High-intensity agricultural production is very commonly associated with negative impacts on the environment. In addition, the substantial increase in fertilizer prices over the past years has severely impacted both agricultural profitability and overall food costs. As a result, there is increasing economic and environmental pressure to improve and optimize crop production. In precision agriculture (PreAg), the issue of managing what-when-where is one of the major research priorities to counteract these negative consequences. An increasingly explored approach to support such management processes is the non-destructive spectral determination of plant characteristics using optical sensors on UAV platforms. In this contribution, previous study results on crop trait detection from a winter wheat field trial in 2020 are compared with a repeated field trial in 2021 using a special and unique UAV-based multispectral multi-camera imaging system for the short-wave infrared (SWIR). For this purpose, two vegetation indices (VIs), NRI and GnyLi were derived from the spectral image data for each of the two survey dates and examined in simple linear regression (SLR) analyses with previously collected and analyzed ground truth data. The results of these SLR analyses could then in turn be compared to each other. The results of the comparison, especially for the NRI, show a positive trend in the repeatability of the previous study results for crop trait detection.