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Leaf area index (LAI) is involved in biological, environmental and physiological processes, which are related to photosynthesis, transpiration, interception of radiation and energy balance. Thus, most crop models use LAI as a key feature to characterize the growth and development of crops. However, direct measures of LAI are destructive and tedious so that samplings can seldom be repeated in time and in space. Green canopy cover (GCC) is directly involved in crop growth and development. GCC estimation can benefit from aerial observation, as it can be measured by using image analysis or estimated by obtaining different vegetation indices. The main purpose of the second part of this paper was to study the relationships between GCC and LAI by using aerial images from UAVs in order to characterize crop growth. Also, the relationships between GCC and a vegetation index based on the visible spectrum was calibrated and validated. Relationships between LAI and GCC, growing degree days (GDD) and GCC and GDD and LAI were calibrated and validated for maize and onion crops with proper fitting. Visible atmospherically resistant index also appears to be a sensitive indicator to different growing stages and could generally be applied to any field crop. To apply this methodology, GCC and LAI relationships must be calibrated for many other crops in different irrigable areas. In addition, the cost of the UAV is expected to decrease while autonomy increases through improved battery life and reductions in the weight of on-board sensors.