**1. Introduction**

The global agricultural sector is facing increasing challenges posed by a range of stressors, including a rapidly growing population, the depletion of natural resources, environmental pollution, crop diseases, and climate change. Precision agriculture is a promising approach to address these challenges through improving farming practices, e.g., adaptive inputs (e.g., water and fertilizer), ensured outputs (e.g., crop yield and biomass), and reduced environmental impacts. Remote sensing is capable of identifying within-field variability of soils and crops and providing useful information for

site-specific management practices [1,2]. There are two types of remote sensing technologies given the source of energy, passive (e.g., optical) and active remote sensing (e.g., LiDAR and Radar). Passive optical remote sensing is usually further divided into two groups based on the spectral resolutions of sensors, multispectral and hyperspectral remote sensing [3]. Multispectral imaging is facilitated by collecting spectral signals in a few discrete bands, each spanning a broad spectral range from tens to hundreds of nanometers. In contrast, hyperspectral imaging detects spectral signals in a series of continuous channels with a narrow spectral bandwidth (e.g., typically below 10 nm); therefore, it can capture fine-scale spectral features of targets that otherwise could be compromised [4].

Multispectral images (e.g., Landsat, Sentinel 2, and SPOT images) have been widely used in agricultural studies to retrieve various crop and soil attributes, such as crop chlorophyll content, biomass, yield, and soil degradation [5–10]. However, due to the limitations in spectral resolution, the accuracy of the retrieved variables is often limited, and early signals of crop stresses (e.g., nutrient deficiency, crop disease) cannot be effectively detected in a timely manner [11]. Hyperspectral images (e.g., Hyperion, CASI, and Headwall Micro-Hyperspec) with hundreds of bands can capture more detailed spectral responses; hence, it is more capable of detecting subtle variations of ground covers and their changes over time. Therefore, hyperspectral imagery can be used to address the aforementioned challenges and facilitate more accurate and timely detection of crop physiological status [12,13]. Previous studies have also demonstrated the superior performance of hyperspectral over multispectral images in monitoring vegetation properties, such as estimating the leaf area index (LAI) [14], discriminating crop types [15], retrieving crop biomass [16], and assessing leaf nitrogen content [17]. Despite its outstanding performance, hyperspectral imaging has been utilized comparatively less in operational agricultural applications in the past few decades due to the high cost of the sensors and imaging missions, and various technical challenges (e.g., low signal-to-noise ratio and large data volume) [18–21]. Although ground-based hyperspectral reflectance data can be quickly measured using a spectroradiometer (e.g., ASD Field Spec, Analytical Spectral Devices Inc., Boulder, CO, USA) and have been widely used for observing canopy- and leaf-level spectral features [22–24], such ground-based measurements are limited to a few numbers of field sites, and they cannot capture spatial variability across large areas. In contrast, hyperspectral imaging sensors are more convenient to acquire spatial variability of spectral information across a region.

In recent years, a wide range of mini-sized and low-cost hyperspectral sensors have been developed and are available for commercial use, such as Micro- and Nano-Hyperspec (Headwall Photonics Inc., Boston, MA, USA), HySpex VNIR (HySpex, Skedsmo, Skjetten, Norway), and FireflEYE (Cubert GmbH, Ulm, Germany) [11,25]. These sensors can be mounted on manned or unmanned airborne platforms (e.g., airplanes, helicopters, and unmanned aerial vehicles (UAVs)) for acquiring hyperspectral images and supporting various monitoring missions [13,26,27]. In addition, new spaceborne hyperspectral sensors have been launched recently, such as the DESIS—launched in 2018 [28]—and PRISMA launched in 2019 [29]—or will be launched in the next few years, such as EnMAP, with scheduled launching in 2020 [30,31]. Overall, increasingly more airborne or spaceborne hyperspectral images have become available, bringing unprecedented opportunities for better monitoring of ground targets, especially for better investigation of crop and soil variabilities and supporting precision agriculture. Therefore, a literature search was performed to examine if more research in using hyperspectral imaging for agricultural purposes had been published in recent years. Both Web of Science and Google Scholar were used for conducting the literature search with topics or keywords, including hyperspectral, imaging, agriculture, or farming, and publication over a 30-year time span (1990 to 2020). The searched results were further verified to ensure that each publication falls within the scope of hyperspectral imaging for agriculture applications. It was found that there was an increasing number of publications in recent years that used hyperspectral imaging for agricultural applications (Figure 1). Substantially more studies have been published in the recent decade (e.g., 245 articles published in 2011–2020) than that in the previous one (e.g., 97 published in 2001–2010).

