Remote sensing observations of green vegetation (GV), impervious surface (IS), and bare soil (BS) fractional cover are essential for understanding climate change, characterizing ecosystem functions, monitoringurbanization process. As an important indicator of urbanization, the continuous increase of impervious surfaces alters the radiative transfer process at the surface, causing a series of environmental problems. Therefore, timely and accurate monitoring of the spatial and temporal changes in impervious surfaces and their impact on the ecological environment is of great significance for a comprehensive understanding of the process of urbanization as well as for the planning and construction of future cities. This study aims to propose a generalized method for the accurate estimation of GV, IS, and BS coverage. In this study, the visible impervious surface index (VISI), (Br-Bg)/(Br+Bg), was developed using measured spectral data of GV, IS, and BS, and analyzing their spectral characteristics to determine the spectral bands where they can be distinguished. Furthermore, the VISI combined with the NDVI was utilized to establish a triangular space for linear unmixing of the satellite image data to estimate the coverage of its GV, IS, and BS. Finally, the generalizability of this method was verified using UAV and satellite image data, with pearson correlation coefficient > 0.69. The results demonstrate that the VISI index proposed in this study is feasible for long-term series of multispectral imagery and large-scale coverage estimation.

中文翻译：

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基于可见光-近红外卫星影像的基于 NDVI-VISI 响应空间的分数覆盖估计


绿色植被 （GV） 、不透水表面 （IS） 和裸土 （BS） 部分覆盖的遥感观测对于了解气候变化、表征生态系统功能、监测城市化进程至关重要。不透水面作为城市化的重要指标，不透水面的不断增加改变了地表的辐射传输过程，引发了一系列环境问题。因此，及时、准确地监测不透水面的时空变化及其对生态环境的影响，对于全面认识城市化进程以及未来城市的规划建设具有重要意义。本研究旨在提出一种准确估计 GV、IS 和 BS 覆盖率的通用方法。在本研究中，利用 GV、IS 和 BS 的实测光谱数据开发了可见光不透表面指数 （VISI）、（Br-Bg）/（Br+Bg），并分析了它们的光谱特性，以确定可以区分它们的光谱波段。此外，利用 VISI 与 NDVI 相结合，建立了一个三角形空间，用于卫星图像数据的线性解混，以估计其 GV、IS 和 BS 的覆盖范围。最后，使用无人机和卫星图像数据验证了该方法的泛化性，皮尔逊相关系数 > 0.69。结果表明，本文提出的 VISI 指数对于长期系列多光谱影像和大尺度覆盖估计是可行的。