Crops and Vegetation Monitoring with Remote/Proximal Sensing

• Andere auteurs: Omasa, Kenji (Redacteur), Lu, Shan (Redacteur), Wang, Jie (Redacteur)
• Formaat: Elektronisch Hoofdstuk
• Taal: Engels
• Gepubliceerd in: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Onderwerpen: Research & information: general; Geography; rice and wheat; nitrogen remote sensing; quantitative retrieval; research prospect; vegetation phenology; snow cover; vegetation index; SOS; Tibetan Plateau; remote sensing; forest diversity; GEDI LiDAR; Sentinel-2; machine Learning; yield forecasting; logistic model; normalization method; crop canopy temperature; maize; broadband vegetation indices; chlorophyll content; leaf angle distribution; WorldView-2; RapidEye; GaoFen-6; random forest; land evaluation; soil; biomass; Hungary; gross primary productivity; soil health; soil quality; coastal marsh; continuum removal; hyperspectral; spectral signatures; unmanned aerial vehicle (UAV); vegetation species discrimination; second derivative transformation; canopy temperature; crop water status index; accuracy assessment; peach orchard; stem water potential; backscatter; gradient boosting; machine learning; NDVI; precision agriculture; forest stock volume; NDVIRE; Helan mountains; convolutional neural networks (CNNs); unmanned aerial vehicles (UAVs); semi-natural grasslands; plant communities; time series; reconstruction algorithm; smoothing; optical remote sensing; cropping intensity; temporal mixture analysis; endmember; unmixing; time series images
• Online toegang: DOAB: download the publication; DOAB: description of the publication

Remote sensing is a powerful technique for characterizing and monitoring crop or vegetation properties at reasonable temporal and spatial resolutions. Remote sensing uses airborne and spaceborne platforms to collect various imageries and is widely applied for the vegetation monitoring of local- or l...