**Figure 1.** The number of publications that utilized hyperspectral imaging for agriculture applications (by May 2020).

This review is designed to focus on the acquisition, processing, and analysis of hyperspectral imagery for different agricultural applications. The review is organized in the following main aspects: (1) Hyperspectral imaging platforms and sensors, (2) methods for processing and analyzing hyperspectral images, and (3) hyperspectral applications in agriculture (Table 1). Regarding imaging platforms, different types, including satellites, airplanes, helicopters, fixed-wing UAVs, multi-rotor UAVs, and close-range platforms (e.g., ground or lab based), have been used. These platforms acquire images with different spatial coverage, spatial resolution, temporal resolution, operational complexity, and mission cost. It will be beneficial to summarize various platforms in terms of these features to support the selection of the appropriate one(s) for different monitoring purposes. After raw hyperspectral imagery is acquired, pre-processing is the step for obtaining accurate spectral information. Several procedures need to be carried out during pre-processing (usually implemented in a specialized remote sensing software), including radiometric calibration, spectral correction, atmospheric correction, and geometric correction. Although these are standard processing steps for most satellite imagery, it still can be challenging to perform on many airborne hyperspectral images due to different technical issues (e.g., the requirement of high-accuracy Global Positioning System (GPS) signals for proper geometric correction, the measurement of real-time solar radiance for accurate spectral correction). There are no standardized protocols for all sensors due to the limited availability of hyperspectral imaging in the past and the fact that the new mini-sized and low-cost hyperspectral sensors in the market are from different manufacturers with varying sensor configurations. Various approaches have been used in previous studies to address these challenges [12,19,32,33]. Therefore, it is essential to review these approaches to support other researchers for more accurate and efficient hyperspectral image processing. After pre-preprocessing, such as calibration and correction, spectral information extraction (e.g., band selection and dimension reduction) can be performed to further improve the usability of the hyperspectral image. Techniques for these procedures are reviewed in this study.



With pre-processed hyperspectral images, a robust and efficient analytical method is required for analyzing the tremendous amount of information contained in the images (e.g., spectral, spatial, and textural features) and extracting target properties (e.g., crop and soil characteristics). Previous studies have used a suite of analytical methods, including empirical regression (e.g., linear regression, partial least square regression (PLSR), and multi-variable regression (MLR)), radiative transfer modelling (RTM, e.g., PROSPECT and PROSAIL), machine learning (e.g., random forest (RF)), and deep learning (e.g., convolutional neural network (CNN)) [34–37]. These methods have been developed based on different theories and have different operational complexity, computation efficiency, and performance accuracy. Therefore, it is essential to review the strengths and limitations of these methods and help to choose the appropriate one(s) for specific research purposes. Using hyperspectral information, researchers have investigated a wide range of agricultural features. Some popular ones include crop water content, LAI, chlorophyll and nitrogen contents, pests and disease, plant height, phenological information, soil moisture, and soil organic matter content [11,38]. It will also be valuable to review the performances of hyperspectral imaging in these studies and further explore the potential of this technology for monitoring other agricultural features. Lastly, challenges of using hyperspectral imaging for precision agriculture, together with future research directions, are discussed. A few previous review articles have discussed some of these topics to some extent [11,38,39]. More details and contributions of this review will be discussed in each specific section. Overall, this review aims to examine the main procedures in collecting and utilizing hyperspectral images for different agricultural applications, to further understand the strengths and limitations of hyperspectral technology, and to promote the faster adoption of this valuable technology in precision farming.

#### **2. Hyperspectral Imaging Platforms and Sensors**

Hyperspectral sensors can be mounted on different platforms, such as satellites, airplanes, UAVs, and close-range platforms, to acquire images with different spatial and temporal resolutions. Platforms used in the literature were identified and summarized over the publication years, aiming to find, if any, the platforms that had been used more frequently in a specific time period, and the results are shown in Figure 2. Airplanes have been the most widely used platforms for hyperspectral imaging in agriculture (Figure 2). Approximately 30 articles that used airplanes were published every five years starting from 2001 (e.g., 27 publications in 2001–2005 and 38 in 2006–2010). In comparison, satellite-based hyperspectral imaging has been used less frequently; approximately 20 or fewer articles were published in all five-year periods. UAVs are popular platforms for remote sensing and have been widely used in the last decade for hyperspectral imaging in agriculture (e.g., more than 20 publications in 2011–2015 and 2016–2020). Close-range platforms have been the most widely used in the last five years (i.e., 2016–2020), with 49 publications (Figure 2). The review in this section is structured based on different platforms, including satellites, airplanes, UAVs, and close-range platforms. In contrast to previous articles reviewing hyperspectral platforms [20,38,39], the review in this section focuses more on recent advancements of imaging platforms (e.g., UAVs, helicopters, and close range) and their applications to precision farming (e.g., weed classification, fine-scale evaluation of crop health, pests, and disease).
