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Knowledge of the field of view (FOV) of a remote sensing instrument isparticularly important when interpreting their data and merging them withother spatially referenced data. Especially for instruments in space,information on the actual FOV, which may change during operation, may bedifficult to obtain. Also, the FOV of ground-based devices may change duringtransportation to the field site, where appropriate equipment for the FOV determination may beunavailable. This paper presents an independent, simple and robust method to retrieve theFOV of an instrument during operation, i.e. the two-dimensional sensitivitydistribution, sampled on a discrete grid. The method relies on correlatedmeasurements featuring a significantly higher spatial resolution, e.g. byan imaging instrument accompanying a spectrometer. The method was applied totwo satellite instruments, GOME-2 and OMI, and a ground-based differentialoptical absorption spectroscopy (DOAS) instrument integrated in anSO2 camera. For GOME-2, quadrangular FOVs could be retrieved, whichalmost perfectly match the provided FOV edges after applying a correction forspatial aliasing inherent to GOME-type instruments. More complex sensitivitydistributions were found at certain scanner angles, which are probably causedby degradation of the moving parts within the instrument. For OMI, which doesnot feature any moving parts, retrieved sensitivity distributions were muchsmoother compared to GOME-2. A 2-D super-Gaussian with six parameters wasfound to be an appropriate model to describe the retrieved OMI FOV. Thecomparison with operationally provided FOV dimensions revealed smalldifferences, which could be mostly explained by the limitations of our IFRimplementation. For the ground-based DOAS instrument, the FOV retrieved usingSO2-camera data was slightly smaller than the flat-discdistribution, which is assumed by the state-of-the-art correlation technique.Differences between both methods may be attributed to spatialinhomogeneities. In general, our results confirm the already deduced FOV distributions of OMI,GOME-2, and the ground-based DOAS. It is certainly applicable for degradationmonitoring and verification exercises. For satellite instruments, the gainedinformation is expected to increase the accuracy of combined products, wheremeasurements of different instruments are integrated, e.g. mapping ofhigh-resolution cloud information, incorporation of surface climatologies.For the SO2-camera community, the method presents a new andefficient tool to monitor the DOAS FOV in the field

In-operation field-of-view retrieval (IFR) for satellite and ground-based DOAS-type instruments applying coincident high-resolution imager data