LEADER 00971cam0-2200337---450- 001 990004040950403321 005 20160118090619.0 010 $a0-333-37659-5 035 $a000404095 035 $aFED01000404095 035 $a(Aleph)000404095FED01 035 $a000404095 100 $a19990604d1986----km-y0itay50------ba 101 0 $aeng 102 $aGB 105 $aa-------001yy 200 1 $aFaith and doubt in Victorian Britain$fElisabeth Jay 210 $aHoundmills ; London$cMacmillan$d1986 215 $aXI, 136 p. , [2] c. di tav.$cill.$d22 cm 225 1 $aContext and Commentary 610 0 $aGran Bretagna$aStoria della Chiesa$aSec. 19. 676 $a274.1081 676 $a274.2 700 1$aJay,$bElisabeth$0384673 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990004040950403321 952 $a274.1 JAY 1$bD.F.M.1428$fFLFBC 959 $aFLFBC 996 $aFaith and doubt in Victorian Britain$9471575 997 $aUNINA LEADER 08646nam 2202533z- 450 001 9910346664103321 005 20210211 035 $a(CKB)4920000000095026 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/40344 035 $a(oapen)doab40344 035 $a(EXLCZ)994920000000095026 100 $a20202102d2019 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aAdvances in Quantitative Remote Sensing in China - In Memory of Prof. Xiaowen Li 210 $cMDPI - Multidisciplinary Digital Publishing Institute$d2019 215 $a1 online resource (404 p.) 311 08$a3-03897-276-2 330 $aQuantitative land remote sensing has recently advanced dramatically, particularly in China. It has been largely driven by vast governmental investment, the availability of a huge amount of Chinese satellite data, geospatial information requirements for addressing pressing environmental issues and other societal benefits. Many individuals have also fostered and made great contributions to its development, and Prof. Xiaowen Li was one of these leading figures. This book is published in memory of Prof. Li. The papers collected in this book cover topics from surface reflectance simulation, inversion algorithm and estimation of variables, to applications in optical, thermal, Lidar and microwave remote sensing. The wide range of variables include directional reflectance, chlorophyll fluorescence, aerosol optical depth, incident solar radiation, albedo, surface temperature, upward longwave radiation, leaf area index, fractional vegetation cover, forest biomass, precipitation, evapotranspiration, freeze/thaw snow cover, vegetation productivity, phenology and biodiversity indicators. They clearly reflect the current level of research in this area. This book constitutes an excellent reference suitable for upper-level undergraduate students, graduate students and professionals in remote sensing. 606 $aGeography$2bicssc 610 $aCunninghamia 610 $a3D reconstruction 610 $aaboveground biomass 610 $aaerosol 610 $aaerosol retrieval 610 $aalbedometer 610 $aalgorithmic assessment 610 $aAMSR2 610 $aanisotropic reflectance 610 $aAntarctica 610 $aarid/semiarid 610 $aAVHRR 610 $aBEPS 610 $abiodiversity 610 $ablack-sky albedo (BSA) 610 $aboreal forest 610 $aBRDF 610 $acanopy reflectance 610 $aChina 610 $aChinese fir 610 $acloud fraction 610 $aCMA 610 $acomposite slope 610 $acomprehensive field experiment 610 $acontrolling factors 610 $acopper 610 $acost-efficient 610 $acrop-growing regions 610 $adaily average value 610 $adecision tree 610 $adense forest 610 $adisturbance index 610 $adownscaling 610 $adownward shortwave radiation 610 $adrought 610 $aend of growing season (EOS) 610 $aevapotranspiration 610 $aEVI2 610 $afluorescence quantum efficiency in dark-adapted conditions (FQE) 610 $aflux measurements 610 $aforest canopy height 610 $aforest disturbance 610 $afractional vegetation cover (FVC) 610 $aFraunhofer Line Discrimination (FLD) 610 $aFY-3C/MERSI 610 $aFY-3C/MWRI 