PKU LAnd Surface Temperature(PKULAST)

pkulast is a Python module capable of retrieving land surface temperature (LST) from thermal infrared remote sensing data. It is built on-top of various scientific Python packages (numpy, scipy, etc.). With the help of pkulast, you can obtain algorithm coefficients of an existing LST retrieval algorithm for specific TIR sensors, or create a novel form of algorithm for new TIR sensor prototypes from scratch).

• Website: PKULAST
• Documentation: PKULAST Documentation(TBD)

Features

• Native Python implementation. A native Python implementation for a variety of land surface temperature retrieval algorithms. To see the list of all supported algorithms, check this link.

• Interface to MODTRAN. A MODTRAN wrapper class is implemented for reference purposes and integration. This provides access to atmospheric radiative transfer simulation —.

• Conceptual Models. A conceptual forward model to simulate the top of atmosphere radiance; and inverse model to retrieve and validate land surface temperature.

Dependencies

In most cases you will want to follow the requirements defined in the requirements/*.txt files in the package.

Base dependencies

scipy
numpy
satpy
metpy
pandas
pygrib
...

Note: Installing pygrib is complicated, please see: pygrib install instructions using conda

Installation

To install pkulast, simply type the following command:

$ pip install pkulast.tar.gz

This will install the latest release from the Python package index.

Configuration

pkulast supports a configuration file in configparser syntax. The configuration is handled by the pkulast.config module. The default file location is <install_dir>/pkulast.cfg but can be changed using the PKULAST_CONFIG environment variable.

Contributing

This project is open for contributions. Here are some of the ways for you to contribute:

• Bug reports/fix
• Features requests
• Use-case demonstrations
• Documentation updates

In case you want to implement PKULAST, please read our Developer's Guide to help you integrate your implementation in our API.

To make a contribution, just fork this repository, push the changes in your fork, open up an issue, and make a Pull Request!

Cite

If you used pkulast in your research or project, please cite our work:

@ARTICLE{2022pkulast,
   author = {Zhu, Jinshun and Ren, Huazhong and Ye, Xin and Teng, Yuanjian and Zeng, Hui and Liu, Yu and Fan, Wenjie},
   title = "{PKULAST-An Extendable Model for Land Surface Temperature Retrieval from Thermal Infrared Remote Sensing Data}",
   publisher = {IEEE Xplore},
   journal = {IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing},
   pages = {1--17},
   issn = {2151-1535},
   doi = {10.1109/JSTARS.2022.3217105},
   year = 2022,
   month = oct
}