Deep learning-based pansharpening methods typically use simulated data at the reduced-resolution scale for training. It limits their performance when generalizing the trained model to the full-resolution scale due to incomprehensive information utilization of panchromatic (PAN) images at the full-resolution scale and low generalization ability. In this paper, we adopt two targeted strategies to address the above two problems. On the one hand, we introduce a cross-scale comprehensive information capture module, which improves the information utilization of the original PAN image through fully-supervised reconstruction. On the other hand, we pioneer a domain adaptation strategy to tackle the problem of low generalization across different scales. Considering the instinct domain gap between different scales, we leverage the maximum mean discrepancy loss and the inherent pixel-level correlations between features at different scales to reduce the scale variance, thus boosting the generalization ability of our model. Experiments on various satellites demonstrate the superiority of our method over the state-of-the-arts in terms of information retention. Our code is publicly available at https://github.com/Meiqi-Gong/SDIPS.