A Backup Way for TSEB Model Installation

Subtitle - TSEB-Backup-Old-Style
This repository serves as a backup of the previous method used to install the TSEB model for our research. It is based on the version available at https://github.com/hectornieto/pyTSEB, as published around 2021.
Please consider citing the original work as referenced in the included CITATION.cff file below. The original setup included two key folders, which are preserved here:

• pyTSEB-master.zip
• pypro4sail-master.zip

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Nieto"
  given-names: "Hector"
  orcid: "https://orcid.org/0000-0003-4250-6424"
- family-names: "Guzinski"
  given-names: "Radoslaw"
  orcid: "https://orcid.org/0000-0003-0044-6806"
- family-names: "Kustas"
  given-names: "William P."
  orcid: "https://orcid.org/0000-0001-5727-4350"

title: "pyTSEB: A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data"
version: 2.2
doi: 10.5281/zenodo.594732
date-released: 2018-03-04
url: "https://github.com/hectornieto/pyTSEB"

Before the installation, required software are:

• Anaconda
• ArcGIS Pro
  

TSEB model installation

Since much of our research involved working with ArcGIS Pro, we frequently relied on its built-in functions. As a result, we installed and ran the TSEB model within the ArcGIS Pro environment. Steps for model installation in the ArcGIS Pro environment.

1. Create a compatible Python environment. Copy the existing ArcGIS Pro Python environment to the envs folder of your Anaconda installation. This ensures compatibility with ArcGIS Pro's Python setup.
2. Download the TSEB model.
   • Option 1: download from the official repository: https://github.com/hectornieto/pyTSEB
   • Option 2: download pyTSEB-master.zip from this repository.
3. Organize the files. Extract and copy all TSEB files to either:
   • The environment folder you just created, or
   • A project directory where you can better manage your code and data.
4. Install the TSEB model.
   • Open the Anaconda Prompt, and activate the environment you just created.
   • Navigate to the directory containing setup.py using cd.
   • Run the following command:

   python setup.py install
   

   (Note, make sure you're in the correct directory before running the command.)
5. Install the pyPro4Sail dependency. Follow a similar process for installing pyPro4Sail:
   • Repository: https://github.com/hectornieto/pyPro4Sail
   • Or download pypro4sail-master.zip from this repository.
6. Installing Py6S Python Interface:

   pip install Py6S
   

7. Done.

You should now be ready to run the TSEB model within your ArcGIS Pro-compatible Python environment.

Demo for input prepartion for the TSEB-PT model

The flowchart below provides an overview of the general structure for preparing inputs for the TSEB-PT model using UAV imagery. The specific process for generating these inputs may vary depending on individual preferences and workflows. Hopefully, this flowchart serves as a helpful guide to support you in developing your own approach to input preparation.

Run model

Rather than focusing on the detailed steps for running the TSEB model, this repository provides demo data to help you get started:

• "1_Demo_Data": This folder contains image data you can use to test and run the model.
• "2_Decimal_Data.xlsx": This Excel file includes sample decimal-format observations for model input.

Citation

If you use this reporitory in your work, please cite it using the following DOI:

BibTeX:

@misc{gao2025tseb,
  author       = {Rui Gao},
  title        = {A Backup Way for TSEB Model Installation – TSEB-Backup-Old-Style},
  year         = {2025},
  publisher    = {Zenodo},
  doi          = {10.5281/zenodo.15871797},
  url          = {https://doi.org/10.5281/zenodo.15871797}
}

Repository update information:

Created by January 26th, 2022
1st update by August 1st, 2025

Contact information if issues were found:

Rui Gao
[email protected]
[email protected]
[email protected]