TIF: A time-series-based image fusion algorithm

Abstract

We developed a Time-series-based Image Fusion (TIF) algorithm to generate 10-m surface reflectance time series by synthesizing Landsats 8/9 and Sentinel-2 A/B data. Unlike traditional methods that rely on image pairs or thematic maps, TIF extracts all valid pixel-level observation pairs across time to build per-pixel linear regression models. This approach captures the spectral relationships between sensors while accounting for land surface dynamics. A temporal weighting scheme and an iterative refinement strategy improves the fusion process, yielding reusable coefficients that support efficient, scalable 10-m time-series generation. TIF was applied to all Landsat multispectral bands, using native 10-m Sentinel-2 bands (Blue, Green, Red) and resampled bands (NIR and SWIR1/2) for visual assessment, with quantitative accuracy evaluated at the original Sentinel-2 resolutions. Experiments across five U.S. sites show TIF consistently outperforms state-of-the-art methods like STARFM, FSDAF 2.0, Sen2Like, and ESRCNN. For instance, TIF demonstrated a reduction in RMSE by 24 % and an increase in SSIM by 6 % compared to FSDAF 2.0 and ESRCNN, and outclassed STARFM and Sen2Like, which showed weaker results across all metrics. In multi-date change detection, TIF-predicted images achieved a mean F1 score of 0.70 and a mean disagreement rate of 0.05 against reference maps. TIF offers a potential practical and efficient pathway for creating 10-m versions of NASA's HLS products, opening new opportunities for fine-scale, time-sensitive Earth observations. • A novel satellite spatial-temporal fusion algorithm called TIF is proposed. • TIF creates 10-m harmonized Landsat and Sentinel-2 surface reflectance time series. • TIF outperforms other state-of-the-art methods in spectral and spatial accuracy. • TIF operates purely on time-series data, without requiring image pairs. • TIF predictions can be used for detecting land changes.

Topics & Concepts