**1. Introduction**

The use of hyperspectral technology for an optimal quantification of crop and soil biophysical variables at various spatial scales is an important aspect in agricultural management practices and monitoring [1,2]. Moreover, there is a great interest to update (i.e., research of new variables) and optimize the retrieval of crop biophysical variables using drone and available satellite data [2–17], as well as future high spatial resolution hyperspectral satellites. To this aim, the exploitation of different approaches for assimilation of the retrieved biophysical parameters into agricultural models is also of primary interest. As it would allow deriving agronomical proxy variables addressing the issues of the multi-scale and multivariate nature of the retrieved variables [6,7,11–19]. For example, a complete and updated knowledge of the spatial distribution of leaf area index (LAI), pigments like chlorophyll content and nitrogen can support sustainable agricultural practices and optimize related costs, through optimal use of fertilizer, pesticides and water that are strictly subdued to an improvement of crop yields and quality. Hyperspectral imaging has great potential for applications in agriculture, particularly precision agriculture, owing to their ample spectral information sensitive to different plant and soil biophysical and biochemical properties [11–25]. Multiple platforms (satellites, airplanes, unmanned aerial vehicle (UAVs), and close-range platforms) have become more widely available in recent years for collecting hyperspectral data with different spatial (from centimeter to decameter), temporal, and spectral resolutions. These platforms have different strengths and limitations in terms of spatial coverage, flight endurance, flexibility, operational complexity, and costs. These factors need to be evaluated when choosing the hyperspectral platform(s) for specific research purposes, e.g., increasing productivity, expanded coverage, and reduced use of fertilizers, pesticides, and water. Further technological developments are also needed to overcome some of the limitations, such as the short battery endurance in UAV operations and high cost of hyperspectral sensors [4].

All in all, hyperspectral remote sensing (RS) represents an attractive and efficient technology capable of estimating soil and crop biophysical variables of interest from regional to intra-field scales.

Research advances are still required to validate methods and applications for the estimation of additional crop biophysical variables and proxy agronomical products [14–25] and for their assimilation into spatially distributed agricultural models (e.g., grains quality, pest and disease dynamic, water-driven, and crop growing models), also by comparing different assimilation approaches [10–24].

This special issue was set up to highlight and diffuse the recent advances in hyperspectral RS studies and their practical applications for agriculture (soils and crops) mapping and monitoring from regional to within-field scales. Our objectives as guest Editors were to encourage studies and applications on this topic and to assemble high-quality, peer-reviewed research and review articles in a special issue of *Remote Sensing* dedicated to this theme. We accepted manuscripts concerned with all aspects of hyperspectral RS (optical domain) for crop and natural vegetation. This included hyperspectral studies of agricultural soils, crops, as well as other vegetation types using the ground, drone, air-, and space-borne platforms (VIS-NIR, SWIR, and TIR). With various focus on: field, and laboratory hyperspectral measurements for monitoring agriculture and vegetation; retrieval of plant traits at leaf and canopy level from hyperspectral measurements; new methods for hyperspectral data processing and atmospheric compensation techniques; hyperspectral sensors calibration and products validation for agriculture and vegetation monitoring; statistical and computational methods for hyperspectral data analysis in agriculture and vegetation applications; integration or combined use of hyperspectral data from the optical domain with other Earth Observation (EO) technologies; modelling of soils, crops, and vegetation using hyperspectral data; next-generation hyperspectral technologies and missions, platforms, and sensors for agriculture and vegetation.

A total of 18 manuscripts were submitted and peer-reviewed by fifty anonymous, scrupulous reviewers. Of these, 11 manuscripts achieved the level of quality and innovation expected by *Remote Sensing* and were at the end published in this special issue. A total of 77 authors contributed to these 11 articles and hailed from six different nations: Brazil (26 authors), Canada (8), Australia (5), Finland (3), China (23), UK (1), Iran (3), Belgium (1), Spain (1), Poland (3), Ethiopia (1), and USA (2).