610 $agap fraction 610 $ageographical detector model 610 $ageometric optical radiative transfer (GORT) model 610 $ageometric-optical model 610 $ageostationary satellite 610 $aGF-1 WFV 610 $aGLASS 610 $aGLASS LAI time series 610 $aGPP 610 $agradient boosting regression tree 610 $agross primary production (GPP) 610 $agross primary productivity (GPP) 610 $aheterogeneity 610 $ahigh resolution 610 $ahigh-resolution freeze/thaw 610 $aHiWATER 610 $aHJ-1 CCD 610 $ahomogeneous and pure pixel filter 610 $ahumidity profiles 610 $ahybrid method 610 $aICESat GLAS 610 $ainter-annual variation 610 $ainterference filter 610 $ainterpolation 610 $aLAI 610 $aland cover change 610 $aland surface albedo 610 $aLand surface emissivity 610 $aland surface temperature 610 $aLand surface temperature 610 $aland surface variables 610 $aland-surface temperature products (LSTs) 610 $aLandsat 610 $alatent heat 610 $alatitudinal pattern 610 $aleaf 610 $aleaf age 610 $aleaf area density 610 $aleaf area index 610 $aleaf spectral properties 610 $aLiDAR 610 $alight use efficiency 610 $alongwave upwelling radiation (LWUP) 610 $aLUT method 610 $amachine learning 610 $amachine learning algorithms 610 $amaize 610 $aMCD43A3 C6 610 $ameteorological factors 610 $ametric comparison 610 $ametric integration 610 $aMODIS 610 $aMODIS products 610 $aMRT-based model 610 $aMS-PT algorithm 610 $amulti-data set 610 $amulti-scale validation 610 $amultiple ecological factors 610 $amultisource data fusion 610 $aMuSyQ-GPP algorithm 610 $an/a 610 $aNDVI 610 $aNIR 610 $aNortheast China 610 $anorthern China 610 $aNPP 610 $aobservations 610 $apassive microwave 610 $aphenological parameters 610 $aphenology 610 $aphotoelectric detector 610 $apixel unmixing 610 $aplant functional type 610 $apoint cloud 610 $apolar orbiting satellite 610 $apotential evapotranspiration 610 $aprecipitation 610 $aprobability density function 610 $aPROSPECT 610 $aPROSPECT-5B+SAILH (PROSAIL) model 610 $aquantitative remote sensing inversion 610 $aRADARSAT-2 610 $arandom forest model 610 $areflectance model 610 $aremote sensing 610 $arice 610 $arugged terrain 610 $asampling design 610 $asatellite observations 610 $ascale effects 610 $aSCOPE 610 $aSIF 610 $asinusoidal method 610 $asnow cover 610 $asnow-free albedo 610 $asolar-induced chlorophyll fluorescence 610 $asolo slope 610 $aSouth China's 610 $aspatial heterogeneity 610 $aspatial representativeness 610 $aspatial-temporal variations 610 $aspatio-temporal 610 $aspatiotemporal distribution and variation 610 $aspatiotemporal representative 610 $aspecies richness 610 $aspectra 610 $aspectral 610 $aSPI 610 $astandard error of the mean 610 $astart of growing season (SOS) 610 $astatistics methods 610 $asubpixel information 610 $asunphotometer 610 $asurface radiation budget 610 $asurface solar irradiance 610 $aSURFRAD 610 $aSynthetic Aperture Radar (SAR) 610 $atemperature profiles 610 $aterrestrial LiDAR 610 $athermal radiation directionality 610 $aTibetan Plateau 610 $aTMI data 610 $atopographic effects 610 $atree canopy 610 $auncertainty 610 $aurban scale 610 $avalidation 610 $avariability 610 $avegetation dust-retention 610 $avegetation phenology 610 $avegetation remote sensing 610 $avertical structure 610 $avertical vegetation stratification 610 $aVisible Infrared Imaging Radiometer Suite (VIIRS) 610 $avoxel 610 $aVPM 610 $aZY-3 MUX 615 7$aGeography 700 $aShi$b Jiancheng$4auth$01306126 702 $aLiang$b Shunlin$4auth 702 $aYan$b Guangjian$4auth 906 $aBOOK 912 $a9910346664103321 996 $aAdvances in Quantitative Remote Sensing in China ? In Memory of Prof. Xiaowen Li$93028292 997 $aUNINA