## 4 Discussion  
### Comparison with Other Global Urban Products  
To validate the reliability and accuracy of our constructed ISC dataset, we conducted comprehensive comparisons with existing global urban products and assessed potential uncertainties in our methodology across multiple urban agglomerations.  
Figure 12 presents the Pearson correlation coefficient distributions between our ISC dataset and the Global Human Settlement Layer (GHSL) data across three major urban agglomerations in China: (a) Beijing-Tianjin-Hebei, (b) Yangtze River Delta, and (c) Pearl River Delta. Due to the GHSL five-year interval data availability, we employed a sampling strategy extracting corresponding years from both datasets to assess correlation coefficients and constructed histograms of these correlations.  
The results demonstrate consistently high correlations across all three regions: Beijing-Tianjin-Hebei shows a mean correlation of \(\mu=0.8218\) (\(\sigma=0.1750\)), Yangtze River Delta achieves \(\mu=0.8899\) (\(\sigma=0.1221\)), and Pearl River Delta reaches \(\mu=0.8943\) (\(\sigma=0.1171\)). The progressive improvement in correlation coefficients from north to south may reflect regional differences in urbanization patterns, data quality, or methodological sensitivities. Notably, all three distributions show strong right-skewed patterns with most correlation  
Figure 12: Pearson correlation coefficient distributions between ISC and GHSL data across three major urban agglomerations. (a) Beijing–Tianjin–Hebei. (b) Yangtze River Delta. (c) Pearl River Delta.  
values exceeding 0.8, confirming the robustness of our ISC methodology across diverse urban contexts.  
Figure 13a presents a comprehensive time series comparison of annual impervious surface area trends between our ISC dataset and multiple established global urban products, including ESA-CCI land cover data, [PERSON] et al.'s urban extent dataset [24], and Global Impervious Surface Area (GISA), over the period 1985-2019. All datasets capture the characteristic rapid urbanization pattern in the Beijing-Tianjin-Hebei region, with consistent identification of the major expansion peaks around 2010-2015. However, subtle differences exist in absolute values, which can be attributed to varying methodological approaches: ESA-CCI employs multi-sensor fusion techniques, [PERSON] et al.'s dataset relies on nighttime light calibration, while GISA and our ISC use similar Landsat-based approaches [12]. Despite these methodological differences, the overall temporal trends remain remarkably consistent, validating the fundamental urbanization dynamics captured by our dataset.  
Given that GISA employs the most similar data acquisition methodology to our ISC approach and provides the most temporally consistent coverage, we conducted an in-depth correlation analysis between these two datasets (Figure 13b). Using 700 randomly distributed sampling points across the study area from 1985 to 2019, the analysis reveals a strong positive correlation (R\({}^{2}\) = 0.804, \(p\) < 0.001), demonstrating high consistency between our ISC estimates and the established GISA product. The linear relationship indicates that our ISC values are slightly conservative compared to GISA, which can be attributed to our spatial aggregation approach that tends to smooth extreme values within 1 km grid cells while maintaining the overall spatial and temporal patterns.