**3. Methods for Processing and Analyzing Hyperspectral Images**

Hyperspectral images acquired by different platforms and sensors are typically provided in a raw format (e.g., digital numbers) that needs to be pre-processed (e.g., atmospheric, radiometric, and spectral corrections) to retrieve accurate spectral information. Afterward, different approaches can be used for analyzing the hyperspectral information and investigating various agricultural features

(e.g., crop and soil properties). A few commonly used methods include linear regression, advanced regression (e.g., PLSR), machine learning and deep learning (e.g., RF, CNN), and radiative transfer modelling (e.g., PROSPECT and PROSAIL). Researchers have used one or more of these methods for investigations of different agricultural features. In this section, the review is arranged based on the different methods used in the studies.
