Global 1-km monthly mean land surface temperature product (2003-2020)

The monthly mean land surface temperature (MMLST) reflects stable intra- and inter-annual temperature variations, and has a wide range of applications in climatological and meteorological studies. We used a combination method (including a weighted average model derived from 253 flux sites and considering the influence of the count of valid observations) and MODIS instantaneous LST products (MOD11A1 and MYD11A1) to generate a global 1-km MMLST dataset for the years 2003–2020. The validation with SURFRAD stations showed a root mean square error of 1.5 K and a Bias of 0.5 K. Compared with existing MMLST product, the newly generated product exhibited a high consistency in reflecting temporal variations of global temperature, and had a better ability to retrieve spatial details of temperature variations.