ABSTRACT:

Spectral Indices (SIs) were selected from the Awesome Spectral Indices (ASI) catalog (https://github.com/awesome-spectral-indices/awesome-spectral-indices). This catalog contains 231 SIs and is divided into 8 groups, which mostly represent specific application domains, namely: vegetation, water, burn, snow, urban, radar, soil, and kernel indices (Montero et al. 2023). Seventy-night SIs were selected from the ASI catalog, which were computed from Uncrewed Aircraft System (UAS) multispectral (blue, green, red, red edge, and near-infrared) and thermal bands. The Jupyter Notebook has the SIs formulas and compute them by having thermal and multispectral UAS information.

ABSTRACT:

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.