Monitoring Color for Small Water Bodyies by Fusing Sentinel-2 Multispectral Data and Gaofen -1 Panchromatic Imagery

Water color is an intuitive parameter for indicating the condition of natural waters. It can be captured by satellite images on a large scale and over a long period of time. However, it can be challenging to capture this information for small bodies of water due to the trade-off between spatial and spectral resolution of imaging sensors. We propose a deep learning-based method, called Spectral Angle (SA) Consistent Unsupervised Generative Adversarial Network (SA-UGAN), to fuse Sentinel-2 multispectral data and China's Gaofen-1 panchromatic images. The loss function is optimized to emphasize spectral preservation through an SA-based loss. SA-UGAN outperforms three traditional approaches in terms of quality assessment. The hue angle, which is used to describe water color, was monitored based on the fused image. The results showed that the spatial distribution of hue angle obtained from the SA-UGAN fused images was consistent with the original results.