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Complex terrain and a lack of reference data pose challenges to long-term impervious surface percentage (ISP) mapping in mountainous areas. In this paper, a long-term ISP inversion method based on multi-source data collected in mountainous areas is developed. First, the impervious surfaces in 2019 were extracted by combining the Sentinel-2, Sentinel-1, and terrain data to obtain the ISP reference data. Then, invariant sample sets for 1990, 2000, and 2010 were constructed based on the publicly available land cover products and reference data. Finally, regression models were designed to invert the long-term ISP. The results show that the fusion of multi-source data can improve the classification accuracy in mountainous areas, and the sample construction method can ensure the efficiency and objectivity of the inversion. The root mean square error values of the inversion results are 7.84%–13.34%. This method provides decision-making support for urban planning and a theoretical basis for environmental protection.

Improving long-term impervious surface percentage mapping in mountainous areas based on multi-source remote sensing data