Urban Heat Islands (UHI), characterized by elevated temperatures, present important challenges to sustainability. This study introduces a novel multimodal approach for high-resolution Land Surface Temperature (LST) estimation, a critical component in addressing UHI. The methodology initially employs RGB orthophotography for LST estimation and progressively integrates additional relevant variables correlated with LST, including elevation and land cover. Leveraging conditional Generative Adversarial Networks (cGANs), LST maps are generated, enabling informed urban planning. Experimental results highlight the potential of this multimodal approach, emphasizing that the combination of all data variables yields the most favorable outcomes. These findings advance UHI research and support data-driven urban climate management.