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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, 2020, Vol. 17, No. 2, pp. 30-39

Evaluation of the SREM atmospheric correction algorithm applicability to time series analysis using Landsat data example and its open source software implementation

E.E. Kazakov 1 , Yu.I. Borisova 1, 2 
1 State Hydrological Institute, Saint Petersburg, Russia
2 Saint Petersburg State University, Saint Petersburg, Russia
Accepted: 11.03.2020
DOI: 10.21046/2070-7401-2020-17-2-30-39
The problem of Earth remote sensing data atmospheric correction remains one of the most uncertain, but at the same time acute, especially for processing and interpretation of different-time data. The provision of terrestrial atmospheric data for most of the planet is still unsatisfactory, and the qualitative configuration of atmospheric correction models involving assimilation of atmospheric data is often very difficult, especially for non-specialists. The SREM atmospheric correction algorithm, proposed by an international team of scientists in 2019, does not require additional atmospheric data other than those already contained in the metadata of a satellite image. Authors show a high level of correction quality. In this paper, we offer a description of the SREM algorithm in Russian, an additional assessment of the quality of the SREM algorithm for Landsat data based on a comparison of correction results with the results of several common methods (authoritative LaSRC algorithm, popular DOS algorithm, raw non-corrected reflectance data) for the annual time series, as well as an open software implementation of SREM in Python, available for public use, which, we hope, will help to attract the attention of the professional community to the original algorithm. Additional quality assessment shows satisfactory performance for some spectral ranges (0.53–0.88 microns) and index images (e.g. NDVI), and unsatisfactory for others (<0.53 µm, >0.88 µm, index images based on them, e.g. NDWI, NDBI).
Keywords: Landsat, atmospheric correction, SREM, LaSRC, DOS, time-series, surface reflectance, NDVI, NDBI, NDWI
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