*4.3. Classifying Imagery to Identify Crop Types, Growing Stages, Weeds*/*Invasive Species, and Stress*/*Disease*

Besides quantifying crop properties, hyperspectral images have also been used for classification purposes, such as differentiating crop types, identifying crop growing stages, classifying weeds or invasive species, and detecting disease [218]. Examples of previous studies are shown in Table 10. Different agricultural land covers or crop types have different spectral characteristics; hence, hyperspectral images can contribute greatly to the classification of these agricultural features.


**Table 10.** Selected previous studies for the classification of agricultural features using hyperspectral images.

Weed infestation is a severe issue in agricultural fields and could substantially affect crop growth and yield. Identifying and mapping weeds in agricultural fields using remote sensing will contribute greatly to variable rate treatment in the fields [219]. Researchers have utilized different remote sensing data and methods for weed mapping, as shown in Table 11. Overall, the identification of weeds typically requires a high spatial resolution since many weeds are small in size and mixed with crops. UAV-based and close-range hyperspectral imaging is capable of acquiring high-spatial-resolution images, and thus has high potential to contribute to weed detection.


**Table 11.** Selected previous studies for detecting weeds using different hyperspectral imaging platforms.

Monitoring crop disease is highly important to growers trying to reduce economic and yield losses [38]. Hyperspectral imaging collects signals at fine spectral resolutions (e.g., less than 10-nm intervals), and thus can possibly detect early symptoms of crop disease and support timely interventions [225]. Previous studies have used hyperspectral images for detecting diseases in different types of groups (Table 12). Overall, hyperspectral signals are sensitive to the variations of crop growth status (e.g., caused by disease or stress) and thus can indicate the occurrence of crop disease or stress. However, considering that crop status can be affected by other factors (e.g., nutrient deficiency), repeat imaging and analysis together with robust modelling would be critical for accurate and timely detection of crop disease or stress.


**Table 12.** Selected previous studies for detecting disease in different crops using hyperspectral images.

#### *4.4. Retrieving Soil Moisture, Fertility, and Other Physical or Chemical Properties*

Agricultural soil properties, including soil moisture, soil organic matter, soil salinity, and roughness, are important factors influencing crop growth and final production [7]. Hyperspectral remote sensing can contribute greatly to the investigation of these factors. For instance, estimating soil moisture is one of the most popular research topics. Finn et al. [108] estimated soil moisture at three different depths using airborne hyperspectral images and linear regression and discussed the contributions and limitations of hyperspectral remote sensing for soil moisture studies. Casa et al. [229] investigated soil water, clay, and sand contents using a fusion of CHRIS-PROBA images and soil geophysical data. Shoshany et al. [7] summarized four main approaches for estimating soil moisture content: (1) Radar techniques; (2) radiation balance and surface temperature calculations; (3) reflectance in the visible, NIR, and SWIR ranges; and (4) integrative methods using multiple spectral ranges. Although soil moisture can be estimated using optical remote sensing data, it is often affected by the plant ground cover. Integrating multi-type remote sensing data, e.g., SAR and thermal data, can possibly generate more accurate estimates.

SOC is a critical component of soil fertility, which highly controls both the growth and yield of crops. Hyperspectral data provide fine spectral details that are critical for the estimation of SOC content. Previous studies have used hyperspectral images collected by different platforms for investigating SOC (Table 13). Overall, hyperspectral imagery has a high potential for the estimation of soil organic

matter and carbon. However, similar to the evaluation of soil moisture, the investigation of soil organic matter and carbon can be highly influenced by vegetation cover. Therefore, collecting hyperspectral images in non-growing seasons could be a solution.


**Table 13.** Selected previous studies for estimating soil organic carbon using hyperspectral images acquired by different platforms.

Hyperspectral remote sensing data have also been used for estimating other soil features, as shown in Table 14. It can be found from these studies that hyperspectral images can be used for studying a wide range of soil features. Different soil features influence the spectral signals in different bands and with different magnitudes, while some of these influences may be spectrally overlapped. Therefore, when investigating a specific soil feature, it is critical to collect a suitable number of soil samples with other soil features generally controlled.


**Table 14.** Selected previous studies for investigating different soil features using hyperspectral images.

In summary, hyperspectral imaging has been successfully applied to a wide range of agricultural applications, as reviewed above, and summarized in Table 15. Future research directions are also suggested.


