Accurate Land Surface Temperature (LST) estimation is crucial for understanding environmental dynamics and addressing diverse scientific and societal challenges. This study explores a novel approach for LST estimation using RGB data and integrating it with additional data modalities. By leveraging conditioned Generative Adversarial Networks (cGANs) for LST generation on adjacent tiles, certain artifacts are observed. To address this issue, we introduce a comprehensive processing pipeline and tiling strategy, evaluating fusion methods for artifact removal and improving LST generation accuracy. Our findings demonstrate the potential of integrating multi-modal data to enhance LST estimation, leading to promising advancements, particularly in LST data inference.