop type mapping 610 $aParrot Sequoia (Sequoia) 610 $aplant disease detection 610 $apolarimetric decomposition 610 $aprecision agriculture (PA) 610 $arandom forest (RF) 610 $arecursive feature increment (RFI) 610 $ared-edge spectral bands and indices 610 $areflectance 610 $aremote sensing (RS) 610 $aremote sensing indices 610 $aSAR 610 $aSentinel-1 610 $aSentinel-1 and 2 integration 610 $asoil moisture Karnataka India 610 $asoil moisture semi-empirical model 610 $asoybean 610 $aspatial resolution 610 $aspectral angle mapper 610 $aspring wheat 610 $astatistically homogeneous pixels (SHPs) 610 $astorm damage mapping 610 $asupport vector machine 610 $asupport vector regression 610 $aSynthetic Aperture Radar 610 $asynthetic aperture radar (SAR) 610 $athermal infrared (TIR) 610 $athermal UAV RS 610 $aUAV 610 $aUAV-based LiDAR 610 $aunmanned aerial vehicles (UAVs) 610 $avegetation index (VI) 610 $avegetation status monitoring 610 $avolumetric soil moisture 610 $awinter wheat 610 $ayellow rust 610 $ayield estimation 615 7$aEnvironmental science, engineering and technology 615 7$aHistory of engineering and technology 615 7$aTechnology: general issues 700 $aBorgogno-Mondino$b Enrico$4edt$0436404 702 $aTarantino$b Eufemia$4edt 702 $aCapolupo$b Alessandra$4edt 702 $aBorgogno-Mondino$b Enrico$4oth 702 $aTarantino$b Eufemia$4oth 702 $aCapolupo$b Alessandra$4oth 906 $aBOOK 912 $a9910637779903321 996 $aRemote Sensing in Agriculture: State-of-the-Art$93022567 997 $aUNINA