In fact, both multi- and hyperspectral imagery have the potential to take data mining to a whole new exploration level in many areas, including food quality assessment [
11] and agriculture [
12]. For instance, productivity and stress indicators in both agricultural and forest ecosystems can be assessed through photosynthetic light use efficiency quantification, which can be obtained by measuring the photochemical reflectance index (PRI) relying on narrowband absorbance of xanthophyll pigments at 531 and 570 nm [
15]. However, while the higher spectral resolution present in hyperspectral data allows remote sensing of narrowband spectral composition—also known as spectra, signature or, according to [
16], spectral signature—multispectral data manifests itself in larger intervals over the electromagnetic spectrum, which does not enable to reach the same level of detail. Thus, hyperspectral data has a better performance profiling materials and respective endmembers due to its almost continuous spectra. On the one hand, it covers spectral detail that might pass unnoticeable in multispectral data due to its discrete and sparse nature. For example, in
Figure 2, since red-edge (RE, 670–780 nm) is not accessible through the broadband sensor, leaf chlorophyll content, phenological state and vegetation stress—which are parameters that manifest in that spectral range—cannot be assessed. On the other hand, hyperspectral has the ability to discriminate components that may be unwittingly grouped by multispectral bands (see, for example, [
17] for more details).
Along with the resolution improvement, the hyperspectral sensing approach also increases data processing complexity, since such imagery ranges from hundreds to thousands of narrow bands that can be difficult to handle in real-time with reduced computational resources. Besides, spectral signatures can undergo through variations depending on light exposure and atmospheric conditions, which is an issue that has been leading the scientific community to propose processes for acquisition (to control environmental conditions) and/or analysis methodologies (to correct the noise resulting from environmental conditions). Such efforts allow accurately matching spectral data and identifying material compositions.
The first developments of imaging spectrometry in remote sensing applications started using satellites, more specifically to support Landsat-1 data analysis through field spectral measurements, according to [
19]. Studies were mostly regarded to mineral exploration [
20], but also landmine detection [
21], agroforestry and related areas [
22]. Back then, hyperspectral imaging technology did not have the supporting resources to go mainstream because developments in electronics, computing and software areas were required. Progress in the 1980s ended up to overcome technological limitations, opening the doors for the dissemination of this remote sensing technique for earth monitoring by the 1990s [
23]. However, its development is still considered an ongoing process [
24]. Currently, satellite capabilities for wide spatial range covering along with the improvements that have been carried out regarding spatial and spectral resolution [
25] have been allowing the development of remote sensing works in—also but not limited to—agriculture, forestry and related areas (e.g., [
26,
27,
28]). Notwithstanding, long distances relatively to earth surface raises recurrent problems, according to [
29]. For example, Pölönen et al. [
30] pointed out that for conditions involving cloudiness and short growing season, hyperspectral data acquired from traditional platforms can become useless in some cases. Other issues include the high cost of commercial satellite imagery, which only provide up to 30 cm resolution [
31]. Even recent technology such as Sentinel 2 provides up to 10 m resolution in RGB and NIR [
32], which is too coarse for some applications. For a better insight, considering a scenario with vines in which consecutive rows are parted by 4 m, such imagery would mix, at least, 2 rows with a significant portion of soil. An alternative to satellites started to be designed by National Aeronautics and Space Administration Jet Propulsion Laboratory (NASA/JPL) in 1983, with the development of hyperspectral hardware, specific for aircrafts, resulting in an Airborne Imaging Spectrometer (AIS). Later, in 1987, the airborne visible/infrared imaging spectrometer (AVIRIS) [
33] came out as a high quality hyperspectral data provider that became popular among the scientific community [
19]. However, besides the costs involved in the use of this solution, a certified pilot for manning the aerial vehicle and flight-related logistics is required. Lately, a remote sensing platform capable of overcoming not only satellite but also manned aircraft issues by bringing enhanced spectral and spatial resolutions, operational flexibility and affordability to the users is emerging: the UAS [
34]. Together with specialized sensors, UAS are becoming powerful sensing systems [
35] that complement the traditional sensing techniques rather than competing with them [
1]. According to Pádua et al. [
1], a UAS can be defined as a power-driven and reusable aircraft, operated without a human pilot on board [
36]. Usually, it is composed of a UAV that, in turn, is capable of carrying remote sensing devices. UAS can be remotely controlled or have a programmed route to perform an autonomous flight using the embedded autopilot. Generally, it also requires a ground-control station and communication devices for carrying out flight missions [
37]. Colomina and Molina [
38] share a similar perspective by referring that a UAV is usually referred to as the remotely piloted platform, whereas the UAS is regarded as the platform and control segment. They also add that UAV and UAS are somewhat used interchangeably too. In what regards to hyperspectral data handling, a set of steps can be followed [
13]: (1) image acquisition; (2) calibration; (3) spectral/spatial processing; (4) dimensionality reduction and; finally (5) computation related tasks (e.g., analysis, classification, detection, etc.). Similarly, in [
39], file reduction and subsetting, spectral library definition (e.g., made by selecting a portion in the image) and classification are pointed out as valid operations to constitute a chain. Remote sensing, through the combination of UAV and on-board hyperspectral sensors and relying in many of the aforementioned steps/operations, has been applied both to agriculture and forestry (e.g., [
40,
41,
42]). However, available works are not so numerous when compared with other platforms, since this is a relatively new research field. Even so, they provide a proper demonstration of this approach’s potential.
All in all, the main focus of this paper is, precisely, UAS-based remote sensing using hyperspectral sensors, applied both in agriculture and forestry. The acquisition equipment designed to be attached to UAVs is presented next, in
Section 2. Then,
Section 3 provides a discussion towards the operations that should be carried out before and after flight missions, as well as pre-processing data procedures for image calibration. Important approaches for data processing are reviewed in
Section 4, specifically data dimension, target detection, classification and vegetation indices operations. Supporting software tools and libraries are presented in
Section 5. Applications focusing the use of UAV’s and hyperspectral sensors in agriculture and forestry are addressed in
Section 6, right before some conclusions within
Section 7. To provide guidance along this paper’s reading, a glossary regarding the used abbreviations and acronyms is listed in
Appendix A.