Many remote sensing applications require high spatial resolution images, but the elevated cost of these images makes some studies unfeasible. Single-image super-resolution algorithms can improve the spatial resolution of a low-resolution image by recovering feature details learned from pairs of low-high resolution images. In this work, several configurations of ESRGAN, a state-of-the-art algorithm for image super-resolution are tested. We make a comparison between several scenarios, with different modes of upsampling and channels involved.  The best results are obtained training a model with RGB-IR channels and using progressive upsampling.