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ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa
CURRENT PROBLEMS IN REMOTE SENSING OF THE EARTH FROM SPACE

  

Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2026, V. 23, No. 4, pp. 377-389

On comparison of NO2 profile estimation methods based on MAX-DOAS measurements

O.V. Postylyakov 1 , A.N. Borovski 1 , D.R. Shamsutdinov 1 , A.I. Chulichkov 2, 1 
1 A.M. Obukhov Institute of Atmospheric Physics RAS, Moscow, Russia
2 Lomonosov Moscow State University, Moscow, Russia
Accepted: 24.02.2026
DOI: 10.21046/2070-7401-2026-23-4-377-389
We compared possible methods for retrieving the vertical profiles (VP) of nitrogen dioxide (NO2) in the lower troposphere based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) measurements. Along with traditionally used linear statistical estimates, nonlinear methods that have not previously been applied to solve this problem are considered. The considered nonlinear methods, including artificial neural networks and linear programming, allow using a priori information about the positivity of the reconstructed vertical distribution and the expected number of extrema in the profile. Numerical experiments were conducted to compare the results obtained using the new methods with those obtained using linear statistical estimation methods. Assuming that the a priori distribution function of NO2 VP is known, an estimate was obtained using an ANN (Artificial Neural Network) that has a smaller root-mean-square error than the optimal linear estimate. A comparison of the optimal statistical estimation and linear programming estimates indicates that the preferred retrieval method (with a lower root-mean-square error) depends on the measurement noise and the retrievable time series itself. In general, it can be noted that, for high signal-to-noise ratios and NO2 layers located at lower tropospheric altitudes, the linear programming method performs better than the optimal statistical estimation method. As the signal-to-noise ratio deteriorates, preference shifts to the optimal statistical estimation method. The choice of the preferred estimation method should be made on the basis of specific measurement conditions and expected homogeneity of the pollutant VP.
Keywords: MAX-DOAS, nitrogen dioxide, aerosol, vertical distribution, optimal statistical estimation, unbiased linear estimation, linear programming
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