## 3 Application of Hyperspectral RS into Plant RS and Military Actions
### Research Advances of Plant Hyperspectral RS Technology  
Due to the influence of electromagnetic waves, molecules, atoms, and electrons undergo diverse and complex transformations (e.g., oscillation, rotation, and transition) to varying degrees. Consequently, spectral reflection information forms absorption and reflection characteristics in specific wavelength bands that map to the morphological and structural characteristics and material components of plants [43]. The main influencing factors of vegetation spectral characteristics are pigment composition and content, cell structure, and water content. Hyperspectral RS can detect and extract plant functional traits (e.g., leaf biochemical characteristics and canopy structure characteristics) at large spatial scales and can even be used to identify different tree species and estimate some gene-level characteristics. At present, hyperspectral RS is widely used in fields such as vegetation monitoring, plant functional trait mapping, biodiversity estimation, and pest and disease control. In the 1960s and 1970s, researchers from the United States Department of Agriculture collected and analyzed spectral data from various leaves and their crushed materials, obtaining a total of 42 spectral characteristic bands corresponding to their physiological, physical, and chemical parameters in the entire wavelength range. Such research lays the foundation for identifying, monitoring, and estimating plant physiological and chemical parameters based on RS. [PERSON] et al. found the band at 700 nm is characteristic for plant chlorophyll identification [44]. [PERSON] and [PERSON] analyzed the feasibility and necessity of estimating chemical components of leaves based on hyperspectral RS [45]. Since then, research on using hyperspectral reflectance information to estimate plant physiological and physical parameters has been favored by researchers from various countries. [PERSON] et al. found the wavelengths at 550 and 675 nm can accurately estimate the nitrogen content of pimiento leaves [46]. [PERSON] et al. found, compared with the original spectrum, using the derivative transformation (first-order and second-order derivatives) of the spectral reflectance of leaves can significantly improve the estimation accuracy of plant physiological and physiochemical parameters, and the transformation of spectral derivatives can effectively eliminate environmental effects, such as background and noise [47]. [PERSON] et al. reported that the trilateral parameters extracted based on the first derivative transformation of the spectrum can more accurately estimate plant chlorophyll concentration, and, of note, the use of the red-edge position to estimate chlorophyll concentration was the most accurate [48]. [PERSON] further proposed the calculation concept of the normalized difference water index (NDWI) on the basis of plant moisture content [49]. [PERSON] et al. used two-band vegetation indices (PVI, RVI, WDVI, and NDVI) to invert and estimate rice biomass, and the inversion accuracy reached 0.97 [50]. [PERSON] and [PERSON] showed that nitrogen accumulation in corn plants can be accurately estimated by calculating the spectral reflectance ratio R780/R740 [51]. [PERSON] et al. compared the performances of a soybean chlorophyll content inversion model based on the partial least squares algorithm and the back-propagation (BP) neural network algorithm and found that the BP neural network algorithm can more accurately estimate the soybean chlorophyll content [52]. Recently, the use of hyperspectral data for identifying and classifying tree species has become a research hotspot. [PERSON] et al. used hyperspectral data to classify northern forest tree species and used three classifiers (e.g., support vector machine, random forest, and maximum likelihood method) to classify tree species, proving that hyperspectral data are very effective in classifying the forest types of northern forest tree species [53].