Covers and surface materials composition of urban, peri-urban and rural landscapes is significant information for environmental, climate and human-ecosystems interaction monitoring and modeling, as well as for addressing specific urban planning and improving environmental management. In this study the multiple endmember spectral mixture analysis (MESMA) was exploited to overcome the low spatial resolution and spectral mixture of the hyperspectral (HS) satellite PRISMA (PRecursore IperSpettrale della Missione Applicativa). A multi-level detail large-scale mapping of complex urban and rural fractional composition of land covers and surface materials (LCSM) was carried out. High-resolution airborne data enabled the collection of pure endmembers for each impervious and pervious surface materials, also acting as a reference for assessing resulted sub-pixel fractional covers at the pixel scale. Absolute Errors (AE) have shown that MESMA is very promising for quantifying complex landscape composition at the sub-pixel level from PRISMA HS data (overall AE <=0.282; per-class AE < 0.336, with average values ​​even < 0.1 for some classes). Bias Errors (BE) instead attested that under- and overestimation errors for each class were contained in ±0.25 median values for all three levels of detail (i.e., number of classes) tested. These results demonstrate that the proposed framework integrating MESMA and PRISMA HS is a valuable tool to provide detailed land composition in complex landscapes to support urban planning and enhance environmental sustainability.

中文翻译：

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采用多端元光谱混合分析 （MESMA） 对城乡土地覆盖和表面材料组成的 PRISMA 成像

城市、城郊和农村景观的覆盖物和表面材料组成是环境、气候和人类生态系统相互作用监测和建模以及解决特定城市规划和改善环境管理的重要信息。在本研究中，利用多端元光谱混合分析 （MESMA） 来克服高光谱 （HS） 卫星 PRISMA （PRecursore IperSpettrale della Missione Applicativa） 的低空间分辨率和光谱混合问题。对土地覆盖和表面材料 （LCSM） 的复杂城乡分数组成进行了多层次细节大尺度制图。高分辨率机载数据能够收集每种不透水和透水表面材料的纯端元，也可以作为在像素尺度上评估产生的亚像素分数覆盖的参考。绝对误差 （AE） 表明，MESMA 非常有希望从 PRISMA HS 数据中量化亚像素级别的复杂景观构成（总体 AE <=0.282;每类 AE < 0.336，某些类别的平均值甚至 为 < 0.1）。相反，偏倚误差 （BE） 证明，对于测试的所有三个细节级别（即类别数），每个类别的低估和高估误差都包含在 ±0.25 的中位数中 。这些结果表明，整合 MESMA 和 PRISMA HS 的拟议框架是一个有价值的工具，可以在复杂的景观中提供详细的土地构成，以支持城市规划和增强环境可持续性。