The ability to investigate the Earth’s environment will be greatly improved by hyperspectral satellite data. The FLuorescence EXplorer (FLEX) will be the first hyperspectral mission designed to monitor the photosynthetic activity of the terrestrial vegetation layer by using a completely novel technique measuring the sun-induced chlorophyll fluorescence (SIF) signal that originates from the core of the photosynthetic machinery. In preparation of the upcoming FLEX satellite mission that will be launched in 2022 a large field campaign, namely FLEXSense, was conducted in summer 2018 including representative study sites at several locations in middle and south Europe as well as North America.During the different/various field activities, airborne data was acquired with the hyperspectral airborne imager HyPlant, whichthat consists of two sensor heads. The DUAL module is a line-imaging push-broom sensor, which providinges contiguous spectral information from 370 to 2500 nm. The vegetationchlorophyll fluorescence signal is measured with a separate push-broom sensor, the FLUO module, which produces data at high spectral resolution (0.25 nm) in the spectral region of the two oxygen absorption bands covering a range from 670 to 780 nm. Currently, two different algorithms are used routinely to retrieve red (SIF680) and far-red SIF (SIF760) from HyPlant data. Both methods are based on the oxygen absorption bands., but wWhile the improved Fraunhofer Line Depth (iFLD) method employs a semi-empirical atmospheric correction (i.e., bare-soils), the Spectral Fitting method (SFM) makes useis based onof a physically-based atmospheric modeling (MODTRAN5 code). A common method of testing the reliability of remotely-sensed SIF (in this study airborne maps) is the comparison with “ground truth” data. In many cases, however, ground measurements of SIF are not available or are too work-intensive to be measured at regional level. For that reason we developedwant to present an alternative approach how the quality of airborne SIF maps can be assessed. For this purpose we applyhave developed so-called ’quality criteria’, which should help to find errors and artefacts that have arisen during the SIF retrieval. This method was applied to determine the quality of individual SIF maps derived from HyPlant images acquired during the 2018 FLEXSense campaign. The application of the proposed quality features proved to be a valuable tool for assessing the quality of SIF maps derived from HyPlant airborne data. Therefore, we propose to apply the different criteria even in the case of a with sufficient number of ground reference measurements are available, as because they provide important additional information about the quality of spatial SIF products is provided, especially when comparing the outputs of different retrieval methods.