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


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2019, Vol. 16, No. 4, pp. 203-212

On the influence of cyanobacteria, surface roughness, and bottom radiance on the remote sensing reflectance of the Gorky Reservoir

A.A. Molkov 1 , E.N. Korchemkina 2 , G.V. Leshchev 1 , O.A. Danilicheva 1 , I.A. Kapustin 1 
1 Institute of Applied Physics RAS, Nizhny Novgorod, Russia
2 Marine Hydrophysical Institute RAS, Sevastopol, Russia
Accepted: 25.04.2019
DOI: 10.21046/2070-7401-2019-16-4-203-212
This paper presents the results of the remote sensing reflectance (Rrs) measurements in the Gorky Reservoir during 2018 summer season in relation to the task of satellite monitoring of inland waters. Ship measurements were carried out at 26 stations located in the lake part of the reservoir from its southern side on an area of about 1502. Secchi depth, vertical profiles of chlorophyll a (Chl-a) and colored dissolved organic matter, integral underwater irradiance, as well as some apparent optical properties, necessary to retrieve of Rrs, were measured at these stations. On the basis of the obtained data, the analysis of Rrs variability associated with seasonal changes of the water optical properties and the observation conditions has been carried out. It was established that hydro-optical regime of the reservoir was conventionally divided into two stages: spring that characterized by a high content of mineral suspension in water, and summer with a low content of mineral suspension and high spatial-temporal variations of Chl-a concentration. For the period of the most intense of blue-green algal bloom, the obtained Rrs spectra were classified by two types of Chl-a vertical profiles corresponding to different wind-wave conditions. Also, preliminary estimates of the Chl-a decrease in the euphotic layer with increase of fetch were obtained. At the same time, it was shown that the influence of the bottom radiance on the Rrs spectra can be neglected for areas where depths exceeding half of the Secchi depth. These results are aimed at improving the algorithms for retrieval of the water optical properties by satellite images of the Gorky Reservoir.
Keywords: remote sensing reflectance, Secchi depth, euphotic zone depth, chlorophyll a vertical profiles, Gorky Reservoir
Full text


  1. Korchemkina E. N., Molkov A. A., Regional’nyi bioopticheskii algoritm dlya Gor’kovskogo vodokhrani­lishcha: pervye rezul’taty (Regional bio-optical algorithm for Gorky reservoir: first results), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 3, pp. 184–192.
  2. Molkov A. A., Kapustin I. A., Shchegolkov Yu. B., Vodeneeva E. L., Kalashnikov I. N., On correlation between inherent optical properties at 650 nm, Secchi depth and blue-green algal abundance for the Gorky reservoir, Fundamentalnaya i Prikladnaya Gidrofizika, 2018, Vol. 11, No. 3, pp. 26–33.
  3. Okhapkin A. G., Mikul’chik I. A., Korneva L. G., Mineeva N. M., Fitoplankton Gor’kovskogo vodokhranilishcha (Phytoplankton of the Gorky Reservoir), Tolyatti: IBIW, 1997, 224 p.
  4. Ansper A., Alikas K., Retrieval of Chlorophyll a from Sentinel-2 MSI Data for the European Union Water Framework Directive Reporting Purpose, Remote Sensing, 2019, Vol. 1, Issue 1, pp. 64–90.
  5. Beutler M., Wiltshire K. H., Meyer B., Moldaenke C., Lüring C., Meyerhöfer M., Hansen U.-P., Dau H., A fluorometric method for the differentiation of algal populations in vivo and in situ, Photosynthesis Research, 2002, Vol. 72, Issue 1, pp. 39–53.
  6. Chorus I., Falconer I. R., Salas H. J., Bartram J., Health risks caused by freshwater cyanobacteria in recreational waters, J. Environmental Toxicology and Pharmacology Health. Part B: Critical Reviews, 2000, Vol. 3, Issue 4, pp. 323–347.
  7. Gitelson A. A., Gurlin D., Moses W. J., Yacobi Y. Z., Remote Estimation of Chlorophyll-a Concentration in Inland, Estuarine, and Coastal Waters, In: Advances in Environmental Remote Sensing: Sensors, Algorithms, and Applications, CRC Press, 2011, Chapter 18, pp. 449–478.
  8. Molkov A. A., Fedorov S. V., Pelevin V. V., Korchemkina E. N., Regional Models for High-resolution Retrieval of Chlorophyll a and TSS Concentrations in Gorky Reservoir by Sentinel-2 Images, Remote Sensing, 2019, Vol. 11, Issue 10, pp. 1215–1241.
  9. Morel A., Prieur L., Analysis of Variations in Ocean Color, Limnology and Oceanography, 1977, V. 22, pp. 709–722.
  10. Spyrakos E., O’Donnell R., Hunter P., Miller C., Scott M., Simis S., Neil C., Barbosa C., Binding C., Bradt S., Bresciani M., Dall’Olmo G., Giardino C., Gitelson A., Kutser T., Li L., Matsushita B., Martinez-Vicente V., Matthews M., Ogashawara I., Ruiz-Verdú A., Schalles J., Tebbs E., Zhang Yu., Tyler A., Optical types of inland and coastal waters, Limnology and Oceanography, 2018, Vol. 63, No. 2, pp. 846–870.
  11. Xue K., Zhang Yu., Duan H., Ma R., Loiselle S., Zhang M., A Remote Sensing Approach to Estimate Vertical Profile Classes of Phytoplankton in a Eutrophic Lake, Remote Sensing, 2015, Vol. 7, Issue 11, pp. 14403–14427.