  Land surface temperature (LST) is a key parameter when monitoring land
surface processes. However, cloud contamination and the tradeoff between the
spatial and temporal resolutions greatly impede the access to high-quality
thermal infrared (TIR) remote sensing data. Despite the massive efforts made to
solve these dilemmas, it is still difficult to generate LST estimates with
concurrent spatial completeness and a high spatio-temporal resolution. Land
surface models (LSMs) can be used to simulate gapless LST with a high temporal
resolution, but this usually comes with a low spatial resolution. In this
paper, we present an integrated temperature fusion framework for
satellite-observed and LSM-simulated LST data to map gapless LST at a 60-m
spatial resolution and half-hourly temporal resolution. The global linear model
(GloLM) model and the diurnal land surface temperature cycle (DTC) model are
respectively performed as preprocessing steps for sensor and temporal
normalization between the different LST data. The Landsat LST, Moderate
Resolution Imaging Spectroradiometer (MODIS) LST, and Community Land Model
Version 5.0 (CLM 5.0)-simulated LST are then fused using a filter-based
spatio-temporal integrated fusion model. Evaluations were implemented in an
urban-dominated region (the city of Wuhan in China) and a natural-dominated
region (the Heihe River Basin in China), in terms of accuracy, spatial
variability, and diurnal temporal dynamics. Results indicate that the fused LST
is highly consistent with actual Landsat LST data (in situ LST measurements),
in terms of a Pearson correlation coefficient of 0.94 (0.97-0.99), a mean
absolute error of 0.71-0.98 K (0.82-3.17 K), and a root-mean-square error of
0.97-1.26 K (1.09-3.97 K).
