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Spatio-temporal fusion is a technique applied to create images with both fine spatial and temporal resolutions by blending images with different spatial and temporal resolutions. Spatial unmixing is a widely used approach for spatio-temporal fusion, which requires only the minimum number of input images. However, ignorance of spatial variation in land cover between pixels is a common issue in existing spatial unmixing methods. For example, all coarse neighbors in a local window are treated equally in the unmixing model, which is inappropriate. Moreover, determination of the appropriate number of clusters in the known fine spatial resolution image remains a challenge. In this paper, a geographically weighted spatial unmixing (SU-GW) method was proposed to address the spatial variation in land cover and increase the accuracy of spatio-temporal fusion. SU-GW is a general model suitable for any spatial unmixing methods. Specifically, the existing regularized version and soft classification-based version were extended with the proposed geographically weighted scheme, producing 24 versions (i.e., 12 existing versions were extended to 12 corresponding geographically weighted versions) for spatial unmixing. Furthermore, the cluster validity index of Xie and Beni (XB) was introduced to determine automatically the number of clusters. A systematic comparison between the experimental results of the 24 versions indicated that SU-GW was effective in increasing the prediction accuracy. Importantly, all 12 existing methods were enhanced by integrating the SU-GW scheme. Moreover, the identified most accurate SU-GW enhanced version was demonstrated to outperform two prevailing spatio-temporal fusion approaches in a benchmark comparison. Therefore, it can be concluded that SU-GW provides a general solution for enhancing spatio-temporal fusion, which can be used to update existing methods as well as future potential versions.

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