Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuracy. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Planck's radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.1 ¿C. Two methods have been used to validate the proposed algorithm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.25 ¿C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.75 ¿C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.24 ¿C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the applicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data. ¿ 2001 American Geophysical Union