Spatio-temporal fusion for daily Sentinel-2 images

Wang, Qunming and Atkinson, Peter M. (2018) Spatio-temporal fusion for daily Sentinel-2 images. Remote Sensing of Environment, 204. pp. 31-42. ISSN 0034-4257

[thumbnail of Spatio-temporal fusion for daily Sentinel-2 images_accepted author manuscript]
PDF (Spatio-temporal fusion for daily Sentinel-2 images_accepted author manuscript)
Spatio_temporal_fusion_for_daily_Sentinel_2_images_accepted_author_manuscript.pdf - Accepted Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivs.

Download (1MB)


Abstract Sentinel-2 and Sentinel-3 are two newly launched satellites for global monitoring. The Sentinel-2 Multispectral Imager (MSI) and Sentinel-3 Ocean and Land Colour Instrument (OLCI) sensors have very different spatial and temporal resolutions (Sentinel-2 MSI sensor 10 m, 20 m and 60 m, 10 days, albeit 5 days with 2 sensors, conditional upon clear skies; Sentinel-3 OLCI sensor 300 m, < 1.4 days with 2 sensors). For local monitoring (e.g., the growing cycle of plants) one either has the desired spatial or temporal resolution, but not both. In this paper, spatio-temporal fusion is considered to fuse Sentinel-2 with Sentinel-3 images to create nearly daily Sentinel-2 images. A challenging issue in spatio-temporal fusion is that there can be very few cloud-free fine spatial resolution images temporally close to the prediction time, or even available, strong temporal (i.e., seasonal) changes may exist. To this end, a three-step method consisting of regression model fitting (RM fitting), spatial filtering (SF) and residual compensation (RC) is proposed, which is abbreviated as Fit-FC. The Fit-FC method can be performed using only one Sentinel-3–Sentinel-2 pair and is advantageous for cases involving strong temporal changes (i.e., mathematically, the correlation between the two Sentinel-3 images is small). The effectiveness of the method was validated using two datasets. The created nearly daily Sentinel-2 time-series images have great potential for timely monitoring of highly dynamic environmental, agricultural or ecological phenomena.

Item Type:
Journal Article
Journal or Publication Title:
Remote Sensing of Environment
Additional Information:
This is the author’s version of a work that was accepted for publication in Remote Sensing of Environment. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Remote Sensing of Environment, 204, 2018 DOI: 10.1016/j.rse.2017.10.046
Uncontrolled Keywords:
?? sentinel-2sentinel-3image fusiondownscalingsoil sciencecomputers in earth sciencesgeology ??
ID Code:
Deposited By:
Deposited On:
13 Nov 2017 11:40
Last Modified:
20 Mar 2024 00:38