Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm
By O. Rozenstein, Zhihao Qin, Y. Derimian, Arnon Karnieli. Italian National Conference on Sensors, 2014
401 citations recorded by the source corpus.
Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.
Subfields: Atmospheric Science, Environmental Engineering
View Paper: https://www.semanticscholar.org/paper/d9cfc5fa5389388051e6d2d1f875e75d0eb205cd
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Citations in the corpus
- Land Surface Temperature Retrieval from Landsat 8 TIRS - Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method (2014, methodology): “The former one derived a SW algorithm and studied the parameters’ (including water vapor content and land surface emissivity) sensitivity of the algorithm [22].”
- An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data (2015, methodology): “[10] adapted the two-factor split-window algorithm of Qin et al.”
- Urban heat island effect: A systematic review of spatio-temporal factors, data, methods, and mitigation measures (2018, neutral): “…researchers have, however, shown that ASTER datasets provide more accurate estimation of thermal patterns (Al Kuwari et al., 2016) - i.e. see also (Rozenstein et al., 2014); Yu et al. (2014) 3.3 Methods used to measure spatial changes (including urban growth and land cover changes) Urban growth…”