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ARS Home » Plains Area » Temple, Texas » Grassland Soil and Water Research Laboratory » Research » Publications at this Location » Publication #415901

Research Project: Enhancing Cropping System and Grassland Sustainability in the Texas Gulf Coast Region by Managing Systems for Productivity and Resilience

Location: Grassland Soil and Water Research Laboratory

Title: vegspec: A compilation of spectral vegetation indices and transformations in Python

Author
item Thorp, Kelly

Submitted to: SoftwareX
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/4/2024
Publication Date: 10/9/2024
Citation: Thorp, K.R. 2024. vegspec: A compilation of spectral vegetation indices and transformations in Python. SoftwareX. https://doi.org/10.1016/j.softx.2024.101928.
DOI: https://doi.org/10.1016/j.softx.2024.101928

Interpretive Summary: Spectral vegetation indices are mathematical formulae that combine information on the reflectance of light at various wavelengths to estimate properties of vegetation. Hundreds of these indices have been developed by remote sensing scientists over the past half century. The goal of this effort was to codify the calculation of these indices in the modern Python programming language and release the software to open-source repositories. The software will be useful to Python programmers and scientists who want to make comprehensive calculations of existing spectral vegetation indices for their spectral reflectance data.

Technical Abstract: The vegspec software package is a Python-based compilation of 1) more than 145 spectral vegetation indices from refereed literature over the past half century and 2) algorithms for several common spectral transformations, including first and second derivatives of reflectance, the logarithm of inverse reflectance and its derivatives, and continuum removal. The software was developed to support analyses of spectral reflectance data from field spectroradiometers and hyperspectral imagers. The outputs are useful for data mining or machine learning studies that relate plant biophysical variables (e.g., leaf chlorophyll content) with vegetative spectral properties.