The training of super-resolution (SR) methods for remote sensing applications typically relies on low/high-resolution image pairs derived from a single device by down-sampling the original image. However, this approach does not reflect real-world deployment scenarios, where SR models are applied to images captured from different devices, leading to an unrealistic assessment of the performance of state-of-the-art SR remote sensing methods. To contribute to advancing the knowledge in this field, we introduce a cross-device dataset and evaluation protocol for quantifying the impact of using a single device for training in cross-device inference scenarios. Additionally, we propose a training-free post-processing strategy for domain shift compensation. Our findings show that models trained on a single device suffer a moderate performance decay when applied to unseen devices ( \(\approx \) 7.3% in PSNR). This decay is more attenuated ( \(\approx \) 5.4% in PSNR) when applying our proposed correction strategy. Also, we observed that training with both devices ensures an improvement of 4.1% in PSNR in cross-device scenarios over the alternative of using a single device and post-correction during inference. These insights highlight the importance of considering domain shift in remote sensing SR applications.