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Advances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li

dc.contributor.authorShi, Jiancheng*
dc.contributor.authorLiang, Shunlin*
dc.contributor.authorYan, Guangjian*
dc.date.accessioned2021-02-11T07:50:21Z
dc.date.available2021-02-11T07:50:21Z
dc.date.issued2019*
dc.date.submitted2019-03-08 11:42:05*
dc.identifier32466*
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/40344
dc.description.abstractQuantitative 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.*
dc.languageEnglish*
dc.subjectG1-922*
dc.subjectQ1-390*
dc.subject.classificationthema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geographyen_US
dc.subject.othergross primary production (GPP)*
dc.subject.otherinterference filter*
dc.subject.otherVisible Infrared Imaging Radiometer Suite (VIIRS)*
dc.subject.othercost-efficient*
dc.subject.otherprecipitation*
dc.subject.othertopographic effects*
dc.subject.otherland surface temperature*
dc.subject.otherLand surface emissivity*
dc.subject.otherscale effects*
dc.subject.otherspatial-temporal variations*
dc.subject.otherstatistics methods*
dc.subject.otherinter-annual variation*
dc.subject.otherspatial representativeness*
dc.subject.otherFY-3C/MERSI*
dc.subject.othersunphotometer*
dc.subject.otherPROSPECT*
dc.subject.otherpassive microwave*
dc.subject.otherflux measurements*
dc.subject.otherurban scale*
dc.subject.othervegetation dust-retention*
dc.subject.othermultiple ecological factors*
dc.subject.otherleaf age*
dc.subject.otherstandard error of the mean*
dc.subject.otherLUT method*
dc.subject.otherspectra*
dc.subject.otherSURFRAD*
dc.subject.otherLand surface temperature*
dc.subject.otheraboveground biomass*
dc.subject.otheruncertainty*
dc.subject.otherland surface variables*
dc.subject.othercopper*
dc.subject.otherNortheast China*
dc.subject.otherforest disturbance*
dc.subject.otherend of growing season (EOS)*
dc.subject.otherrandom forest model*
dc.subject.otherprobability density function*
dc.subject.otherdownward shortwave radiation*
dc.subject.othermachine learning*
dc.subject.otherMODIS products*
dc.subject.othercomposite slope*
dc.subject.otherdaily average value*
dc.subject.othercanopy reflectance*
dc.subject.otherspatiotemporal representative*
dc.subject.otherlight use efficiency*
dc.subject.otherhybrid method*
dc.subject.otherdisturbance index*
dc.subject.otherquantitative remote sensing inversion*
dc.subject.otherSCOPE*
dc.subject.otherGPP*
dc.subject.otherSouth China’s*
dc.subject.otheranisotropic reflectance*
dc.subject.othervertical structure*
dc.subject.othersnow cover*
dc.subject.otherland cover change*
dc.subject.otherstart of growing season (SOS)*
dc.subject.otherMS–PT algorithm*
dc.subject.otheraerosol*
dc.subject.otherpixel unmixing*
dc.subject.otherHiWATER*
dc.subject.otheralgorithmic assessment*
dc.subject.othersurface radiation budget*
dc.subject.otherlatitudinal pattern*
dc.subject.otherICESat GLAS*
dc.subject.othervegetation phenology*
dc.subject.otherSIF*
dc.subject.othermetric comparison*
dc.subject.otherAntarctica*
dc.subject.otherspatial heterogeneity*
dc.subject.othercomprehensive field experiment*
dc.subject.otherreflectance model*
dc.subject.othersinusoidal method*
dc.subject.otherNDVI*
dc.subject.otherBRDF*
dc.subject.othercloud fraction*
dc.subject.otherNPP*
dc.subject.otherVPM*
dc.subject.otherChina*
dc.subject.otherdense forest*
dc.subject.othervegetation remote sensing*
dc.subject.other<i>Cunninghamia</i>*
dc.subject.otherhigh resolution*
dc.subject.othergeometric-optical model*
dc.subject.otherphenology*
dc.subject.otherLiDAR*
dc.subject.otherZY-3 MUX*
dc.subject.otherpoint cloud*
dc.subject.othermulti-scale validation*
