Generation of a time-consistent land surface temperature product from MODIS data

Land surface temperature (LST) is crucial for a wide variety of land-atmosphere studies. A long-term and time-consistent LST product is highly desirable for use in global climate studies. In this study, we developed a method to normalize the Terra-MODIS LST during daytime to a consistent local solar time to generate a time-consistent LST product. A multiple linear regression model for the slope of LST versus the local solar time during the period 10:00-12:00 as a function of the normalized-difference vegetation index, solar zenith angle, and digital elevation model was established using MSG-SEVIRI data. The regression equation was then applied to normalize the Terra-MODIS LST during daytime to a consistent local solar time (i.e., 11:00 local solar time). The accuracy of the proposed method was evaluated using MSG-SEVIRI-derived LST data. The results indicate that the root mean square error of the differences between the LST before temporal normalization and the actual LST (derived from MSG-SEVIRI data) is approximately 1.5 K, whereas those between the LST after temporal normalization and the actual LST is approximately 0.5 K.