Fusing Sentinel-2 and Landsat-8 Surface Reflectance Data via Pixel-Wise Local Normalization

Abstract

Medium spatial resolution surface reflectance image series from the combination of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Imager (MSI) observations have great importance to the land surface monitoring tasks, for which great efforts have been paid for blending the two data. However, most of the efforts focus on the image series with spatial resolution of 30 m, which can not meet the data demand of some applications. Therefore, it is necessary to fuse Landsat-8 and Sentinel-2 images to provide 10 m image series. Currently, there are three means to achieve that, including the area-to-point regression kriging fusion approach (ATPRK), spatiotemporal fusion methods and deep learning based fusion models. However, the ATPRK and spatiotemporal fusion methods suffer from the limited fusion performance, while the deep learning based fusion models are hardware dependent, i.e., requiring the graphics processing units (GPUs), which may not be satisfied sometimes. To address these issues, in this paper, we develop a new pixel-wise local normalization-based fusion method (LN-FM) for fusing Sentinel-2 and Landsat-8 images. The newly proposed LN-FM is compared to ATPRK and three representative spatiotemporal fusion methods in experiments, which use imagery collected from both a rural area and an urban area. The experimental results demonstrate that the newly developed LN-FM exhibits excellent qualitative and quantitative performance, as well as remarkable spatial, spectral and pixel distribution fidelity. Furthermore, this approach is fast, which may improve its applicability.