dc.subject.otherFraunhofer Line Discrimination (FLD)*
dc.subject.otherrice*
dc.subject.otherfractional vegetation cover (FVC)*
dc.subject.otherinterpolation*
dc.subject.otherhigh-resolution freeze/thaw*
dc.subject.otherdrought*
dc.subject.otherSynthetic Aperture Radar (SAR)*
dc.subject.othercontrolling factors*
dc.subject.othersampling design*
dc.subject.otherdownscaling*
dc.subject.othern/a*
dc.subject.otherChinese fir*
dc.subject.otherMRT-based model*
dc.subject.otherRADARSAT-2*
dc.subject.othernorthern China*
dc.subject.otherleaf area density*
dc.subject.otherpotential evapotranspiration*
dc.subject.otherblack-sky albedo (BSA)*
dc.subject.otherdecision tree*
dc.subject.otherCMA*
dc.subject.otherfluorescence quantum efficiency in dark-adapted conditions (FQE)*
dc.subject.othersurface solar irradiance*
dc.subject.othervalidation*
dc.subject.othergeographical detector model*
dc.subject.othervertical vegetation stratification*
dc.subject.otherspatiotemporal distribution and variation*
dc.subject.othergap fraction*
dc.subject.otherphenological parameters*
dc.subject.otherspatio-temporal*
dc.subject.otheralbedometer*
dc.subject.othervariability*
dc.subject.otherGLASS*
dc.subject.othergross primary productivity (GPP)*
dc.subject.otherEVI2*
dc.subject.othermachine learning algorithms*
dc.subject.otherlatent heat*
dc.subject.otherGLASS LAI time series*
dc.subject.otherboreal forest*
dc.subject.otherleaf*
dc.subject.othermaize*
dc.subject.otherheterogeneity*
dc.subject.othertemperature profiles*
dc.subject.othercrop-growing regions*
dc.subject.othersatellite observations*
dc.subject.otherrugged terrain*
dc.subject.otherspecies richness*
dc.subject.othervoxel*
dc.subject.otherLAI*
dc.subject.otherTMI data*
dc.subject.otherGF-1 WFV*
dc.subject.otherspectral*
dc.subject.otherHJ-1 CCD*
dc.subject.otherleaf area index*
dc.subject.otherevapotranspiration*
dc.subject.otherland-surface temperature products (LSTs)*
dc.subject.otherSPI*
dc.subject.otherAVHRR*
dc.subject.otherTibetan Plateau*
dc.subject.othersnow-free albedo*
dc.subject.otherPROSPECT-5B+SAILH (PROSAIL) model*
dc.subject.otherMCD43A3 C6*
dc.subject.other3D reconstruction*
dc.subject.otherphotoelectric detector*
dc.subject.othermulti-data set*
dc.subject.otherBEPS*
dc.subject.otheraerosol retrieval*
dc.subject.otherplant functional type*
dc.subject.othermultisource data fusion*
dc.subject.otherremote sensing*
dc.subject.otherleaf spectral properties*
dc.subject.othersolo slope*
dc.subject.otherland surface albedo*
dc.subject.otherlongwave upwelling radiation (LWUP)*
dc.subject.otherterrestrial LiDAR*
dc.subject.otherAMSR2*
dc.subject.othergeometric optical radiative transfer (GORT) model*
dc.subject.otherMuSyQ-GPP algorithm*
dc.subject.othertree canopy*
dc.subject.otherFY-3C/MWRI*
dc.subject.othermeteorological factors*
dc.subject.othersolar-induced chlorophyll fluorescence*
dc.subject.othermetric integration*
dc.subject.otherobservations*
dc.subject.otherpolar orbiting satellite*
dc.subject.otherarid/semiarid*
dc.subject.otherhomogeneous and pure pixel filter*
dc.subject.otherthermal radiation directionality*
dc.subject.otherbiodiversity*
dc.subject.othergradient boosting regression tree*
dc.subject.otherforest canopy height*
dc.subject.otherLandsat*
dc.subject.othersubpixel information*
dc.subject.otherMODIS*
dc.subject.otherhumidity profiles*
dc.subject.otherNIR*
dc.subject.othergeostationary satellite*
dc.titleAdvances in Quantitative Remote Sensing in China – In Memory of Prof. Xiaowen Li*
dc.typebook
oapen.identifier.doi10.3390/books978-3-03897-277-8*
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0*
oapen.relation.isbn9783038972761*
oapen.pages404*
oapen.volume2*
oapen.edition1st*


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