Journal Article

Special Issue “Hyperspectral Remote Sensing of Agriculture and Vegetation”

Darvishzadeh, Roshanak, Huang, Wenjiang, Pignatti, Stefano, Casa, Raffaele, Pascucci, Simone

2020

Overview

The advent of up-to-date hyperspectral technologies, and their increasing performance both spectrally and spatially, allows for new and exciting studies and practical applications in agriculture (soils and crops) and vegetation mapping and monitoring atregional (satellite platforms) andwithin-field (airplanes, drones and ground-based platforms) scales. Within this context, the special issue has included eleven international research studies using different hyperspectral datasets (from the Visible to the Shortwave Infrared spectral region) for agricultural soil, crop and vegetation modelling, mapping, and monitoring. Different classification methods (Support Vector Machine, Random Forest, Artificial Neural Network, Decision Tree) and crop canopy/leaf biophysical parameters (e.g., chlorophyll content) estimation methods (partial least squares and multiple linear regressions) have been evaluated. Further, drone-based hyperspectral mapping by combining bidirectional reflectance distribution function (BRDF) model for multi-angle remote sensing and object-oriented classification methods are also examined. A review article on the recent advances of hyperspectral imaging technology and applications in agriculture is also included in this issue. The special issue is intended to help researchers and farmers involved in precision agriculture technology and practices to a better comprehension of strengths and limitations of the application of hyperspectral measurements for agriculture and vegetation monitoring. The studies published herein can be used by the agriculture and vegetation research and management communities to improve the characterization and evaluation of biophysical variables and processes, as well as for a more accurate prediction of plant nutrient using existing and forthcoming hyperspectral remote sensing technologies.

• Publisher: MDPI AG
• Subject: Accuracy / administrative management / Agricultural land / Agricultural research / agricultural soils / Agricultural technology / Agriculture / airplanes / Algorithms / Artificial neural networks / Bidirectional reflectance / canopy / Chlorophyll / Classification / Crops / data collection / decision support systems / Decision trees / Distribution functions / estimation / farmers / high-resolution spectroscopy for agricultural soils and vegetation / hyperspectral data as input for modelling soil, crop, and vegetation / hyperspectral databases for agricultural soils and vegetation / hyperspectral imagery / Hyperspectral imaging / hyperspectral imaging for vegetation / hyperspectral remote sensing for soil and crops in agriculture / Laboratories / least squares / leaves / Lettuce / Mapping / Monitoring / Neural networks / Parameter estimation / Pesticides / plant traits / Precision agriculture / prediction / reflectance / Remote sensing / researchers / Satellites / Sensors / Short wave radiation / Support vector machines / Unmanned aerial vehicles / Vegetation / Vegetation mapping / Vegetation surveys