A gapless 100 m resolution daily mean land surface temperature dataset over the Tibetan Plateau in 2019

The Tibetan Plateau (TP), known as the “Third Pole”, is highly sensitive to climate change. Fine-scale daily mean land surface temperature (DMLST) data are needed to study regional climate processes, permafrost dynamics, glacier melt, and land surface energy balance. However, existing DMLST products over the TP generally have spatial resolutions of 1 km to 0.05°, which are too coarse to resolve local processes in glacier forefields, permafrost slopes, and other heterogeneous landscapes. Here, we developed a partition-based spatiotemporal fusion framework that integrates ERA5-Land reanalysis data with MODIS and Landsat LST products to generate a gapless 100 m-resolution DMLST dataset over the TP for 2019. Validation against ground-based observations shows that the dataset achieves a root mean square error (RMSE) of 3.13 K, a mean absolute error (MAE) of 2.42 K, a mean bias error (BIAS) of −0.81 K, and a coefficient of determination (R²) of 0.93 at the daily scale. Compared with an existing 1 km product, the proposed dataset improves error metrics by approximately 0.50–0.60 K. The 100 m resolution improves the representation of slope aspect, microtopography, land-cover contrasts, local temperature gradients, and transition zones in glacierized and other typical regions. This dataset provides a high-resolution and gapless DMLST resource for quantitative studies of land surface energy balance, permafrost degradation, glacier melt, and related environmental processes over the TP.