A Data Fusion Method for Generating Hourly Seamless Land Surface Temperature from Himawari-8 AHI Data

High temporal resolution and spatially complete (seamless) land surface temperature (LST) play a crucial role in numerous geoscientific aspects. This paper proposes a data fusion method for producing hourly seamless LST from Himawari-8 Advanced Himawari Imager (AHI) data. First, the high-quality hourly clear-sky LST was retrieved from AHI data by an improved temperature and emissivity separation algorithm; then, the hourly spatially complete China Land Data Assimilation System (CLDAS) LST was calibrated by a bias correction method. Finally, the strengths of the retrieved AHI LST and bias-corrected CLDAS LST were combined by the multiresolution Kalman filter (MKF) algorithm to generate hourly seamless LST at different spatial scales. Validation results showed the bias and root mean square error (RMSE) of the fused LST at a finer scale (0.02 degrees) were -0.65 K and 3.38 K under cloudy sky conditions, the values were -0.55 K and 3.03 K for all sky conditions, respectively. The bias and RMSE of the fused LST at the coarse scale (0.06 degrees) are -0.46 K and 3.11 K, respectively. This accuracy is comparable to the accuracy of all-weather LST derived by various methods reported in the published literature. In addition, we obtained the consistent LST images across different scales. The seamless finer LST data over East Asia can not only reflect the spatial distribution characteristics of LST during different seasons, but also exactly present the diurnal variation of the LST. With the proposed method, we have produced a 0.02 degrees seamless LST dataset from 2016 through 2021 that is freely available at the National Tibetan Plateau Data Center. It is the first time that we can obtain the hourly seamless LST data from AHI.

Automated summary

This paper proposes a data fusion method for generating hourly seamless land surface temperature (LST) from Himawari-8 Advanced Himawari Imager (AHI) data. The method involves retrieving high-quality LST from AHI data, calibrating it with the China Land Data Assimilation System (CLDAS) LST, and combining the two using the multiresolution Kalman filter (MKF) algorithm. The validation results show that the fused LST is comparable in accuracy to other methods and provides consistent LST images at different spatial scales.