Hyperspectral Remote Sensing of Agriculture and Vegetation
Pascucci, Simone
Hyperspectral Remote Sensing of Agriculture and Vegetation - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (266 p.)
Open Access
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 collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.
Creative Commons
English
books978-3-03943-908-9 9783039439072 9783039439089
10.3390/books978-3-03943-908-9 doi
Research & information: general
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
Hyperspectral Remote Sensing of Agriculture and Vegetation - Basel, Switzerland MDPI - Multidisciplinary Digital Publishing Institute 2021 - 1 electronic resource (266 p.)
Open Access
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 collected inside the book are intended to help researchers and farmers involved in precision agriculture techniques and practices, as well as in plant nutrient prediction, to a higher comprehension of strengths and limitations of the application of hyperspectral imaging to agriculture and vegetation. Hyperspectral remote sensing for studying agriculture and natural vegetation is a challenging research topic that will remain of great interest for different sciences communities in decades.
Creative Commons
English
books978-3-03943-908-9 9783039439072 9783039439089
10.3390/books978-3-03943-908-9 doi
Research & information: general
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