Estimating Large-Scale Interannual Dynamic Impervious Surface Percentages Based on Regional Divisions

Impervious surface information is an important indicator to describe urban development and environmental changes. The substantial increase in impervious surface area will have a significant impact on the regional landscape and environment. Therefore, the timely and accurate acquisition of large-scale impervious surface percentage (LISP) is of great significance for urban management and ecological assessment. However, previous LISP estimation methods often ignored the impact of regional geographic environment and climate differences on remote sensing information, resulting in low overall accuracy and obvious regional differences in the estimated results. Thus, in this study, based on the time-series characteristics of multi-temporal remote sensing images combined with the information on geographical environment and climate heterogeneity, a method of time-series remote sensing image fusion and LISP estimation based on regional divisions was proposed. Firstly, the entire region was divided into several regions according to the spatial differences of Köppen–Geiger climate data and MODIS NDVI time-series data. Subsequently, adaptive time-series image fusion methods and remote sensing feature construction methods were proposed for different regions. Finally, the proposed method was used to estimate the percentage of impervious surfaces in other years in Asia. The results indicate that the overall R² of each region is better than 0.82, and the estimation models have a good ability to transfer across time and can directly estimate the impervious surface percentage in other years without using additional samples. In addition, compared with other existing impervious surface products, the proposed method has higher overall estimation accuracy and regional consistency.