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

Document Type: Research Paper

Authors

Irrigation and Drainage Engineering Department, College of Abureyhan ,University of Tehran, Tehran, Iran

Abstract

Land surface temperature (LST) is a significant parameter for many applications. Many studies have proposed
various algorithms, such as the split-window method, for retrieving surface temperatures from two spectrally
adjacent thermal infrared bands of satellite data. Each algorithm is developed for a limited study area and
application. 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 AVHRR were compared. This study was carried
out in a wheat farm site located in the Pakdasht Agricultural Region. Measurements of LST over the farm were made
with a manual infrared radiometer at the time of NOAA overpass for 18 days of May to June 2004. These days were
cloud free over the study area. A total of 18 NOAA images were acquired for the days that LST measurements were
made. The temperatures derived by the different split-window algorithms were compared to ground truth
measurements. The performance of the split window algorithms was checked with three statistical indices: root mean
square error (RMSE), mean bias error (MBE) and coefficient of determination (R2). The results showed that the
Ulivieri split-window algorithm produced the lowest value of RMSE and MBE (2.71 and 0.26 K, respectively) and
its highest value of R2 (0.92) gave more accurate results than the other algorithms.

Keywords