Comparison of split window algorithms to derive land surface temperature from satellite TIRS data
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ORIGINAL PAPER
Comparison of split window algorithms to derive land surface temperature from satellite TIRS data Sajad Zareie 1 & Kazem Rangzan 2 & Hassan Khosravi 3 & Vladimir Modestovich Sherbakov 1 Received: 29 December 2016 / Accepted: 11 July 2018 # Saudi Society for Geosciences 2018
Abstract Remote sensing data can be used as the basis for meteorological data. Due to the limitations of meteorological stations on the Earth, derivation of land surface temperature is one of the most important aspects of the remote sensing application in climatology studies. In the present study, Landsat-8 thermal infrared sensor data of the scene located over Khuzestan province with row/path of 165/38 were used to derive land surface temperature (LST). Normalized difference vegetation index (NDVI), fraction of vegetation cover, satellite brightness temperature, and land surface emissivity were calculated as the vital criteria to derive LSTs using the split window algorithms. LST determination was performed by nine different split window algorithms. Eventually, LST products were evaluated using ground-based measurements at the meteorological stations of the study area. The results showed that algorithm of Coll and Casselles had a highest accuracy with RMSE 1.97 °C, and Vidal’s method presented the lowest accuracy to derive LST with RMSE 4.11 °C. According to the results, regions with high density of vegetation and water resources have lowest diurnal temperature and regions with bare soils and low density of vegetation have a highest diurnal temperature. Results of the study indicated that LST algorithm accuracy is an important factor in the environmental and climate change studies. Keywords Land surface temperature . Split window algorithm . Accuracy . Landsat-8 . TIRS
Introduction Surface temperature is a function of pure energy at ground surface that depends on the amount of energy transferred to the land surface, land surface emissivity, humidity, and atmospheric flows. Land surface temperature (LST) index can be calculated using the thermal infrared radiation emitted by Earth’s surface. LST is an important factor in controlling Earth’s biological, chemical, and physical processes. LST index is used in a range of global climate change, urban land use/land cover, hydrological, meteorological, biogeochemical, ecological, agricultural, and climatological applications
* Hassan Khosravi [email protected] 1
Institute of Earth Sciences, Saint Petersburg State University, Saint Petersburg, Russian Federation
2
Faculty of Earth Science, ShahidChamran University, Ahwaz, Iran
3
Department of Arid and Mountainous Regions Reclamation, Faculty of Natural Resources, College of Agriculture and Natural Resources, University of Tehran, Karaj, Iran
(Liu et al. 2015; Reutter et al. 1994; Avdan and Jovanovska 2016; Yu et al. 2008; Li et al. 2013; Wan and Li 1997). Since satellite remote sensing provides a repetitive synoptic view in short intervals of the Earth’s surface, this technology is a vital tool for monitoring LST (Rozenstein e
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