Horizontal heterogeneity can significantly affect the flux data quality at monitoring sites in complex terrain. In heterogeneous conditions, the adoption of the eddy-covariance technique is contraindicated by the lack of horizontal homogeneity and presence of advective conditions. In addition, uncertainty concerning the sources or sinks influencing a measurement compromises the data interpretation. The consideration of the spatial context of a measurement, defined by a footprint analysis, can therefore provide an important tool for data quality assessment. This study presents an update of an existing footprint-based quality evaluation concept for flux measurement sites in complex terrain. The most significant modifications in the present version are the use of a forward Lagrangian stochastic trajectory model for the determination of the spatial context of the measurements, and the determination of effective roughness lengths with a flux aggregation model in a pre-processing step. Detailed terrain data gathered by remote sensing methods are included. This approach determines spatial structures in the quality of flux data for varying meteorological conditions. The results help to identify terrain influences affecting the quality of flux data, such as dominating obstacles upwind of the site, or slopes biasing the wind field, so that the most suitable footprint regions for the collection of high-quality datasets can be identified. Additionally, the approach can be used to evaluate the performance of a coordinate rotation procedure, and to check to what extent the measured fluxes are representative for a target land-use type.