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

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

Sihler, Holger (Max Planck Institute of Chemistry; University of Heidelberg)
Lübcke, Peter (University of Heidelberg)
Lang, Rüdiger (European Organisation for the Exploitation of Meteorological Satellites)
Beirle, Steffen (Max Planck Institute of Chemistry)
de Graaf, M. (TU Delft Atmospheric Remote Sensing; Royal Netherlands Meteorological Institute (KNMI))
Hörmann, Christoph (Max Planck Institute of Chemistry)
Lampel, Johannes (Max Planck Institute of Chemistry)
Penning de Vries, Marloes (Max Planck Institute of Chemistry)
Remmers, Julia (Max Planck Institute of Chemistry)
Trollope, Ed (European Organisation for the Exploitation of Meteorological Satellites; Telespazio VEGA Deutschland)
Wang, Yang (Max Planck Institute of Chemistry)
Wagner, Thomas (Max Planck Institute of Chemistry)

2017-03-09

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.

Satellite imagery

http://resolver.tudelft.nl/uuid:f531a003-492d-444e-b8ee-eae7f9fccd6a

https://doi.org/10.5194/amt-10-881-2017

1867-1381

Atmospheric Measurement Techniques, 10 (3), 881-903

Institutional Repository

journal article

© 2017 Holger Sihler, Peter Lübcke, Rüdiger Lang, Steffen Beirle, M. de Graaf, Christoph Hörmann, Johannes Lampel, Marloes Penning de Vries, Julia Remmers, Ed Trollope, Yang Wang, Thomas Wagner