Spatio-temporal fusion for daily Sentinel-2 images

REMOTE SENSING OF ENVIRONMENT

卷:204

Article

Wang, Qunming^1,2  Atkinson, Peter M.^1,3,4 

[1] Univ Lancaster, Lancaster Environm Ctr, Lancaster LA1 4YQ, England

[2] Ctr Ecol & Hydrol, Lancaster LA1 4YQ, England

[3] Univ Southampton, Geog & Environm, Southampton SO17 1BJ, Hants, England

[4] Queens Univ Belfast, Sch Geog Archaeol & Palaeoecol, Belfast BT7 1NN, Antrim, North Ireland

关键词: Sentinel-2;    Sentinel-3;    Image fusion;    Downscaling;   

DOI  :  10.1016/j.rse.2017.10.046

来源: Elsevier

【 摘 要 】

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.

【 授权许可】

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