Leaf area index (LAI) is an important structural parameter of vegetation, and is used in many models of climate and ecosystem services. Maps of LAI are typically produced by relating satellite remote sensing with field-based measurements of LAI, but such field measurements are time consuming to collect over large areas. In this study we develop a rapid and scalable method for mapping LAI by fusing high-resolution freely-available Sentinel 2 satellite imagery with ground measurements of LAI extracted from a large publicly available database of street-level panoramic photographs. The use of existing street-level photographs allowed large numbers of training data to be automatically obtained. The method developed here was validated against a field dataset collected using established techniques. The use of existing online databases of street-level photographs will allow rapid mapping of LAI in many situations worldwide. The technique developed here may be particularly useful in cities, which have high heterogeneity in vegetation, and high densities of street level photographs collected along road networks. The approach could be applied to map LAI at high resolution across very large areas, for national- or continental-scale comparison.

叶面积指数（LAI）是植被的重要结构参数，并在许多气候和生态系统服务模型中使用。LAI的地图通常是通过将卫星遥感与基于LAI的基于现场的测量值相关联而生成的，但是，这种现场测量值在大面积上收集非常耗时。在这项研究中，我们通过将高分辨率的免费提供的Sentinel 2卫星图像与LAI的地面测量值融合在一起，从而开发了一种快速且可扩展的LAI映射方法，该地面测量值是从大型可公开获得的街道全景照片数据库中提取的。使用现有的街道照片可以自动获取大量训练数据。此处开发的方法已针对使用既有技术收集的现场数据集进行了验证。使用现有的街道图片在线数据库将可以在全球范围内快速绘制LAI的地图。此处开发的技术在植被异质性很高，沿道路网络收集的街道照片密度高的城市中可能特别有用。该方法可用于在非常大的区域内以高分辨率对LAI进行地图绘制，以进行国家或大陆范围的比较。