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There is an increasing demand for a land surface temperature (LST) dataset with both fine spatial and temporal resolutions due to the key role of LST in the Earth’s land–atmosphere system. Currently, the technique most commonly used to meet the demand is thermal infrared (TIR) remote sensing. However, cloud contamination interferes with TIR transmission through the atmosphere, limiting the potential of space-borne TIR sensors to provide the LST with complete spatio-temporal coverage. To solve this problem, we developed a two-step integrated method to: (i) estimate the 10-km LST with a high spatial coverage from passive microwave (PMW) data using the multilayer perceptron (MLP) model; and (ii) downscale the LST to 1 km and fill the gaps based on the geographically and temporally weighted regression (GTWR) model. Finally, the 1-km all-weather LST for cloudy pixels was fused with Aqua MODIS clear-sky LST via bias correction. This method was applied to produce the all-weather LST products for both daytime and nighttime during the years 2013–2018 in South China. The evaluations showed that the accuracy of the reproduced LST on cloudy days was comparable to that of the MODIS LST in terms of mean absolute error (2.29–2.65 K), root mean square error (2.92–3.25 K), and coefficients of determination (0.82–0.92) against the in situ measurements at four flux stations and ten automatic meteorological stations with various land cover types. The spatial and temporal analysis showed that the MLP-GTWR LST were highly consistent with the MODIS, in situ, and ERA5-Land LST, with the satisfactory ability to present the LST pattern under cloudy conditions. In addition, the MLP-GTWR method outperformed a gap-filling method and another TIR-PMW integrated method due to the local strategy in MLP and the consideration of temporal non-stationarity relationship in GTWR. Therefore, the test of the developed method in the frequently cloudy South China indicates the efficient potential for further application to other humid regions to generate the LST under cloudy condition.

A Two-Step Integrated MLP-GTWR Method to Estimate 1 km Land Surface Temperature with Complete Spatial Coverage in Humid, Cloudy Regions