In the paper, an attention was paid to the study of the possibility of using linear temporal interpolation of time series data of the surface temperature of Lake Baikal retrieved from AVHRR data to recover the gaps caused by cloudiness. This approach was verified using a set of coincident water surface temperature estimates acquired by AVHRR-based regional retrieval algorithms and evaluations through time series interpolation. During the research, maps of the spatial distribution of absolute values of the differences between coincident interpolated temperature estimates and temperature retrievals obtained with regional algorithms were compiled. Also values of the mean absolute error of interpolation of the surface temperature of Lake Baikal were assessed. It was shown, that if the shift between the times of day of the interpolation result and of data used for interpolation was greater, then the error of interpolation was larger too. So, if the shift was not more than 1.3 hour, then the mean absolute error was less than 1 °C. Apparently, this was due to the diurnal variability of the surface temperature of Lake Baikal. According to AVHRR data, the diurnal range of the mean lake water surface temperature reached almost 3 °C in June and 4 °C in July.

Sutyrina Ekaterina Nikolaevna, Candidate of Sciences (Geography), Associate Professor, Department of Hydrology and Nature Management, Irkutsk State University, 1, K. Marx st., Irkutsk, 664003, Russian Federation, tel.: (3952) 52-10-72, е-mail: ensut78@gmail.com 

Timofeeva Sofia Sergeevna, Student, Faculty of Geography, Irkutsk State University, 1, K. Marx st., Irkutsk, 664003, Russian Federation, tel.: (3952) 52-10-72, e-mail: t.sofiya@bk.ru

Sutyrina E N., Timofeeva S.S. The Application of the Interpolation of Satellite Data to Recover Lake Baikal Water Surface Temperature Values. The Bulletin of Irkutsk State University. Series Earth Sciences, 2018, vol. 26, pp. 114-124. https://doi.org/10.26516/2073-3402.2018.26.114 (in Russian)

Kondrat'ev K.Ya. (ed.). Kompleksnyi distantsionnyi monitoring ozer [Integrated remote monitoring of lakes]. Leningrad, Nauka, 1987, 288 p. (in Russian)

Mogilev N.Yu., Gnatovskii R.Yu. Issledovanie rezhima temperatury poverkhnosti ozera Baikal s ispol'zovaniem regulyarnoi sputnikovoi informatsii [Investigation of the temperature regime of the surface of Lake Baikal using regular satellite information]. Geografiya i prirodnye resursy [Geography and natural resources], 2002, no. 2, pp. 136-142. (in Russian)

Sutyrina E.N. Izuchenie vnutrennikh vodoemov i vodosborov s primeneniem dannykh distantsionnogo zondirovaniya Zemli [A study of inland water bodies and catchments using remote sensing data of the Earth]. Irkutsk, IGU Publ., 2014, 133 p. (in Russian)

Khromov S.P., Petrosyants M.A. Meteorologiya i klimatologiya [Meteorology and Climatology]. Moscow, MSU Publ., 2012. 584 p. (in Russian)

Park Kyung-Ae, Lee Eun-Young, Chung Sung-Rae, Sohn Eun-Ha. Accuracy Assessment of Sea Surface Temperature from NOAA/AVHRR Data in the Seas around Korea and Error Characteristics. Korean Journal of Remote Sensing, 2011, vol. 27, no. 6, pp. 663-675.

Barton I.J. Satellite-derived sea surface temperatures: Current status. J. Geoph. Res., 1995, vol. 100, pp. 8777-8790.

Narayanan Madhavan, Vasan D. Thirumalai, Bharadwaj A. K., Thanabalan P., Dhileeban N. Comparison and validation of ˮsea surface temperature (SST)ˮ using MODIS and AVHRR sensor data. International Journal of Remote Sensing & Geoscience, 2013, vol. 2, is. 3, pp. 1-7.

Williams Gabriela N., Zaidman Paula C., Glembocki Nora G., Narvarte Maite A., González Raúl A. C., Esteves José L., Gagliardini Domingo A. Comparison between remotely-sensed sea-surface temperature (AVHRR) and in situ records in San Matías Gulf (Patagonia, Argentina). Lat. Am. J. Aquat. Res. 2014, vol. 42, no. 1. pp. 192-203. https://doi.org/103856/vol42-issue1-fulltext-16

Sherstyankin P. P., Alekseev S. P., Abramov A. M., Stavrov K. G., De Batist M., Hus R., Canals M., Casamor J. L. Computer-Based Bathymetric Map of Lake Baikal. Doklady Earth Sciences, 2006, vol. 408, no. 4. pp. 564-569. 

Troitskaya E., Blinov V., Ivanov V., Zhdanov A., Gnatovsky R., Sutyrina E., Shimaraev M. Cyclonic circulation and upwelling in Lake Baikal. Aquatic Sciences, 2015, vol. 77, is. 2, pp. 171-182.  https://doi.org/10.1007/s00027-014-0361-8

Ding Haiyong, Elmore Andrew J. Spatio-temporal patterns in water surface temperature from Landsat time series data in the Chesapeake Bay, U.S.A. Remote Sensing of Environment, 2015, vol. 168, pp. 335-348. https://doi.org/10.1016/j.rse.2015.07.009

Ding Chao, Liu Xiangnan, Huang Fang. Temporal Interpolation of Satellite-Derived Leaf Area Index Time Series by Introducing Spatial-Temporal Constraints for Heterogeneous Grasslands. Remote Sens., 2017, vol. 9(9), no. 968, pp. 1-12. https://doi.org/10.3390/rs9090968

Mariethoz G., Linde N., Jougnot D., Rezaee H. Feature-preserving interpolation and filtering of environmental time series. Environmental Modelling and Software, 2015, vol. 72, pp. 71-76. https://doi.org/10.1016/j.envsoft.2015.07.001

Gautam R.K., Omjai S.V. Sea Surface Temperature and Net Heat Flux Variation in The Gulf of Thailand Using Buoy, Meteorological and Remote Sensing Data. Coastal Engineering Journal, 2000, vol. 42, no. 4, pp. 341-356.

Lake Baikal. Available at: https://whc.unesco.org/en/list/754 (date of access: 02.08.2017).

Malm J., Jonsson L. A study of the thermal bar in Lake Ladoga using water sur-face temperature data from satellite images. Remote sensing of Environment, 1993, vol. 44, pp. 35-46.

Thomas A. C., Emery W. J. Relationship between near-surface plankton concentration, hydrography, and satellite-measured sea surface temperature. J. Geophy. Res., 1988, no. 93 (C12), pp. 15733-15748.

Colditz René R., Conrad Christopher, Wehrmann Thilo, Schmidt Michael, Dech Stefan. TiSeG: A Flexible Software Tool for Time-Series Generation of MODIS Data Utilizing the Quality Assessment Science Data Set. IEEE transactions on geoscience and remote sensing, 2008, vol. 46, no. 10, pp. 3296-3308. 

Willmott C. J., Matsuura K. Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance. Climate Research, 2005, vol. 30, pp. 79-82. https://doi.org/10.3354/cr030079

Wyatt D., Tooley M. Aircraft Communications and Navigation Systems: Principles, Maintenance and Operation. Elsevier, 2007. 329 p.