Comparison of split window algorithms for land surface temperature retrieval from NOAA-AVHRR data

Land surface temperature (LST) retrieval from NOAA-AVHRR data is mainly through so-called split window algorithms. During the last 20 years 17 split window algorithms has been published. These algorithms can be grouped into four categories: emissivity-dependent models, two-factors models, complicated models and radiance model. In this paper we intend to compare these split window algorithms in terms of their computation and accuracy. Two methods are used for the comparison: ground datasets and simulation datasets. Results from comparison shows that different algorithms have different performances under different situations. For simulation datasets, the algorithms of Qin et al. and Sobrino et al. are the best. The average root mean square (RMS) error of the two algorithms is less than 0.3degC. The algorithms of Franca and Cracknell, Prata and Uliverir et al. also have very low RMS errors (0.5-0.7degC). Results from comparison with ground datasets indicates that the algorithms of Qin et al. and Sobrino et al. are among the best for the dataset without precise in situ atmospheric water vapor contents. These algorithms are able to provide LST retrieval with average RMS error less than 1.9degC for the 361 measurements of the two Australian sites. An obvious contrast to the generally higher RMS error for the dataset is the much lower RMS error of the algorithms for the intensive experiments with precise in situ atmospheric water vapor contents. Based on the above two methods for comparison, it can be concluded that, comprehensively, the algorithm of Qin et al. is the best alternative for LST retrieval from AVHRR followed by Sobrino et al., Franca and Cracknell, and Prata when data are available to estimate both emissivity and transmittance