<p>The reliability of global land cover maps, such as the ESA WorldCover 2020 product, depends critically on understanding the spatial distribution of classification error, which is often obscured by traditional overall accuracy metrics. Many studies assume that topographic complexity is the primary driver of this variation, particularly in geographically diverse regions. This research aims to test this hypothesis by modeling the spatial impact of topographic variables on the accuracy of the ESA WorldCover 2020 product in Tartous Governorate, Syria. An integrated methodological framework was employed, based on aggregating validated accuracy data onto a hexagonal grid and applying a series of spatial statistical techniques, including Global and Local Moran’s I (LISA), Hot Spot Analysis (Getis-Ord Gi*), and Geographically Weighted Regression (GWR). The results revealed significant spatial variation in accuracy (ranging from 20% to 100%) and the presence of statistically significant local clusters of low accuracy (cold spots) in specific areas. To explain these patterns, a validation of the sampling design was performed using Wilson Score Intervals, followed by a landscape analysis using the landscapemetrics package in R. The integration of fragmentation metrics (Edge Density and Largest Patch Index) significantly improved the explanatory power of the analysis (Adjusted R² = 0.125; Local R² up to 0.24). Crucially, the model revealed a dual influence: while topography (slope) retained an unexpected positive effect, landscape complexity, specifically high edge density, emerged as a significant negative driver of accuracy. These findings challenge the common assumption that terrain roughness is the primary source of error, suggesting that in this region, landscape fragmentation is the dominant constraint.</p>