Protocol for Measuring Leaf Area Index (LAI) in Urban Turfgrass Using a Destructive Method

Biomass and Leaf Area Index (LAI) are crucial parameters for accurate evapotranspiration modeling. LAI is particularly useful for assessing the photosynthetic capacity of turfgrass canopies. However, there is a shortage of instruments available to measure ground-based LAI for urban turfgrass, necessitating the use of destructive methods to generate LAI input for remote-sensing-based surface energy balance models. To address this issue, turfgrass samples were collected and then their leaves were scanned. Using unsupervised classification technique, K-means, and the scanned leaves, leaf area was estimate to calculate LAI for urban turfgrass. To facilitate the process, we developed a Google Collaboratory notebook that employs the K-means algorithm for estimating leaf area.

生物量与叶面积指数（Leaf Area Index, LAI）是精准蒸散量建模的关键参数。叶面积指数尤其适用于评估草坪冠层的光合能力。然而，当前可用于实地测量城市草坪叶面积指数的仪器十分匮乏，因此不得不采用破坏性方法，为基于遥感的地表能量平衡模型生成叶面积指数输入数据。为解决这一问题，研究团队采集了草坪样本并对其叶片进行扫描。借助无监督分类技术K-means与扫描得到的叶片图像，研究人员估算叶片面积以计算城市草坪的叶面积指数。为简化该流程，本研究开发了一款谷歌协作实验室（Google Collaboratory）笔记本，通过K-means算法实现叶片面积的估算。