ALBERT

Keywords: G1-922 ; Q1-390 ; gross primary production (GPP) ; interference filter ; Visible Infrared Imaging Radiometer Suite (VIIRS) ; cost-efficient ; precipitation ; topographic effects ; land surface temperature ; Land surface emissivity ; scale effects ; spatial-temporal variations ; statistics methods ; inter-annual variation ; spatial representativeness ; FY-3C/MERSI ; sunphotometer ; PROSPECT ; passive microwave ; flux measurements ; urban scale ; vegetation dust-retention ; multiple ecological factors ; leaf age ; standard error of the mean ; LUT method ; spectra ; SURFRAD ; Land surface temperature ; aboveground biomass ; uncertainty ; land surface variables ; copper ; Northeast China ; forest disturbance ; end of growing season (EOS) ; random forest model ; probability density function ; downward shortwave radiation ; machine learning ; MODIS products ; composite slope ; daily average value ; canopy reflectance ; spatiotemporal representative ; light use efficiency ; hybrid method ; disturbance index ; quantitative remote sensing inversion ; SCOPE ; GPP ; South China’s ; anisotropic reflectance ; vertical structure ; snow cover ; land cover change ; start of growing season (SOS) ; MS–PT algorithm ; aerosol ; pixel unmixing ; HiWATER ; algorithmic assessment ; surface radiation budget ; latitudinal pattern ; ICESat GLAS ; vegetation phenology ; SIF ; metric comparison ; Antarctica ; spatial heterogeneity ; comprehensive field experiment ; reflectance model ; sinusoidal method ; NDVI ; BRDF ; cloud fraction ; NPP ; VPM ; China ; dense forest ; vegetation remote sensing ; 〈i〉Cunninghamia〈/i〉 ; high resolution ; geometric-optical model ; phenology ; LiDAR ; ZY-3 MUX ; point cloud ; multi-scale validation ; Fraunhofer Line Discrimination (FLD) ; rice ; fractional vegetation cover (FVC) ; interpolation ; high-resolution freeze/thaw ; drought ; Synthetic Aperture Radar (SAR) ; controlling factors ; sampling design ; downscaling ; n/a ; Chinese fir ; MRT-based model ; RADARSAT-2 ; northern China ; leaf area density ; potential evapotranspiration ; black-sky albedo (BSA) ; decision tree ; CMA ; fluorescence quantum efficiency in dark-adapted conditions (FQE) ; surface solar irradiance ; validation ; geographical detector model ; vertical vegetation stratification ; spatiotemporal distribution and variation ; gap fraction ; phenological parameters ; spatio-temporal ; albedometer ; variability ; GLASS ; gross primary productivity (GPP) ; EVI2 ; machine learning algorithms ; latent heat ; GLASS LAI time series ; boreal forest ; leaf ; maize ; heterogeneity ; temperature profiles ; crop-growing regions ; satellite observations ; rugged terrain ; species richness ; voxel ; LAI ; TMI data ; GF-1 WFV ; spectral ; HJ-1 CCD ; leaf area index ; evapotranspiration ; land-surface temperature products (LSTs) ; SPI ; AVHRR ; Tibetan Plateau ; snow-free albedo ; PROSPECT-5B+SAILH (PROSAIL) model ; MCD43A3 C6 ; 3D reconstruction ; photoelectric detector ; multi-data set ; BEPS ; aerosol retrieval ; plant functional type ; multisource data fusion ; remote sensing ; leaf spectral properties ; solo slope ; land surface albedo ; longwave upwelling radiation (LWUP) ; terrestrial LiDAR ; AMSR2 ; geometric optical radiative transfer (GORT) model ; MuSyQ-GPP algorithm ; tree canopy ; FY-3C/MWRI ; meteorological factors ; solar-induced chlorophyll fluorescence ; metric integration ; observations ; polar orbiting satellite ; arid/semiarid ; homogeneous and pure pixel filter ; thermal radiation directionality ; biodiversity ; gradient boosting regression tree ; forest canopy height ; Landsat ; subpixel information ; MODIS ; humidity profiles ; NIR ; geostationary satellite ; thema EDItEUR::R Earth Sciences, Geography, Environment, Planning::RG Geography