An Annual Temperature Cycle Feature Constrained Method for Generating MODIS Daytime All-Weather Land Surface Temperature

In the face of rapid global climate change and the increasing occurrence of extreme weather events, acquiring seamless land surface temperature (LST) with high spatial and temporal resolution on a global scale has become increasingly crucial. However, the limited ability of thermal infrared (TIR) remote sensing to penetrate cloud cover has hindered the widespread application of TIR LST datasets. To address this limitation, we propose a novel reconstruction approach for cloud-covered pixels, which is established based on the annual surface temperature cycle. It shifted the previous reconstruction from directly modeling LST to indirectly modeling the residual term derived from the LST observations and the annual temperature cycle (ATC) model fit values. A random forest regression (RFR) was used to build this estimation model and the model was applied to cloud-covered pixels to derive their LSTs. Taking the Iberian Peninsula as the study area, the proposed method was applied to generate the all-weather LST product for the whole year 2021. The visual assessment demonstrates its robust performance across different seasons and weather conditions. Additionally, through the validation with the masked clear-sky LST observations, it reveals that the proposed method achieves a stable estimation accuracy, with the average value of the coefficient of determination (
${R} ^{2}$
) and root mean squared error (RMSE) of above 0.8 and 1.08 K under different climatic conditions. In comparison, the validation with the ERA-5 land reanalysis data also indicates a relatively good consistency between the performance of the reconstructed LST and the clear-sky LST, although with a slight decline in
${R} ^{2}$
and RMSE. Additionally, the indirect validation with near-surface air temperature (NSAT) also shows the comparable ability of the reconstructed LST in NSAT estimation as the clear-sky LST, with an increase of RMSE no more than 0.95 K. In general, the proposed method shows good potential in reconstructing cloud-covered LSTs with relatively stable performance under different cloud-cover conditions and it can be applied for generating all-weather LST products.