Improvement of Clear-Sky LST Monthly Products by Using Diurnal Temperature Cycle Model (DTC)

Land surface temperature (LST) monthly composite data are valuable observational resources for study climate regionally and globally. However, two major issues associated with LST observations from the thermal remote sensing could be potentially problematic for general applications. For the near-polar orbiting satellites, the data acquisition time is determined by the satellite orbits; therefore, LST is temporally sparse in a diurnal cycle (e.g., MODIS onboard Terra and Aqua offer at most four times a day observations). The current MODIS monthly products present a certain time of a day, rather than a daily mean over each month. Another major disadvantage of thermal remote sensing of land is the missing data due to clouds temporally and spatially, casting an inevitable risk of uneven sampling among different overpass monthly composites. The regions with a seasonal and diurnal cloud pattern could largely influence its temporal consistency. This issue is influential for both geostationary and near-polar LST observations. To overcome problems caused by the sparsely temporal sampled and spatially missing data in the monthly composite LST products, this study used the diurnal temperature cycle (DTC) model to reconstruct quasi-clear-sky LST in a diurnal cycle. Geostationary observations at 5km are used to test the approach. The modeled results offer the monthly composite daily mean and daily maximum and minimum LST, enriching the temporal features of LST products and improving the spatial consistency of LST. The overall assessment of the new products over multiple countries in East and West Africa suggest an overall improved result. The modeled products correct the low bias of daily maximum and high bias of daily minimum, and overall improve the representation of the seasonal cycle of LST. Then, we tested on MODIS data with a higher spatial resolution of 1km. In sum, new products offer great potential for climate studies and model evaluations.