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

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.