Crops and Vegetation Monitoring with Remote/Proximal Sensing

• Format: eBook
• Language: English
• Published: Basel MDPI - Multidisciplinary Digital Publishing Institute 2023
• Subjects: accuracy assessment; backscatter; biomass; broadband vegetation indices; canopy temperature; chlorophyll content; coastal marsh; continuum removal; convolutional neural networks (CNNs); crop canopy temperature; crop water status index; cropping intensity; Earth Sciences, Geography, Environment, Planning; endmember; forest diversity; forest stock volume; GaoFen-6; GEDI LiDAR; Geography; gradient boosting; gross primary productivity; Helan mountains; Hungary; hyperspectral; land evaluation; leaf angle distribution; logistic model; machine Learning; maize; NDVI; NDVIRE; nitrogen remote sensing; normalization method; optical remote sensing; peach orchard; plant communities; precision agriculture; quantitative retrieval; random forest; RapidEye; reconstruction algorithm; Reference, Information and Interdisciplinary subjects; remote sensing; Research and information: general; research prospect; rice and wheat; second derivative transformation; semi-natural grasslands; Sentinel-2; smoothing; snow cover; soil; soil health; soil quality; SOS; spectral signatures; stem water potential; temporal mixture analysis; Tibetan Plateau; time series; time series images; unmanned aerial vehicle (UAV); unmanned aerial vehicles (UAVs); unmixing; vegetation index; vegetation phenology; vegetation species discrimination; WorldView-2; yield forecasting
• ISBN: 3036594469; 9783036594477; 3036594477; 9783036594460
• DOI: 10.3390/books978-3-0365-9447-7

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 large-scale interest concerning the effect of geophysical and climate parameters. The Special Issue highlights vegetation monitoring using remote sensing data acquired from satellite or unmanned aerial vehicle platforms. In addition to the optical data, thermal data is utilized to estimate crop yield or production, orchard water status, chlorophyll content, forest diversity mapping, or vegetation phenology.