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

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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

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