Reprint
Hyperspectral Remote Sensing of Agriculture and Vegetation
Edited by
January 2021
266 pages
- ISBN978-3-03943-907-2 (Hardback)
- ISBN978-3-03943-908-9 (PDF)
This book is a reprint of the Special Issue Hyperspectral Remote Sensing of Agriculture and Vegetation that was published in
Engineering
Environmental & Earth Sciences
Summary
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.
Format
- Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
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; hyperspectral; spectra; vegetation; plant; classification; discrimination; feature selection; waveband selection; support vector machine; random forest; Natura 2000; invasive species; expansive species; support vector machine; random forest; biodiversity; spectroscopy; proximal sensor; macronutrient; micronutrient; artificial intelligence; precision agriculture; 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; hyperspectral; waveband selection; 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