Knowledge of the field of view (FOV) of a remote sensing instrument is particularly important when interpreting their data and merging them with other spatially referenced data. Especially for instruments in space, information on the actual FOV, which may change during operation, may be difficult to obtain. Also, the FOV of ground-based devices may change during transportation to the field site, where appropriate equipment for the FOV determination may be unavailable. This paper presents an independent, simple and robust method to retrieve the FOV of an instrument during operation, i.e. the two-dimensional sensitivity distribution, sampled on a discrete grid. The method relies on correlated measurements featuring a significantly higher spatial resolution, e.g. by an imaging instrument accompanying a spectrometer. The method was applied to two satellite instruments, GOME-2 and OMI, and a ground-based differential optical absorption spectroscopy (DOAS) instrument integrated in an SO2 camera. For GOME-2, quadrangular FOVs could be retrieved, which almost perfectly match the provided FOV edges after applying a correction for spatial aliasing inherent to GOME-type instruments. More complex sensitivity distributions were found at certain scanner angles, which are probably caused by degradation of the moving parts within the instrument. For OMI, which does not feature any moving parts, retrieved sensitivity distributions were much smoother compared to GOME-2. A 2-D super-Gaussian with six parameters was found to be an appropriate model to describe the retrieved OMI FOV. The comparison with operationally provided FOV dimensions revealed small differences, which could be mostly explained by the limitations of our IFR implementation. For the ground-based DOAS instrument, the FOV retrieved using SO2-camera data was slightly smaller than the flat-disc distribution, which is assumed by the state-of-the-art correlation technique. Differences between both methods may be attributed to spatial inhomogeneities. In general, our results confirm the already deduced FOV distributions of OMI, GOME-2, and the ground-based DOAS. It is certainly applicable for degradation monitoring and verification exercises. For satellite instruments, the gained information is expected to increase the accuracy of combined products, where measurements of different instruments are integrated, e.g. mapping of high-resolution cloud information, incorporation of surface climatologies. For the SO2-camera community, the method presents a new and efficient tool to monitor the DOAS FOV in the field.@en

The Ozone Monitoring Instrument (OMI) is a push-broom imaging spectrometer, observing solar radiation backscattered by the Earth's atmosphere and surface. The incoming radiation is detected using a static imaging CCD (charge-coupled device) detector array with no moving parts, as opposed to most of the previous satellite spectrometers, which used a moving mirror to scan the Earth in the across-track direction. The field of view (FoV) of detector pixels is the solid angle from which radiation is observed, averaged over the integration time of a measurement. The OMI FoV is not quadrangular, which is common for scanning instruments, but rather super-Gaussian shaped and overlapping with the FoV of neighbouring pixels. This has consequences for pixel-area-dependent applications, like cloud fraction products, and visualisation.

The shapes and sizes of OMI FoVs were determined pre-flight by theoretical and experimental tests but never verified after launch. In this paper the OMI FoV is characterised using collocated MODerate resolution Imaging Spectroradiometer (MODIS) reflectance measurements. MODIS measurements have a much higher spatial resolution than OMI measurements and spectrally overlap at 469 nm. The OMI FoV was verified by finding the highest correlation between MODIS and OMI reflectances in cloud-free scenes, assuming a 2-D super-Gaussian function with varying size and shape to represent the OMI FoV. Our results show that the OMPIXCOR product 75FoV corner coordinates are accurate as the full width at half maximum (FWHM) of a super-Gaussian FoV model when this function is assumed. The softness of the function edges, modelled by the super-Gaussian exponents, is different in both directions and is view angle dependent.

The optimal overlap function between OMI and MODIS reflectances is scene dependent and highly dependent on time differences between overpasses, especially with clouds in the scene. For partially clouded scenes, the optimal overlap function was represented by super-Gaussian exponents around 1 or smaller, which indicates that this function is unsuitable to represent the overlap sensitivity function in these cases. This was especially true for scenes before 2008, when the time differences between Aqua and Aura overpasses was about 15 min, instead of 8 min after 2008. During the time between overpasses, clouds change the scene reflectance, reducing the correlation and influencing the shape of the optimal overlap function.@en