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

Abstract

Abstract. 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 is absent. This paper presents an independent, simple and robust method to retrieve 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 2D 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 retrieval. 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 commonly used 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.

Parameterizing the instrumental spectral response function and its changes by a super-Gaussian and its derivatives

“…The SG S, however, is particularly suited for this approach due to the limited number of parameters, i.e., PAs, and the tangible meaning of the parameters w (width) and k (shape) and, optionally, a w (asymmetry). The same would hold for a parameterization based on a measured ISRF tuned by, e.g., widening or sharpening parameters as in Sun et al (2016), which might be preferable when high-quality pre-launch measurements of the ISRF of satellite instruments are available and analytical parameterizations do not meet accuracy requirements.…”

“…Nadarajah (2005) denotes it as generalized normal distribution and provides an overview of its mathematical characteristics. Recently, it has also been used to describe the spatial field of view of OMI (de Graaf et al, 2016;Sihler et al, 2016).…”

MICRU: an effective cloud fraction algorithm designed for UV–vis satellite instruments with large viewing angles

“…It is noted that the along-track pixel size increases with increasing VZA due to the Earth's curvature. Hence, the pixel shape becomes trapezoidal (de Graaf et al, 2016;Sihler et al, 2017).…”

“…Spectral convolution kernel MSC RT Comment Munro et al, 2016). Spatial aliasing is caused by the sequential detector readout in connection with the movement of the GOME-2 scanning mirror (Sihler et al, 2017). The comparison of MICRU results from MSC channels 2, 5, 10, and 11 allows the investigation of spatial aliasing.…”

“…Compared to imager instruments, spectrometers are characterised by lower spatial but much higher spectral resolution. For example, the Visible Infrared Imaging Radiometer Suite (VIIRS) imager features 22 spectral channels at a resolution of approximately 400 m, whereas the spectrometer Ozone Monitoring Instrument (OMI) offers 1176 spectral channels at a nadir resolution of 13 km × 24 km (Schueler et al, 2002;Levelt et al, 2006;KNMI, 2019;de Graaf et al, 2016;Sihler et al, 2017). Based on this difference of spatial resolution, it becomes evident that the probability to obtain a completely cloud-free observation over a certain location is much higher for imagers than for spectrometers (Krijger et al, 2007).…”