Hyperspectral Remote Sensing of Agriculture and Vegetation

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 colle...

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Бібліографічні деталі
Співавтори:	Pascucci, Simone, Pignatti, Stefano, Casa, Raffaele, Darvishzadeh, Roshanak, Huang, Wenjiang
Формат:	Livro
Мова:	Inglês
Опубліковано:	MDPI - Multidisciplinary Digital Publishing Institute 2021
Предмети:	thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::K Economics, Finance, Business and Management::KC Economics::KCV Economics of specific sectors::KCVG 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
Онлайн доступ:	https://directory.doabooks.org/handle/20.500.12854/68321
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Hyperspectral Remote Sensing of Agriculture and Vegetation

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