ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2020, Vol. 17, No. 7, pp. 26-38

Estimation of the accuracy of cloud masking algorithms using Sentinel-2 and PlanetScope data

A.V. Tarasov 1 
1 Perm State National Research Univercity, Perm, Russia
Accepted: 05.11.2020
DOI: 10.21046/2070-7401-2020-17-7-26-38
Nowadays, many automated satellite-based monitoring systems (e. g. for forestry or agriculture) widely use the images from Sentinel-2 and PlanetScope satellites, which combine high spatial and temporal resolution. An important challenge in the building of such monitoring systems is high-quality preprocessing of the images, including cloud masking. Traditional (threshold-based) cloud masking algorithms use only spectral reflectance in the visible and infrared (IR) bands. Also the machine learning methods (which take into account not only spectral characteristics but also geometry and texture) are successfully used for cloud masking on satellite images in recent decade. In this study, we estimated accuracy of cloud masking on Sentinel-2 images obtained at different seasons with the use of traditional Fmask and Sen2Cor algorithms and machine-learning-based S2cloudless algorithm, on the example of the Perm Region territory. For Sentinel-2 data, S2cloudless showed the best result (average accuracy 83 %), and the lowest accuracy was obtained with Fmask (70 %). The seasonal variability of cloud masking accuracy did not exceed 6 %. The applicability of the listed algorithms for PlanetScope data having only visible and near-IR spectral bands was also estimated. It was found that S2cloudless can be used for PlanetScope images obtained in winter, since the cloud masking accuracy is higher than that the standard cloud mask product delivered with the data.
Keywords: cloud masking, machine learning, PlanetScope, Sentinel-2
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