27 Sep 2021

27 Sep 2021

Review status: this preprint is currently under review for the journal AMT.

Deep Learning Based Post-Process Correction of the Aerosol Parameters in the High-Resolution Sentinel-3 Level-2 Synergy Product

Antti Lipponen1, Jaakko Reinvall2, Arttu Väisänen2, Henri Taskinen2, Timo Lähivaara2, Larisa Sogacheva1, Pekka Kolmonen1, Kari Lehtinen1,2, Antti Arola1, and Ville Kolehmainen2 Antti Lipponen et al.
  • 1Finnish Meteorological Institute, Atmospheric Research Centre of Eastern Finland, Kuopio, Finland
  • 2University of Eastern Finland, Department of Applied Physics, Kuopio, Finland

Abstract. Satellite-based aerosol retrievals provide global spatially distributed estimates of atmospheric aerosol parameters that are commonly needed in applications such as estimation of atmospherically corrected satellite data products, climate modeling and air quality monitoring. However, a common feature of the conventional satellite aerosol retrievals is that they have reasonably low spatial resolution and poor accuracy caused by uncertainty in auxiliary model parameters, such as fixed aerosol model parameters, and the approximate forward radiative transfer models utilized to keep the computational complexity feasible. As a result, the improvement and re-processing of the operational satellite data retrieval algorithms would become a tedious and computationally excessive problem. To overcome these problems, we have developed a machine learning-based post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite retrieval data and a post-processing step where a machine learning algorithm is utilized to predict the approximation error in the conventional retrieval. With approximation error we refer to the discrepancy between the true aerosol parameters and the ones retrieved using the satellite data. Our hypothesis is that the prediction of the approximation error with a finite training data set is a less complex and easier task than the direct fully learned machine learning based prediction in which the aerosol parameters are directly predicted given the satellite observations and measurement geometry. With our approach, there is no need to re-run the existing retrieval algorithms and only a computationally feasible post-processing step is needed. Our approach is based on neural networks trained based on collocated satellite data and accurate ground based AERONET aerosol data. Based on our post-processing approach, we propose a post-process corrected high resolution Sentinel-3 Synergy aerosol product, which gives a spectral estimate of the aerosol optical depth at five different wavelengths with a high spatial resolution equivalent to the native resolution of the Sentinel-3 level-1 data (300 meters at nadir). With aerosol data from Sentinel-3A and 3B satellites, we demonstrate that our approach produces high-resolution aerosol data with better accuracy than the operational Sentinel-3 level-2 Synergy aerosol product or a conventional fully learned machine learning approach.

Antti Lipponen et al.

Status: open (until 02 Nov 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse

Antti Lipponen et al.

Model code and software

POPCORN Sentinel-3 Synergy aerosol parameter post-process correction Finnish Meteorological Institute and University of Eastern Finland

Video supplement

Animation of Sentinel-3 aerosol optical depth over Europe in 2019 Antti Lipponen

Antti Lipponen et al.


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Short summary
We have developed a machine-learning-based model that can be used to correct the Sentinel-3 satellite based aerosol parameter data of the Synergy data product. The strength of the model is that the original satellite data processing does not have to be carried out again but the correction can be carried out to the data already available. We show that the correction significantly improves the accuracy of the satellite aerosol parameters.