Analytical models and traffic microsimulation are two widely used platforms for evaluating roundabout operations. The application of the correct inputs and proper specification of calibration parameters should precede the actual simulation, to replicate field traffic conditions. In this sense, simultaneous data collection and estimation of the input, calibration, and validation variables, along with knowledge of their definitions, are crucial. Although simultaneity of data gathering is virtually guaranteed with the use of wide-frame videos captured with an unmanned aerial system (UAS), there are cases where sight distance restrictions may obscure observations of the back of queue and arrival patterns. This paper explores the calibration and validation efforts associated with an analytical platform, SIDRA 9, and a microsimulation model, TransModeler 5, conducted under sight-restricted conditions. Video captured from a drone, followed by trajectory extraction using video processing software, was used to analyze operations on two approaches at a single-lane roundabout. In the process, the team employed a specialized demand estimation method, and developed a novel data collection scheme for estimating the critical headway distribution in TransModeler 5. Because of sight distance constraints, the model validation was limited to the use of the observable system travel time and associated travel speed within the field of view. The comparison results, for both platforms, have confirmed the value of model calibration in more accurately describing field performance. The calibrated models performed differently between the two approaches, with the approach having a larger presence of buses and heavy vehicles yielding slightly poorer results.