Validation of Remotely Sensed Land Surface Temperature at Lake Baikal’s Surroundings Using In Situ Observations

Egor Dyukarev (), Nadezhda Voropay, Oksana Vasilenko and Elena Rasputina
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Egor Dyukarev: Institute of Monitoring of Climatic and Ecological System, Siberian Branch Russian Academy of Sciences, Tomsk 634055, Russia
Nadezhda Voropay: Institute of Monitoring of Climatic and Ecological System, Siberian Branch Russian Academy of Sciences, Tomsk 634055, Russia
Oksana Vasilenko: V. B. Sochava Institute of Geography, Siberian Branch Russian Academy of Sciences, Irkutsk 664033, Russia
Elena Rasputina: V. B. Sochava Institute of Geography, Siberian Branch Russian Academy of Sciences, Irkutsk 664033, Russia

Land, 2024, vol. 13, issue 4, 1-18

Abstract: The accuracy of Land Surface Temperature (LST) products retrieved from satellite data in mountainous-coastal areas is not well understood. This study presents an analysis of the spatial and temporal variability of the differences between the LST and in situ observed air and surface temperatures (ISTs) for the southeastern slope of Lake Baikal’s surroundings. The IST was measured at 12 ground observation sites located on the southeastern macro-slope of the Primorskiy Ridge (Baikal, Russia) within an elevation range of 460–1656 m above sea level from 2009 to 2021. LST was estimated using 617 Landsat (7 and 8) images from 2009–2021, taking into account brightness temperature, surface emissivity and vegetation cover fraction. The comparison of the LST from satellite data and the IST from ground observation showed relatively high differences, which varied depending on the season and site type. A neural network was suggested and calibrated to improve the LST data. The corrected remote-sensed temperature was found to reproduce the IST very well, with mean differences of about 0.03 °C and linear correlation coefficients of 0.98 and 0.95 for the air and surface IST.

Keywords: land surface temperature; validation; neural network; Lake Baikal; remote sensing (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:gam:jlands:v:13:y:2024:i:4:p:555-:d:1379893

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