On 4 days in summer and winter the mixing layer height over the municipal area of Munich, Germany, was determined by several remote sensing instruments and in situ probes. The main motivation was to obtain information on aerosols, and therefore we decided to understand the mixing layer as that layer where most of the locally produced aerosols are concentrated. In this paper we wanted to investigate the potential of the quite different methodologies which depend on measurements of aerosol properties and those which do not. The operation of two lidars, a ceilometer, a wind-temperature-radar, a sodar, radiosondes, and aerosol probes onboard of a microlight aircraft allowed such a thorough intercomparison. As the instruments were located at different sites, the horizontal homogeneity of the mixing layer could also be observed. It was found that the agreement between the different methodologies is very good as long as the mixing layer height does not exceed approximately 1 km, which is the common measurement range of all instruments. In summer, however, the mixing layer can reach 2 km and more, so that the lidar turns out to be the most capable remote sensing technique. Another advantage of the lidar is the possibility to clearly derive the internal structure of the mixing layer. The latter is important in cases when simple parameterizations assume vertical homogeneity of aerosol properties within the mixing layer. On the other hand, lidars are quite expensive and require a trained operator. As a conclusion, the development of unattended working lidars including automated data evaluation should be fostered. From the limited data set it was found that the mixing layer height in Munich did not change more than approximately 100 m over a horizontal distance of around 50 km. If this finding can be confirmed by further measurements, the area of Munich is a good test bed for the validation of aerosol retrievals from satellite data with medium spatial resolution and for the validation of the numerical treatment of aerosols in mesoscale chemistry transport models.