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:

The Jupyter Notebook was arranged for our golf course case study based Kljun et al., 2015 and Volk et al., 2023. This repository shows the eddy covariance data located in the Eagle Lake golf course, Roy, Utah.
The daily flux footprints were generated for evaluating the Two Source Enegy Balance (TSEB) model and OpenET data on the golf course study.

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.