Innovative surface reflectance retrieval from UV satellites

The knowledge of the surface reflectance is essential for the retrieval of atmospheric trace-gases from satellites. It is required in the conversion of the observed trace gas slant column to the total vertical column by means of a so-called air-mass factor. Although there exists climatological databases based on UV satellite data (e.g. OMI, GOME-2), these have a low spatial resolution and are not appropriate for current and future UV satellite missions like Sentinel-5p/TROPOMI or MTG-S/UVN (Sentinel-4) due their significantly higher spatial and spectral resolution. Current climatologies which are used in operational retrievals provide the Lambertian Equivalent Reflection (LER, e.g. OMI, GOME-2, TROPOMI, see [1,2,3]) and Directional-LER (DLER, e.g. GOME-2, TROPOMI see [3,4]) for selected wavelength in the UV-VIS range and are based on the so-called minimum LER approach, i.e. determine the minimum surface reflectance in the measurement timeframe.

We present here a new technique called GE_LER (Geometry-dependent Effective Lambertian Equivalent Reflectivity) based on Machine Learning, which retrieves the DLER from UV satellites in a wavelength range as opposed to the single wavelength approaches of existing climatologies. In this way, dedicated surface reflectivities for specific trace gas retrieval wavelength ranges can be determined. We train a Neural Network with simulated UV spectra, which have been calculated with (V)LIDORT (see [5]). This radiative transfer model is also used for the generation of Air Mass Factors in the operational TROPOMI trace gas retrieval. In this way we reduce the influence of using different radiative transfer models with respect to trace gas retrievals.

First results of our GE_LER retrieval for several trace-gases based on TROPOMI data will be shown.

 

References

[1] Kleipool (2010), OMI/Aura Surface Reflectance Climatology L3 Global Gridded 0.5 degree x 0.5 degree V3, Greenbelt, MD, USA, Goddard Earth Sciences Data and Information Services Center (GES DISC),, 10.5067/Aura/OMI/DATA3006

[2] Tilstra et al. (2017), Surface reflectivity climatologies from UV to NIR determined from Earth observations by GOME-2 and SCIAMACHY, J. Geophys. Res. Atmos. 122, 4084-4111, doi:10.1002/2016JD025940

[3] Tilstra et al. (2021), Directionally dependent Lambertian-equivalent reflectivity (DLER) of the Earth's surface measured by the GOME-2 satellite instruments, Atmos. Meas. Tech. 14, 4219-4238, doi:10.5194/amt-14-4219-2021

[4] Tilstra et al. (2023), A directional surface reflectance climatology determined from TROPOMI observations, Atmos. Meas. Tech. Discuss. [preprint], doi:10.5194/amt-2023-222, in review

[4] Spurr et al. (2008), LIDORT and VLIDORT: Linearized pseudo-spherical scalar and vector discrete ordinate radiative transfer models for use in remote sensing retrieval problems. Light Scattering Reviews, Volume 3, ed. A. Kokhanovsky, Springer