Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data

Retrieval of land surface temperature (LST) from advanced very high resolut ion 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 accuacry. The algorithm involves only two essential parameters (trans mittance and emissivity). The principle and method for the linearization of Planck's radiance equation, the mathematical derivation process of the alg orithm, and the method for determining the atmospheric transmittance are di scussed with details. Sensitivity analysis of the algorithm has been perfor med for evaluation of probable LST estimation error due to the possible err ors in transmittance and emissivity. Results from the analysis indicate tha t the proposed algorithm is able to provide an accurate estimation of LST f rom AVHRR data. Assuming an error of 0.05 in atmospheric transmittance esti mate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.1 degreesC. Two methods have been used to validate the proposed algortihm. Comparison has also been done wit h the existing I I algorithms in literature. Results from validation and co mparison using the standard atmospheric simulation for various situations a nd the ground truth data sets demonstrate the applicability of the algorith m. According to the root mean square (RMS) errors of the retrieved LSTs fro m the measured or assumed LSTs, the proposed algorithm is among the best th ree. Considering the insignificant RMS error difference among the three, th e proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulat ion indicates that this algorithm can achieve the accuacry of 0.25 degreesC in LST retrieval for the case without error in both transmittance and emis sivity estimates. The accuary of this algorithm is 1.75 degreesC for the gr ound truth data set without precise in situ atmospheric water vapor content s. The accuracy increases to 0.24 degreesC for another ground truth data se t with precise in situ atmospheric water vapor contents. The much higher ac curacy for this data set confirms the applicability of the proposed algorit hm as an alternative for the accurate LST retrieval from AVHRR data.