Abstract
This work is devoted to estimating the spectral course of validation error of satellite and in situ measurements of remote sensing reflectance for various atmospheric conditions. During data validation, a number of systematic errors of standard algorithms were noted, for example, negative values of remote sensing reflectance in the short-wavelength region at 412 and 443 nm in the presence of dust in the atmosphere. It is shown that the modern approach to determining aerosol light scattering in the short-wavelength part of the visible range by extrapolating the signal from the near-IR region is not sufficiently correct from a physical point of view, and similar solutions by the interpolation method have more accurate estimates. The obtained results show that in the presence of an absorbing aerosol, the spectral law of atmospheric correction errors is close to the function λ–4. This effect is explained by the fact that dust aerosol is determined by remote sensing methods using the Gordon and Wang algorithms using the infrared channel, but arid aerosol has the main effect on the ratio of the aerosol and molecular components (shortwave range). This paper presents trends for further interpolation of satellite data not only under the condition of a clean atmosphere, but also in the presence of an absorbing aerosol. Experimental patterns of validation error for Aqua MODIS have been obtained.
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Funding
The work was carried out with the financial support of the Russian Foundation for Basic Research (scientific project no. 19-35-90066), as well as within the framework of a state order of the Marine Hydrophysical Institute of the Russian Academy of Sciences (no. 0827-2021-0002, 0555-2021-0003 “Development of Methods of Operational Oceanology Based on Interdisciplinary Studies of the Processes of Formation and Evolution of the Marine Environment and Mathematical Modeling Using Remote and Contact Measurement Data”).
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Translated by N. Petrov
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Shybanov, E.B., Papkova, A.S. Differences in the Ocean Color Atmospheric Correction Algorithms for Remote Sensing Reflectance Retrievals for Different Atmospheric Conditions. Cosmic Res 61 (Suppl 1), S35–S40 (2023). https://doi.org/10.1134/S0010952523700697
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DOI: https://doi.org/10.1134/S0010952523700697