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Izvestiya, Atmospheric and Oceanic Physics

, Volume 54, Issue 9, pp 1353–1362 | Cite as

Estimating the Water Turbidity in the Selenga River and Adjacent Waters of Lake Baikal Using Remote Sensing Data

  • M. K. TarasovEmail author
  • O. V. Tutubalina
METHODS AND MEANS OF SATELLITE DATA PROCESSING AND INTERPRETATION

Abstract

The relationship between the DN/reflectance values of Landsat 5 TM, Landsat 8 OLI, and U-K‑DMC2 SLIM-6-22 imagery and the concentration of total suspended matter (TSM) in the water was determined on the basis of field turbidity measurements in 2011 and 2013. The determination coefficient R2 for all of the relationships exceeds 0.84, indicating their high reliability. The average deviation of the calculated values from in-situ measurements varies from 2 to 7 mg/L (from 11 to 29% of the range of values). The most accurate model was obtained for the 2013 data, when the field turbidity measurements were most numerous (approximately 100). The concentration of suspended matter in the waters of Lake Baikal was mapped taking the effect of different penetration depths for solar radiation of different wavelengths into account. We also tested the applicability of imagery of Landsat and UK-DMC2 satellites for mapping the turbidity in the branches of the Selenga Delta and compared the results with the results of processing of high spatial resolution imagery of SPOT 6 NAOMI and experimental hyperspectral images of the ULM Headwall taken in the framework of the Leman–Baikal project.

Keywords:

remote sensing satellite imagery Landsat water turbidity mapping Selenga River Delta 

Notes

ACKNOWLEDGMENTS

The authors express their gratitude to Irina Alekseevna Labutina and Sergey Romanovich Chalov for consultations and to Mikhail Viktorovich Zimin, Yosef Akhtman, and Kévin Barbieux for the provided data.

This work was carried out with financial support of the Russian Foundation of Basis Research, (grants no. 13-05-12061 ofi_m and no. 15-05-05515), the Selenga–Baikal expedition of the Russian Geography Society, and the Leman–Baikal project.

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© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Department of Geography, Moscow State UniversityMoscowRussia

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