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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, 2024, Vol. 21, No. 1, pp. 88-105

Algorithm for retrieving the optical depth of single-layer horizontally inhomogeneous clouds using a neural network

T.V. Russkova 1 , A.V. Skorokhodov 1 
1 V.E. Zuev Institute of Atmospheric Optics SB RAS, Tomsk, Russia
Accepted: 11.12.2023
DOI: 10.21046/2070-7401-2024-21-1-88-105
A neural network technique for retrieving the optical thickness of horizontally inhomogeneous clouds is presented. To train the neural network, sets of reflected solar radiation intensity values in the visible and short-wave infrared regions of the spectrum were used. Radiative transfer simulation in marine stratocumulus clouds was carried out using the Monte Carlo method, and a fractal model based on the method of limited cascades was used to generate cloud fields. The difference between the presented technique and the classical IPA/NIPA (Independent Pixel Approximation/Nonlocal Independent Pixel Approximation) schemes is that the former allows you to integrate radiometric data in the required quantity and take into account the effects of horizontal radiation transfer not only within the target pixel, but also adjacent areas. In addition, without significantly increasing the complexity of the algorithm, additional parameters can be included in the vector of retrieved characteristics, in particular, the indicator of the relative heterogeneity of the optical thickness and the cloud fraction within the observation pixel. The paper examines the dependence of the accuracy of solving the inverse problem on the architecture and values of the hyperparameters of the neural network, the volume and structure of the training sample. High values of the correlation coefficient (0.95–0.99) were achieved between the original and retrieved values of the optical thickness at a fixed value of the effective radius of cloud particles. It is shown that when additional information about the reflected radiation in adjacent pixels is used and the spatial resolution is reduced within the considered range, the standard deviation of the optical thickness decreases and the correlation coefficient increases.
Keywords: remote sensing, broken clouds, optical thickness, inverse problems, numerical simulation, solar radiative transfer, Monte Carlo method, neural networks
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