A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data

hectornieto, updated 🕥 2023-01-18 08:16:03



This project contains Python code for Two Source Energy Balance models (Priestley-Taylor TSEB-PT, Dual Time Difference DTD and TSEB with component soil and canopy temperatures TSEB-2T) for estimating sensible and latent heat flux (evapotranspiration) based on measurements of radiometric surface temperature.

The project consists of:

  1. lower-level modules with the basic functions needed in any resistance energy balance model

  2. higher-level scripts for easily running TSEB with tabulated data and/or satellite/airborne imagery.


Download the project to your local system, enter the download directory and then type

python setup.py install

if you want to install pyTSEB and its low-level modules in your Python distribution.

The following Python libraries will be required:

  • Numpy
  • Pandas
  • pyPro4Sail, at [https://github.com/hectornieto/pyPro4Sail]
  • GDAL, for running TSEB over an image
  • pandas
  • netCDF4
  • bokeh

With conda, you can create a complete environment with conda env create -f environment.yml

Code Example

High-level example

The easiest way to get a feeling of TSEB and its configuration is through the provided ipython/jupyter notebooks. In a terminal shell, navigate to your working folder and type

  • jupyter notebook ProcessPointTimeSeries.ipynb

    for configuring and running TSEB over a time series of tabulated data

  • jupyter notebook ProcessLocalImage.ipynb

    for configuring and running TSEB over an image/scene using local meteorological data

In addition, you can also run TSEB with the scripts TSEB_local_image_main.py and TSEB_point_time_series_main.py, which will read an input configuration file (defaults are Config_LocalImage.txt and Config_PointTimeSeries.txt respectively). You can edit these configuration files or make a copy to fit your data and site characteristics and either run any of these two scripts in a Python GUI or in a terminal shell:

  • python TSEB_local_image_main.py <configuration file>

    where \ points to a customized configuration file... leave it blank if you want to use the default file Config_LocalImage.txt

  • python TSEB_point_time_series.py <configuration file>

    where \ points to a customized configuration file... leave it blank if you want to use the default file Config_PointTimeSeries.txt

Low-level example

You can run any TSEB model or any related process in python by importing the module TSEB from the pyTSEB package. It will also import the ancillary modules (resitances.py as res, netRadiation as rad, MOsimilarity.py as MO, ClumpingIndex.py as CI and meteoUtils.py as met)

python import pyTSEB.TSEB as TSEB output=TSEB.TSEB_PT(Tr_K, vza, Ta_K, u, ea, p, Sdn_dir, Sdn_dif, fvis, fnir, sza, Lsky, LAI, hc, emisVeg, emisGrd, spectraVeg, spectraGrd, z_0M, d_0, zu, zt)

You can type help(TSEB.TSEB_PT) to understand better the inputs needed and the outputs returned

The direct and difuse shortwave radiation (Sdn_dir, Sdn_dif, fvis, fnir) and the downwelling longwave radiation (Lsky) can be estimated by

python emisAtm = TSEB.rad.calc_emiss_atm(ea,Ta_K_1) # Estimate atmospheric emissivity from vapour pressure (mb) and air Temperature (K) Lsky = emisAtm * TSEB.met.calc_stephan_boltzmann(Ta_K_1) # in W m-2 difvis,difnir, fvis,fnir=TSEB.rad.calc_difuse_ratio(Sdn,sza,press=p, Wv=1) # fraction of difuse and PAR/NIR radiation from shortwave irradiance (W m-2, solar zenith angle, atmospheric pressure and precipitable water vapour ) Skyl=difvis*fvis+difnir*fnir # broadband difuse fraction Sdn_dir=Sdn*(1.0-Skyl) Sdn_dif=Sdn*Skyl

Basic Contents

High-level modules

  • .pyTSEB/pyTSEB.py, class object for TSEB scripting

  • ProcessPointTimeSeries.ipynb and ProcessLocalImage.ipynb notebooks for using TSEB and configuring TSEB through a Graphical User Interface, GUI

  • TSEB_local_image_main.py and TSEB_point_time_series.py, high level scripts for running TSEB through a configuration file (Config_LocalImage.txt or Config_PointTimeSeries.txt)

Low-level modules

The low-level modules in this project are aimed at providing customisation and more flexibility in running TSEB. The following modules are included

  • .pyTSEB/TSEB.py

    core functions for running different TSEB models (TSEB_PT (*args,**kwargs), TSEB_2T(*args,**kwargs), DTD (*args,**kwargs)), or a One Source Energy Balance model (OSEB(*args,**kwargs)).

