Filling gaps in Landsat ETM+ SLC-off images with Sentinel-2 MSI images

Abstract

On May 31, 2003, the scan-line corrector (SLC) of Landsat 7 ETM+ failed permanently. The resulting ETM+ SLC-off images contain 22% un-scanned gap pixels, thus, severely limiting their utility. In this paper, we propose a new scheme to fill gaps in SLC-off images by identifying a new source of auxiliary or known image. Specifically, Sentinel-2 MSI images are proposed as known images for gap filling, rather than the common strategy of using Landsat series data. The Sentinel-2 MSI data have the same map projection and similar band wavelengths as the Landsat 7 ETM+ data, and the Sentinel-2A and −2B MSI data together provide global coverage with a 5-day revisit period. The shorter revisit period of Sentinel-2 provides greater opportunities to acquire effective known images that are temporally close to the ETM+ SLC-off images for reliable gap filling. To render the Sentinel-2 MSI images suitable for gap filling, it is necessary to deal with their incompatible spatial resolutions relative to Landsat 7. Here, we used a downscaling-then-upscaling methodology involving Area-to-Point Regression Kriging (ATPRK) for the downscaling step. For the gap filling interpolation task we used a recently developed spatial-spectral radial basis function (SSRBF) method, which makes use of the available spectral as well as spatial information. Experiments were undertaken in which the SSRBF method was applied for gap filling using a range of different known images including Sentinel-2 MSI known images and Landsat 8 known images temporally close to and distant from the predicted image. The experimental results demonstrate that the Sentinel-2 MSI known images produced by downscaling-then-upscaling are a valuable source of information for gap filling, especially when the existing Landsat series images (e.g., the OLI images) are temporally distant to the SLC-off images.

Keywords

• Landsat ETM+
• SLC-off
• Sentinel-2
• Gap filling