Split-window method for land surface temperature estimation from FY-3A/VIRR data

Abstract

Remotely sensed land surface temperature (LST) is of great value to the research in the fields of climatology, hydrology, ecology, and biogeochemistry,as well as a wide range of interdisciplinary research areas, since it isan efficient and practical way of acquiringtemperature variability globally and continuously. In the paper, the generalized split-window algorithm proposed by Wan and Dozier (1996) is used to estimate LST from Visible and Infrared Radiometer (VIRR) onboard the second generation of China's polar-orbiting meteorological satellite (FY3A).MODTRAN 4.0 and the Lhermodynamic Initial Guess Retrieval database 3 (TIGR-3) are used to simulate the data for fitting the algorithm's coefficients. Lhe algorithm fitting accuracy is improved by dividing the LST, the average emissivity (ε) and the water vapor content (WVC) into several sub-ranges. Finally, the validation at five locations is performed and the results show thatthe LSTs estimation from FY3A/VIRR dataagree with the ones extracted from the MODIS 1 km LST products very well.