Filling Then Spatio-Temporal Fusion for All-Sky MODIS Land Surface Temperature Generation

The thermal infrared band of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra/Aqua satellite can provide daily, 1 km land surface temperature (LST) observations. However, due to the influence of cloud contamination, spatial gaps are common in the LST product, restricting its application greatly at the regional scale. In this article, to deal with the challenge of large gaps (especially complete data loss) in MODIS LST for local monitoring, a filling then spatio-temporal fusion (FSTF) method is proposed, which utilizes another type of product with all-sky coverage, but coarser spatial resolution (i.e., the 7 km China Land Data Assimilation System (CLDAS) LST product). Due to the great temporal heterogeneity of LST, temporally closer auxiliary MODIS LST images are considered to be preferable choices for spatio-temporal fusion of CLDAS and MODIS LST time-series. However, such data are always abandoned inappropriately in conventional spatio-temporal fusion if they contain gaps. Accordingly, pregap filling is performed in FSTF to make fuller use of the valid information in temporally close MODIS LST images with small gaps. Through evaluation in both the spatial and temporal domains for three regions in China, FSTF was found to be more accurate in reconstructing MODIS LST images than the original spatio-temporal fusion methods. FSTF, thus, has great potential for updating the current MODIS LST product at the global scale.

Automated summary

This article proposes a filling then spatio-temporal fusion (FSTF) method to address the challenge of large gaps in MODIS LST data. By utilizing the CLDAS LST product, the FSTF method can more accurately reconstruct the MODIS LST images. The results of the study demonstrate the potential of FSTF for updating the current MODIS LST product globally.