With the launch of Sentinel-2 new opportunities for large scale urban mapping arise. However, the spectral information embedded in the Sentinel-2 20 m spatial resolution bands cannot yet be fully explored in heterogeneous urban landscapes. The 20 m image pixels are often composed of different land covers, resulting in a difficult to interpret mixed pixel spectrum. Here, we propose an unmixing-based image fusion algorithm (UnFuSen2) that self-adapts to the spectral variability of varying land covers and improves the image fusion accuracy by constraining the unmixing equations on the basis of spectral mixing models and the correlation between spectral bands of coarse and fine spatial resolution, respectively. When compared to alternative state-of-the-art downscaling methods UnFuSen2 consistently showed the highest accuracy when applied across test sites in three different European cities (RMSE_UnFuSen2 = 203 vs RMSE_alternatives = [252, 337]). In a next step, we applied Multiple Endmember Spectral Mixture Analysis (MESMA) on the downscaled Sentinel-2 image cube (i.e. ten 10 m bands) to generate subpixel urban land cover fractions. We compared our MESMA results against the traditional MESMA output as applied on the original Sentinel-2 image cube (i.e. four 10 m bands and six 20 m bands) and tested its robustness against reference data obtained over all three study sites. Results revealed an average decrease in RMSE of respectively 18% and 8% for impervious surface and vegetation fractions when our approach was compared to the traditional MESMA outcomes.

随着Sentinel-2的推出，出现了大规模城市制图的新机会。但是，在异类城市景观中尚未完全探索Sentinel-2 20 m空间分辨率带中嵌入的光谱信息。20 m图像像素通常由不同的土地覆盖物组成，导致难以解释混合像素光谱。在此，我们提出了一种基于分解的图像融合算法（UnFuSen2），该算法可自适应变化的土地覆盖物的光谱变异性，并通过在光谱混合模型和光谱带之间的相关性的基础上限制分解方程，从而提高图像融合精度。分别具有粗略和精细的空间分辨率。_UnFuSen2  = 203 vs RMSE_替代 = [ _252，337 ]）。在下一步中，我们在缩小的Sentinel-2图像立方体（即10个10 m波段）上应用了多端元光谱混合分析（MESMA），以生成亚像素城市土地覆盖率。我们将我们的MESMA结果与应用于原始Sentinel-2图像立方体（即四个10 m波段和六个20 m波段）的传统MESMA输出进行了比较，并针对在所有三个研究地点获得的参考数据测试了其鲁棒性。结果表明，与传统的MESMA结果相比，不透水的表面和植被部分的RMSE平均分别降低了18％和8％。