Comparison of Some Split-window Algorithms to Estimate Land Surface Temperature from AVHRR Data in Southeastern Tehran, Iran

Land surface temperature (LST) is a significant parameter for many applications. Many studies have proposedvarious algorithms, such as the split-window method, for retrieving surface temperatures from two spectrallyadjacent thermal infrared bands of satellite data. Each algorithm is developed for a limited study area andapplication. In this paper, as part of developing an optimal split-window method in the southeast of Tehran province,Iran, four commonly applied algorithms to retrieve the LST from AYHRR were compared. This study was carriedout in a wheat farm site located in the Pakdasht Agricultural Region. Measurements of LST over the farm were madewith a manual infrared radiometer at the time of NOAA overpass for 18 days of May to June 2004. These days werecloud free over the study area. A total of 18 NOAA images were acquired for the days that LST measurements weremade. The temperatures derived by the different split-window algorithms were compared to ground truthmeasurements. The performance of the split window algorithms was checked with three statistical indices: root meansquare error (RMSE), mean bias error (MBE) and coefficient of determination (R2) . The results showed that theUlivieri split-window algorithm produced the lowest value of RMSE and MBE (2.71 and 0.26 K, respectively) andits highest value of R2 (0,92) gave more accurate results than the other algorithms.