Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
Image Processing Laboratory, University of Valencia, Valencia, 46980, Spain.
Sci Data. 2023 Apr 8;10(1):197. doi: 10.1038/s41597-023-02096-0.
Spectral Indices derived from multispectral remote sensing products are extensively used to monitor Earth system dynamics (e.g. vegetation dynamics, water bodies, fire regimes). The rapid increase of proposed spectral indices led to a high demand for catalogues of spectral indices and tools for their computation. However, most of these resources are either closed-source, outdated, unconnected to a catalogue or lacking a common Application Programming Interface (API). Here we present "Awesome Spectral Indices" (ASI), a standardized catalogue of spectral indices for Earth system research. ASI provides a comprehensive machine readable catalogue of spectral indices, which is linked to a Python library. ASI delivers a broad set of attributes for each spectral index, including names, formulas, and source references. The catalogue can be extended by the user community, ensuring that ASI remains current and enabling a wider range of scientific applications. Furthermore, the Python library enables the application of the catalogue to real-world data and thereby facilitates the efficient use of remote sensing resources in multiple Earth system domains.
光谱指数来源于多光谱遥感产品,被广泛用于监测地球系统动态(如植被动态、水体、火灾情况)。由于提出的光谱指数数量迅速增加,因此对光谱指数目录和计算工具的需求也很高。然而,这些资源大多数是闭源的、过时的、与目录没有连接的,或者缺乏通用的应用程序编程接口(API)。在这里,我们介绍“Awesome Spectral Indices”(ASI),这是一个用于地球系统研究的标准化光谱指数目录。ASI 提供了一个全面的、机器可读的光谱指数目录,该目录与一个 Python 库相连。ASI 为每个光谱指数提供了广泛的属性,包括名称、公式和来源参考。该目录可以由用户社区扩展,以确保 ASI 保持最新状态,并支持更广泛的科学应用。此外,Python 库使目录能够应用于实际数据,从而促进了在多个地球系统领域中有效利用遥感资源。