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

Quantitative 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 ot...

Полное описание

Сохранить в:

Библиографические подробности
Главные авторы:	Shi, Jiancheng, Liang, Shunlin, Yan, Guangjian
Формат:	Online
Язык:	английский
Опубликовано:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Предметы:	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
Online-ссылка:	32465
Метки:	Добавить метку Нет меток, Требуется 1-ая метка записи!

Ваш комментарий будет первым!

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

Схожие документы