*4.1. Estimation of Crop Biochemical and Biophysical Properties*

One important hyperspectral application in agriculture is monitoring crop conditions through the retrieval of crop biochemical and biophysical properties [8,99]. For instance, the leaf chlorophyll content is an essential biochemical property influencing the vegetation photosynthetic capacity and controlling crop productivity [99]. In previous studies, Oppelt and Mauser [105] collected AVIS data to retrieve the chlorophyll and nitrogen contents in a winter wheat field. Similarly, Moharana and Dutta [43] used Hyperion data to estimate the contents of these two biochemical components in a rice field. LAI, on the other hand, is a fundamental vegetation biophysical parameter and is highly related to crop biomass and yield [98]. Previous studies have used hyperspectral remote sensing to estimate the LAI of different crops, and some of the example studies are shown in Table 8.


**Table 8.** Selected previous studies estimating LAI for different crop types using hyperspectral images.

In addition to the above-mentioned vegetation biochemical and biophysical properties, crop water content is a critical parameter for revealing water stress. Richter et al. [98] attempted to estimate the water content in maize, sugar beet, and winter wheat using airborne HyMap data. Moharana and Dutta [204] investigated the water stress in a rice field and its variations using Hyperion images and indicated that the remote sensing-estimated water content matched well with field-observed data. Izzo et al. [128] evaluated the water status in a commercial vineyard using UAV-based hyperspectral data and determined wavelengths sensitive to the canopy water content. Sahoo et al. [4] discussed the applications of hyperspectral remote sensing data for evaluating water features in crops and listed several vegetation indices for calculating the water content.

It can be found from the literature review that many previous studies have focused on estimating the crop chlorophyll content, LAI, and water content using hyperspectral imagery, while other important crop properties, such as carotenoids, that are sensitive to plant stress are less explored. In addition, crop production is influenced by all of these vegetation properties (e.g., chlorophyll, water, and LAI). Besides investigating the spatial and temporal variations of each property, it is also critical to evaluate the relationships between these properties and further understand how they affect crop growth and crop production.

Estimating crop biomass and forecasting yield are also important applications of remote sensing, as they will contribute to the understanding of crop productivity and implementing suitable management measures [126]. Yue et al. [124] utilized UAV-based hyperspectral images for estimating the above-ground biomass of winter wheat. Yang [205] and Mariotto et al. [15] utilized both multispectral and hyperspectral data to estimate crop yield and found that the hyperspectral imagery-based model performed better. In addition, crop residues left in the field are critical materials protecting soil from water and wind erosion and influencing soil biochemical processes. Previous studies, such as Bannari et al. [106], Galloza and Crawford [47], Bannari et al. [46], have used different hyperspectral images for the estimation of crop residues on farmlands

Beyond the estimation of crop biomass and residue, one further research topic is investigating bioenergy (e.g., biogas), which can be generated from the crop biomass. Thomas et al. [100] attempted to estimate the amount of biogas that can be generated per unit of biomass using airborne HyMap data and achieved satisfactory results. Overall, hyperspectral imagery has contributed greatly to the estimation of crop biomass, yield, and other related features (e.g., bioenergy, crop residues). Since crop biomass and yield are highly affected by agricultural practices (e.g., watering and nutrition treatment), involving these practice data, together with hyperspectral imagery, in the model can potentially generate better results. More research in this area is warranted.

#### *4.2. Evaluating Crop Nutrient Status*

Precision farming involves evaluating the crop nutrient status and providing recommendations on site-specific resource management according to crop needs [206]. Such an approach is critical for improving the resource use efficiency and reducing environmental impacts [4,103]. Previous studies have used hyperspectral images for estimating the nitrogen content of different crop types, as shown in Table 9.


**Table 9.** Selected previous studies estimating the nitrogen content for different crop types using hyperspectral images.


**Table 9.** *Cont.*

Overall, owing to the large amount of spectral information in hyperspectral imagery, crop nutrient status can be evaluated with high accuracies, and a corresponding fertilizer treatment plan can be proposed to achieve optimal crop productions. However, it is also essential to keep in mind that there is a wide range of factors, such as soil moisture, soil type, and topographic conditions, that can impact crop growth and production. A more comprehensive treatment plan that takes into consideration both the crop nutrient status and other influencing factors can make a greater contribution to crop production.
