Residue management on agriculture land is a practice of great importance in southwestern Ontario, where soil management practices have an important effect on Great Lakes water quality. The ability of tillage or planting system to maintain soil residue cover is currently measured by using one or more of the common methods, line transect (e.g. knotted rope, Meter stick) and photographic (grid, script, and image analysis) methods. Each of these techniques has various advantages and disadvantages; however, a common feature of each is that they tend to be laborious and time-consuming to complete properly. For these reasons, few landowners make the effort to measure their field residue levels. Therefore, there is a need to identify low-cost, reliable, quick and easy to implement methods for residue estimation. Remote sensing imagery is a valuable tool to assess and map tillage practices, crop residue cover and cover crops over large areas. However, monitoring and deriving information on crop residue cover from space has been restricted in the past by low spectral and spatial-temporal resolution. The evolution of information technologies and a better understanding of the interactions between electromagnetic radiation and cover crop have opened up new potential for Unmanned Aerial Vehicle (UAV) remote sensing applications. The aim of this study is to determine whether very high resolution multi-spectral remote sensing datasets derived  from UAV can be used as alternative method of crop residue estimation for soil cover (bare and crop residue) quantification. To do so, multi-spectral and RGB field images are collected with UAV at the Elora Research Station of the University of Guelph in southwestern Ontario over plots from different long term crop rotations and tillage systems and analyzed with imagery processing methods. Several flight configurations of monitoring UAV to acquire remote images are tested (i.e., altitudes (10–50 m) and modes (stop and cruising)). Results from the UAV approach will be compared against a benchmark, the photographic proximal remote sensing method. This UAV alternative method could be used for future data collection efforts for high quality ground datasets of residue amounts. Such quantitative information on the amount of crop residue cover by field is essential to understand the state of soil management and can used, for example, as input for assessment and modeling of soil carbon sequestration.