Retrieval land surface temperature from visible infrared imager radiometer suite data

Abstract: The land surface temperature is a basic parameter which had been widely used in agriculture drought monitoring, crop monitoring and the yield forecasting model. In this study, we developed a split-window algorithm to retrieve land surface temperature from VIIRS (visible infrared imager radiometer suite) data which can overcome the lack of the water channel. The key parameter of water vapor content required by algorithm is obtained from MODIS data which is board on an Aqua satellite, and the emissivity computed from international geosphere biosphere program (IGBP) global vegetation classification. The character of the VIIRS data was briefly introduced and the method of estimation of the transmittance of atmosphere and emissivity of the VIIRS M15 and M16 channels also been discussed. Then, a VIIRS data imaging on June 4, 2013 has been chosen to verify the accuracy of the algorithm. By comparison with the global surface summary of day (GSOD) data, the result indicates that the method we proposed can retrieve the land surface temperature in china very well. Comparison with the MODIS LST data in the grain producing area with a surface temperature greater than 45℃ shows that the precision of the algorithm is high and the retrieval error is less than 1 K. Finally, the accuracy assessment of the method was performed with the moderate resolution transmission 4 (MODTRAN4) program. The water vapor content is set to 1.0 g/cm2, 2.2 g/cm2, 3.4 g/cm2, the surface temperature is 295 K, 310 K, 325 K, and the land type of dry soil and green vegetation with the emissivity of 0.963, 0.984 (VIIRS M15) and 0.974, 0.992 (VIIRS M16), respectively. The simulation analysis indicates the accuracy of this algorithm is greater than 1 K with the root mean square (RMS) errors 0.516 K and the average error 0.447 K. The much higher accuracy of the method confirms the application of the algorithm in agricultural information from VIIRS data is available.