Estimating the Water Turbidity in the Selenga River and Adjacent Waters of Lake Baikal Using Remote Sensing Data
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
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.
- 1.Akhtman, Y., Constantin, D., Rehak, M., Nouchi, V.M., Bouffard, D., Pasche, N., Shinkareva, G., Chalov, S., and Merminod, B., Leman-Baikal: Remote sensing of lakes using an ultralight plane, in 6th Workshop on Hyperspectral Image and Signal Processing, 2014, EPFL-CONF-199120. http://infoscience.epfl.ch/ record/199120/files/Akhtman-LB-Whispers2014.pdf.Google Scholar
- 2.Atlas “Deshifrirovanie mnogozonal’nykh aerokosmicheskikh snimkov (Atlas "Decoding of Multizone Aerospace Images. Method and Results”), Moscow: Nauka, 1982.Google Scholar
- 3.Chalov, S.R., Estimate for river water turbidity from satellite imagery, in 24-e Plenarnoe mezhvuzovskoe koordinatsionnoe soveshchanie po probleme erozionnykh ruslovykh i ust’evykh protsessov (The 24th Plenary Inter-University Coordination Meeting on the Problem of Erosion Processes of Bed and Estuary), Barnaul, 2009, pp. 218–220.Google Scholar
- 4.Chalov, S.R., Belozerova, E.V., and Gladkova, M.V., Monitoring of surface water turbidity with remote sensing methods, in Resursy i kachestvo vod sushi: otsenka i upravlenie (Resources and Quality of Land Waters: Assessment and Control), Moscow: IVP RAN, MGU, 2012, pp. 260–273.Google Scholar
- 5.Chander, G., Markham, B., and Helder, D., Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI sensors, Remote Sens. Environ., 2009, pp. 893–903.Google Scholar
- 6.Chavez, P.S., Image-based atmospheric corrections-revisited and improved, Photogramm. Eng. Remote Sens., 1996, vol. 62, no. 9, pp. 1025–1035.Google Scholar
- 7.Curran, P.J., The relationship between suspended sediment concentration and remotely sensed spectral radiance: A review, J. Coastal Res., 1988, vol. 4, no. 3, pp. 351–368.Google Scholar
- 8.Delta reki Selengi – estestvennyi biofil’tr i indikator ozera Baikal (The Selenga River Delta as a Natural Biofilter and Indicator of the State of Lake Baikal), Tulokhonov, A.M. and Plyusnin, A.M., Eds., Novosibirsk: SO RAN, 2008.Google Scholar
- 11.Güttler, F.N., Niculescu, S., and Gohin, F., Turbidity retrieval and monitoring of Danube delta waters using multi-sensor optical remote sensing data: An integrated view from the delta plain lakes to the western–northwestern Black Sea coastal zone, Remote Sens. Environ., 2013, vol. 132, pp. 86–101.CrossRefGoogle Scholar
- 14.Khazheyeva, Z.I., Urbazayeva, S.D., Tulokhonov, A.K., Plyusnin, A.M., Sorokovikova, L.M., and Sinyukovich, V.N., Heavy metals in the water and bottom sediments of the Selenga River delta, Geochem. Int., 2005, vol. 43, no. 1, pp. 93–99.Google Scholar
- 15.Kobzar’, A.I., Prikladnaya matematicheskaya statistika (Applied Mathematical Statistics), Moscow: Fizmatlit, 2006, pp. 98–134.Google Scholar
- 16.Labutina, I.A. and Saf’yanov, G.A., Studies of the distribution of solid river flows from aerospace imagery on the example of the Kodery and Selenga rivers, in Kosmicheskaya s”yemka i tematicheskoe kartografirovanie (Space Imaging and Thematic Mapping), Moscow: MGU, 1980, pp. 118–125.Google Scholar
- 19.Pavelsky, T.M. and Smith, L.C., Remote sensing of suspended sediment concentration, flow velocity and lake recharge in the Peace–Athabaska delta, Canada, Water Resour. Res., 2009, no. 45, pp. 110–126.Google Scholar
- 20.Ruddick, K., Nechad, B., Neukermans, G., Park, Y., Doxaran, D., Sirjacobs, D., and Beckers, J.M., Remote sensing of suspended particulate matter in turbid waters: State of the art and future perspectives, in Proc. XIX Conf. of the Ocean Optics, Barga, 2008, pp. 6–10.Google Scholar
- 22.Tarasova, O.B., Khromova, T.F., and Shibalkin, A.E., Osnovy matematicheskoi statistiki (Fundamentals of Mathematical Statistics), Moscow: MSHA, 2004, pp. 51–90.Google Scholar