Remote Sensing data are used across various fields, and their selection must consider the study's objectives, sensor capabilities, and different resolutions. In urban climate investigations, such as the Urban Heat Island (UHI), thermal sensors estimate Land Surface Temperature (LST), but freely available products have low spatial resolution. This study proposes a methodology to resample MODIS LST from 1 km to 10 m (MODIS_LST_1km and MODIS_LST_10m, respectively), between 2018 and 2023, in different seasons, along the border between Portugal and Spain. We used Google Earth Engine to calculate MODIS_LST_1km and the Normalized Difference Vegetation Index (NDVI) from Sentinel-2. We resampled the NDVI to 1 km and calculated a regression equation for each data when images from both products were available. We applied the resulting equation using NDVI with a 10 m (original resolution) to obtain MODIS_LST_10m. We validated the results with Landsat-8 LST (Landsat_LST) data through Spearman correlation in R software. Additionally, we analyzed the correlation between NDVI and the Enhanced Vegetation Index (EVI) from Landsat-8 with MODIS_LST_10m to assess whether higher temperatures corresponded to areas with low vegetation. The model showed good explainability, especially in summer, and validation with Landsat-8 was also more significant in summer (ρ between 0.736 and 0.895). The correlations between MODIS_LST_10m with EVI and NDVI were negative on most dates, indicating higher temperatures in less vegetated areas. For future studies, we plan to test other sensors/satellites, such as Sentinel-3, to reinforce the robustness of this methodology.