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Palabras clave – provistas por la editorial

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