**5. Conclusions**

In this study, three simple and easy-to-promote spatiotemporal fusion models, FSDAF, STDFA, and Fit\_FC, are selected to fuse GF-2 and PS high-resolution satellite data. Four classical evaluation indexes, SSIM, CC, RMSE, and AAD, and visual analysis are adopted. The applicability of the three models for land use classification in karst areas is discussed comprehensively. The results show that the three models can improve the accuracy of land surface recognition, but the accuracy is different in different land use types. Among them, the fusion results of the FSDAF model can improve the recognition accuracy of land and water interface. Different from the FSDAF model, the STDFA model has the highest resolution of fusion image in mountain region, with significant improvement of fusion image resolution and rich details. The fusion effect of the Fit\_FC model is poor in the boundary region of water and land. The image is blurred, and the ground feature information cannot be restored clearly, which is not conducive to the classification of land and water boundary land use. However, the Fit\_FC model can clearly show land use change in vegetation covered areas. Therefore, this paper adopts a high-resolution spatiotemporal fusion algorithm to effectively improve the classification of land use in karst areas. It is of great significance to optimize the allocation of land resources and realize ecological restoration in fragile karst mountainous areas.

**Author Contributions:** Y.Z., C.S., S.Z., R.Y., X.L. and G.Z. contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Y.Z., C.S., S.Z., R.Y., X.L. and G.Z. The first draft of the manuscript was written by Y.Z. commented on previous versions of the manuscript. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research work was supported jointly by the Outstanding Youth of Science and Technology program of Guizhou Province of China ((2021) 5615), the Department of Science and Technology program of Guizhou Province of China ((2022) 213 and (2023) 60).

**Data Availability Statement:** Not applicable.

**Acknowledgments:** Thanks to China Resources Satellite Application Center and PlanetScope data publishing site for providing us with the data, which are important to our research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


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