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.