Multi-sensor spatiotemporal satellite images have become crucial for monitoring the geophysical characteristics of the Earth’s environment. However, clouds often obstruct the view from the optical sensors mounted on satellites and therefore degrade the quality of spectral, spatial, and temporal information. Though cloud imputation with the rise of deep learning research has provided novel ways to reconstruct the cloud-contaminated regions, many learning-based methods still lack the capability of harmonizing the differences between similar spectral bands across multiple sensors. To cope with the inter-sensor inconsistency of overlapping bands in different optical sensors, we propose a novel harmonization-guided residual network to impute the areas under clouds. We present a knowledge-guided harmonization model that maps the reflectance response from one satellite collection to another based on the spectral distribution of the cloud-free pixels. The harmonized cloud-free image is subsequently exploited in the intermediate layers as an additional input, paired with a custom loss function that considers image reconstruction quality and inter-sensor consistency jointly during training. To demonstrate the performance of our model, we conducted extensive experiments on a multi-sensor remote sensing imagery benchmark dataset consisting of widely used Landsat-8 and Sentinel-2 images. Compared to the state-of-the-art methods, results show at least a 22.35% improvement in MSE.