The work presented in this paper investigates the sensitivity of the hyperspectral remotely sensed data to the vegetation density under different soil moisture conditions. The research testbed comprised four corn plots with 4 different densities, one grass plot, and one bare soil plot. For this purpose, the hyperspectral data were recorded simultaneously as the field measurements, which included soil moisture and temperature, soil characterization (gravimetric soil moisture, bulk density, surface roughness), and vegetation measurements (biomass; plant height; leaf orientations, length, thickness; dielectric constant of stalks and leaves; stalk diameter and height). The findings of this study showed that physical and physiological aspects, as well as the structure of the vegetation, have noticeable effects on its spectral response. The results showed distinct spectral response among the different vegetation densities, thus biomass. They also showed that hyperspectral data are effective in detecting soil moisture variability and discriminating among vegetation densities and conditions. The hyperspectral data were in agreement with the ground data and discriminated among small variations in soil moisture and vegetation densities and conditions. This study also showed that the variation in the spectral variability from different vegetation densities becomes negligible when the vegetation leaves cover completely the ground surface.