Long-term composited Enhanced Normalized Difference Impervious Surface Index (ENDISI) for the greater Phoenix, Arizona, USA, metropolitan area and the surrounding Sonoran desert derived from annual and seasonal Landsat imagery, 1998 to 2023

This data package consists of multiple decades of Enhanced Normalized Difference Impervious Surface Index (ENDISI) raster data across the Central Arizona-Phoenix Long-Term Ecological Research (CAP LTER) study area within metropolitan Phoenix, Arizona, USA, temporally aggregated by year and by four meteorological seasons (winter, spring, summer, fall). To serve as a proxy measurement of impervious surface and urbanization across years and seasons, we derived values of ENDISI – following the methods of Chen et al. 2019 – from annual and seasonal composites of 30-m resolution Landsat 5-9 Level-2 Surface Reflectance imagery. Finally, we exported images as individual GeoTIFF raster files, each with five bands corresponding values summarized annually (band 1) and seasonally (bands 2-5). All imagery retrieval and data processing were completed with Google Earth Engine (Gorelick et al. 2017) and program R. A complete description of data processing methods, including the aggregation of imagery by year and season and the calculation of the spectral index, can be found in the data package metadata (see 'Methods and Protocols') and accompanying Javascript code.

### citations

- Gorelick N, Hancher M, Dixon M, et al. (2017) Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment 202:18–27. https://doi.org/10.1016/j.rse.2017.06.031

本数据包包含美国亚利桑那州菲尼克斯都市圈内中央亚利桑那-菲尼克斯长期生态研究（Central Arizona-Phoenix Long-Term Ecological Research, CAP LTER）区域内数十年的增强型归一化差异不透水面指数（Enhanced Normalized Difference Impervious Surface Index, ENDISI）栅格数据，时间维度上按年度及四个气象季节（冬季、春季、夏季、秋季）聚合。为实现跨年度与季节的不透水面及城市化程度的代理测量，我们遵循Chen等人2019年提出的方法，基于30米分辨率Landsat 5-9 Level-2地表反射率影像的年度及季节合成数据，计算得到ENDISI值。最终，我们将影像导出为独立的GeoTIFF栅格文件，每个文件包含5个波段，分别对应年度汇总值（波段1）及季节汇总值（波段2-5）。所有影像检索与数据处理均通过Google Earth Engine（Gorelick等人2017）及R语言完成。数据处理方法的完整说明（包括影像的年度与季节聚合及光谱指数计算）可参见数据包元数据（详见‘Methods and Protocols’）及随附的Javascript代码。