Skip to content

Compute land surface temperature(LST) from Landsat-8 data using the mono-window and split-window techniques in literature.

License

Notifications You must be signed in to change notification settings

dongyi1996/pylandtemp

 
 

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

pylandtemp

GitHub license GitHub stars GitHub forks GitHub issues

Description

pylandtemp is a Python tool for retrieving land surface temperature from NASA's Landsat 8 satellite imagery using the mono-window and split-window techniques in literature. Additionally, it also provides multiple methods for computing land surface emissivity. It is targeted towards supporting research and science workflows in many fields including climate science, earth sciences, remote sensing, geospatial data science, environmental studies, among others.

Even though only Landsat 8 images are currently 'officially' supported, the methods available via this Python tool can be applied to other dataset including ASTER and MODIS.

What's new:

  • December 2021: version 0.0.1-alpha.1 pre-release version is out on PyPI. Find it here
  • December 2021: Implementing tutorial notebooks based on the different methods. Find them here

Installation: PyPI

pip install pylandtemp

How to start using pylandtemp

The notebooks here are a good place to start.

How to contribute to pylandtemp

All kinds of contributions are welcome --- development of enhancements, bug fixes, documentation, tutorial notebooks, new methods, new data, etc....

A guide to get you started with contributing will soon be made available.

@Misc{pylandtemp,
author = {Oladimeji Mudele},
title =        {pylandtemp - a Python tool for retrieving land surface temperature from Landsat 8 satellite imagery},
howpublished = {Github},
year =         {2021},
url =          {https://github.com/pylandtemp}
}

About

Compute land surface temperature(LST) from Landsat-8 data using the mono-window and split-window techniques in literature.

Resources

License

Stars

Watchers

Forks

Packages

No packages published

Languages

  • Jupyter Notebook 96.1%
  • Python 3.9%