Hyperspectral Remote Sensing of Agriculture and Vegetation (MDPI - Multidisciplinary Digital Publishing Institute, 2021)
Keywords: abaxial; adaxial; analytical methods; and vegetation; AOTF; artificial intelligence; biodiversity; BRDF; canopy spectra; chlorophyll content; classification; classification of agricultural features; continuous wavelet transform (CWT); correlation coefficient; crop; crop properties; discrimination; DLARI; Economics; Economics of specific sectors; Economics, Finance, Business and Management; Environmental economics; Eragrostis tef; Ethiopia; expansive species; feature selection; field spectroscopy; future hyperspectral missions; grapevine; heavy metals; high-resolution spectroscopy for agricultural soils and vegetation; hyperspectral; hyperspectral data as input for modelling soil; hyperspectral databases for agricultural soils and vegetation; hyperspectral imaging; hyperspectral imaging for vegetation; hyperspectral LiDAR; hyperspectral remote sensing; hyperspectral remote sensing for soil and crops in agriculture; invasive species; leaf chlorophyll content; macronutrient; MDATT; micronutrient; MLR; multi-angle observation; Natura 2000; new hyperspectral technologies; object-oriented segmentation; partial least square regression (PLSR); partial least squares; peanut; plant; plant traits; platforms and sensors; PLS; precision agriculture; product validation; proximal sensing data; proximal sensor; random forest; Red Edge; Reference, Information and Interdisciplinary subjects; remote sensing; replicability; reproducibility; Research and information: general; soil characteristics; spectra; spectral reflectance; spectroscopy; support vector machine; SVM; vegetation; vegetation classification; vegetation parameters; waveband selection