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Albedo is a known and documented phenomenon, defined as the reflectivity of a surface, i.e., the ratio of reflected light energy to incident light energy. It is a dimensionless quantity, used in particular in agro-forestry, urban environment, cryosphere and geology. It is an Essential Climate Variable (ECV), deemed extremely meaningful to compute the earth heat balance. The albedo of natural surfaces varies largely, especially in the visible, with the lowest values found for water bodies and dense vegetation canopies and the highest values for desert and snow. It also changes with the angular distribution and spectral composition of the incident radiation and with the surface moisture. Satellite observations allow consistent measuring of the surface albedo at continental scale over a short period of time. Long-term series of surface albedo are good indicators of climate change, especially over glaciers and polar caps. On the other hand, the albedo of bare soil provides a good diagnostic of their degradation. The reliability of satellite albedo is verified against ground-based radiometers and UAV, which also serves to calibrate the instruments embarked on space-borne observing systems and check the quality of the atmospheric correction.

Research & information: general --- Environmental economics --- surface albedo --- urbanization --- vegetation variation --- climate change --- DMSP --- albedo --- land use --- remote sensing --- Unmanned Aerial Vehicles --- vegetation indices --- snow --- climate --- Unmanned Aerial Vehicle (UAV) --- landscape --- consumer-grade camera --- radiometric calibration --- sea ice --- VIIRS --- Arctic --- PROMICE --- GC-NET --- validation --- AVHRR --- BRDF --- MODIS --- VJB --- LTDR --- directional correction --- spatial representativeness --- semivariogram --- Landsat --- HLS --- Sentinel 2 --- SURFRAD --- OzFlux --- directional hemispherical reflectance --- bi-hemispherical reflectance --- tower albedometer --- CGLS --- MISR --- upscaling --- bare soil albedo --- MODIS albedo --- contiguous United States --- soil line --- Landsat albedo --- soil moisture --- land surface albedo --- time series --- high spatio-temporal resolution --- EnKF --- spectral unmixing --- empirical modeling --- linear endmember --- forest cover --- forest management --- forest structure --- BRDF/Albedo --- NDSI Snow Cover --- n/a

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Albedo is a known and documented phenomenon, defined as the reflectivity of a surface, i.e., the ratio of reflected light energy to incident light energy. It is a dimensionless quantity, used in particular in agro-forestry, urban environment, cryosphere and geology. It is an Essential Climate Variable (ECV), deemed extremely meaningful to compute the earth heat balance. The albedo of natural surfaces varies largely, especially in the visible, with the lowest values found for water bodies and dense vegetation canopies and the highest values for desert and snow. It also changes with the angular distribution and spectral composition of the incident radiation and with the surface moisture. Satellite observations allow consistent measuring of the surface albedo at continental scale over a short period of time. Long-term series of surface albedo are good indicators of climate change, especially over glaciers and polar caps. On the other hand, the albedo of bare soil provides a good diagnostic of their degradation. The reliability of satellite albedo is verified against ground-based radiometers and UAV, which also serves to calibrate the instruments embarked on space-borne observing systems and check the quality of the atmospheric correction.

surface albedo --- urbanization --- vegetation variation --- climate change --- DMSP --- albedo --- land use --- remote sensing --- Unmanned Aerial Vehicles --- vegetation indices --- snow --- climate --- Unmanned Aerial Vehicle (UAV) --- landscape --- consumer-grade camera --- radiometric calibration --- sea ice --- VIIRS --- Arctic --- PROMICE --- GC-NET --- validation --- AVHRR --- BRDF --- MODIS --- VJB --- LTDR --- directional correction --- spatial representativeness --- semivariogram --- Landsat --- HLS --- Sentinel 2 --- SURFRAD --- OzFlux --- directional hemispherical reflectance --- bi-hemispherical reflectance --- tower albedometer --- CGLS --- MISR --- upscaling --- bare soil albedo --- MODIS albedo --- contiguous United States --- soil line --- Landsat albedo --- soil moisture --- land surface albedo --- time series --- high spatio-temporal resolution --- EnKF --- spectral unmixing --- empirical modeling --- linear endmember --- forest cover --- forest management --- forest structure --- BRDF/Albedo --- NDSI Snow Cover --- n/a

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This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.

hyperspectral LiDAR --- Red Edge --- AOTF --- vegetation parameters --- leaf chlorophyll content --- DLARI --- MDATT --- adaxial --- abaxial --- spectral reflectance --- peanut --- field spectroscopy --- hyperspectral --- heavy metals --- grapevine --- PLS --- SVM --- MLR --- multi-angle observation --- hyperspectral remote sensing --- BRDF --- vegetation classification --- object-oriented segmentation --- spectroscopy --- artificial intelligence --- proximal sensing data --- precision agriculture --- spectra --- vegetation --- plant --- classification --- discrimination --- feature selection --- waveband selection --- support vector machine --- random forest --- Natura 2000 --- invasive species --- expansive species --- biodiversity --- proximal sensor --- macronutrient --- micronutrient --- remote sensing --- hyperspectral imaging --- platforms and sensors --- analytical methods --- crop properties --- soil characteristics --- classification of agricultural features --- canopy spectra --- chlorophyll content --- continuous wavelet transform (CWT) --- correlation coefficient --- partial least square regression (PLSR) --- reproducibility --- replicability --- partial least squares --- Ethiopia --- Eragrostis tef --- hyperspectral remote sensing for soil and crops in agriculture --- hyperspectral imaging for vegetation --- plant traits --- high-resolution spectroscopy for agricultural soils and vegetation --- hyperspectral databases for agricultural soils and vegetation --- hyperspectral data as input for modelling soil, crop, and vegetation --- product validation --- new hyperspectral technologies --- future hyperspectral missions

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This book shows recent and innovative applications of the use of hyperspectral technology for optimal quantification of crop, vegetation, and soil biophysical variables at various spatial scales, which can be an important aspect in agricultural management practices and monitoring. The articles collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.

Research & information: general --- Environmental economics --- hyperspectral LiDAR --- Red Edge --- AOTF --- vegetation parameters --- leaf chlorophyll content --- DLARI --- MDATT --- adaxial --- abaxial --- spectral reflectance --- peanut --- field spectroscopy --- hyperspectral --- heavy metals --- grapevine --- PLS --- SVM --- MLR --- multi-angle observation --- hyperspectral remote sensing --- BRDF --- vegetation classification --- object-oriented segmentation --- spectroscopy --- artificial intelligence --- proximal sensing data --- precision agriculture --- spectra --- vegetation --- plant --- classification --- discrimination --- feature selection --- waveband selection --- support vector machine --- random forest --- Natura 2000 --- invasive species --- expansive species --- biodiversity --- proximal sensor --- macronutrient --- micronutrient --- remote sensing --- hyperspectral imaging --- platforms and sensors --- analytical methods --- crop properties --- soil characteristics --- classification of agricultural features --- canopy spectra --- chlorophyll content --- continuous wavelet transform (CWT) --- correlation coefficient --- partial least square regression (PLSR) --- reproducibility --- replicability --- partial least squares --- Ethiopia --- Eragrostis tef --- hyperspectral remote sensing for soil and crops in agriculture --- hyperspectral imaging for vegetation --- plant traits --- high-resolution spectroscopy for agricultural soils and vegetation --- hyperspectral databases for agricultural soils and vegetation --- hyperspectral data as input for modelling soil, crop, and vegetation --- product validation --- new hyperspectral technologies --- future hyperspectral missions

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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 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.

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 --- Cunninghamia --- 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 --- South China's --- MS-PT algorithm