Remote sensing provides opportunities for diverse applications in agriculture. One consideration of maximizing the utility of these applications, is the need to choose the most efficient spectral resolution. Picking the optimal spectral resolutions (multi, super or hyper) for a specific application is also influenced by other factors (e.g., spatial and temporal resolutions) of the utilized device. This work focuses mainly on the analysis of the effect of different and combined spectral resolutions throughout the research, towards practical implementation for wheat (as a crop model). Hyperspectral data can be examined as a whole spectrum in order to relate it to specific phenomenon or compare it to other spectrum. No doubt, there is no need for hyperspectral data if a phenomenon can be detected by specific wavelengths. Nevertheless, hyperspectral data is valuable for identifying these specific wavelengths as well as resampling data for simulating multi or super (above 10 bands) spectral sensors. In addition to the analysis of the entire spectrum, the effect of different resolutions can be analyzed and vegetation indices can be calculated by combining several bands or specific wavelengths that will be slightly modified by the different resolutions. The crop model, wheat, presents applications based on different spectral resolutions. The applications that will be discussed in this work are: (1) evaluation of nutrients and water contents in plants, phenology, and LAI using quantitative statistical models and vegetation indices; (2) spectral separation between crop and weeds; (3) forecasting yield and yield quality; and (4) scaling up ground level applications to airborne hyperspectral sensor or spaceborne super spectral satellites. Regarding costs and data processing procedure simplicity, we conclude that each application must be based on specific spectral bands/wavelength that should be picked ad hoc, hence, using an intermediate alternative as the super spectral resolutions should be considered as the selected way toward comprehensive application tool in agriculture.