Deriving aerosol parameters from in–situ spectrometer measurements for validation of remote sensing products

Sebastian Riedel; Joanna Janas; Peter Gege; Natascha Oppelt

doi:10.1117/12.2280290

6 October 2017 Deriving aerosol parameters from in–situ spectrometer measurements for validation of remote sensing products

Sebastian Riedel, Joanna Janas, Peter Gege, Natascha Oppelt

Author Affiliations +

Proceedings Volume 10424, Remote Sensing of Clouds and the Atmosphere XXII; 1042404 (2017) https://doi.org/10.1117/12.2280290
Event: SPIE Remote Sensing, 2017, Warsaw, Poland

Abstract

Uncertainties of aerosol parameters are the limiting factor for atmospheric correction over inland and coastal waters. For validating remote sensing products from these optically complex and spatially inhomogeneous waters the spatial resolution of automated sun photometer networks like AERONET is too coarse and additional measurements on the test site are required. We have developed a method which allows the derivation of aerosol parameters from measurements with any spectrometer with suitable spectral range and resolution. This method uses a pair of downwelling irradiance and sky radiance measurements for the extraction of the turbidity coefficient and aerosol Ångström exponent. The data can be acquired fast and reliable at almost any place during a wide range of weather conditions. A comparison to aerosol parameters measured with a Cimel sun photometer provided by AERONET shows a reasonable agreement for the Ångström exponent. The turbidity coefficient did not agree well with AERONET values due to fit ambiguities, indicating that future research should focus on methods to handle parameter correlations within the underlying model.

Conference Presentation

Citation Download Citation

Sebastian Riedel, Sebastian Riedel, Joanna Janas, Joanna Janas, Peter Gege, Peter Gege, Natascha Oppelt, Natascha Oppelt, } "Deriving aerosol parameters from in–situ spectrometer measurements for validation of remote sensing products", Proc. SPIE 10424, Remote Sensing of Clouds and the Atmosphere XXII, 1042404 (6 October 2017); doi: 10.1117/12.2280290; https://doi.org/10.1117/12.2280290

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