A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

Abstract

Abstract This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between −80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively. Acknowledgments The authors would like to thank the following persons for their various help with this study: Zhengming Wan, University of California, Santa Barbara, USA, Zhangliang Li, TRIO/LSIIT (CNRS UMR 7005), Sebastien Brant, France, Vincent Chiang, a member of MODIS team, and Ruiliang Pu, Department of Environmental Science, Policy, and Management (ESPM), University of California at Berkeley, for their kindly providing guidance and material. Thanks to NASA for providing MODIS LST product. Thanks also for the good suggestions and guidance of the reviewers. This research is supported by the National Natural Science Foundation of the People's Republic of China (NSFC) 90302008 (The study of soil moisture and snow quantum of Qingzang tableland), and the National Natural Science Foundation of the People's Republic of China (NSFC) 40471096 (Methodology for LST retrieval from MODIS data and its parameter determination) and project 973 2001CB309404, People's Republic of China.