  • .pyTSEB/net_radiation.py

    functions for estimating net radiation and its partitioning between soil and canopy

  • .pyTSEB/resistances.py

    functions for estimating the different resistances for momemtum and heat transport and surface roughness

  • .pyTSEB/MO_similarity.py

    functions for computing adiabatic corrections for heat and momentum transport, Monin-Obukhov length, friction velocity and wind profiles

  • .pyTSEB/clumping_index.py

    functions for estimating the canopy clumping index and get effective values of Leaf Area Index

  • .pyTSEB/meteo_utils.py

    functions for estimating meteorolgical-related variables such as density of air, heat capacity of air or latent heat of vaporization.

API Reference


Main Scientific References

  • Norman, J. M., Kustas, W. P., Prueger, J. H., and Diak, G. R.: Surface flux estimation using radiometric temperature: a dual-temperature-difference method to minimize measurement errors, Water Resour. Res., 36, 2263, doi: 10.1029/2000WR900033, 2000
  • Norman, J., Kustas, W., and Humes, K.: A two-source approach for estimating soil and vegetation fluxes from observations of directional radiometric surface temperature, Agr. Forest Meteorol., 77, 263–293, doi: 10.1016/0168-1923(95)02265-Y, 1995
  • Kustas, W. P. and Norman, J. M.: A two-source approach for estimating turbulent fluxes using multiple angle thermal infrared observations, Water Resour. Res., 33, 1495–1508, 199
  • Kustas, W. P. and Norman, J. M.: Evaluation of soil and vegetation heat flux prediction using a simple two-source model with radiometric temperatures for partial canopy cover, Agr. Forest Meteorol., 94, 13–29, 199
  • Guzinski, R., Nieto, H., Stisen, S., and Fensholt, R.: Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment, Hydrol. Earth Syst. Sci., 19, 2017-2036, doi:10.5194/hess-19-2017-2015, 2015.
  • William P. Kustas, Hector Nieto, Laura Morillas, Martha C. Anderson, Joseph G. Alfieri, Lawrence E. Hipps, Luis Villagarcía, Francisco Domingo, Monica Garcia: Revisiting the paper “Using radiometric surface temperature for surface energy flux estimation in Mediterranean drylands from a two-source perspective”, Remote Sensing of Environment, In Press. doi:10.1016/j.rse.2016.07.024.


The folder ./Input contains examples for running TSEB in a tabulated time series (ExampleTableInput.txt) and in an image (ExampleImage_\< variable >.tif). Just run the high-level scripts with the configuration files provided by default and compare the resulting outputs with the files stored in ./Output/



pyTSEB: a Python Two Source Energy Balance Model

Copyright 2016 Hector Nieto and contributors.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.


Can I use the ProcessLocalImage.ipynb to derive ET of urban scenes based on UAV images?

opened on 2023-03-18 09:56:09 by HazelSCUT

Dear colleagues,

Recently, I have tried to use ProcessLocalImage.ipynb to process my urban data that were derived from UAV. I used the model of Priestley Taylor. However, the results seems to have many abnormal values (i.e. NaN): The black pixels of the following pictures show NaN, and such areas are almost trees. I don't know whether can I use this method to derive the spatial ET of urban scenes.

sensible heat flux latent heat flux soil heat flux net radiation test area


Improvement to give tkinter file dialog focus

opened on 2021-06-09 09:02:11 by OshinoMoumou

Greetings! I noticed that method _setup_tkinter referred a StackOverflow post, while a recent comment to it advised using root.attributes('-alpha', 0.3) to make the window invisible. I'm trying to learn the usefulness of such small updates on StackOverflow. Would this comment help improve your code? I understand that such improvement might not be helpful in real life situation. In that case, do you think this comment can help prevent future bugs (for example, when the code were reused somewhere else)? I'll really appreciate it if you could kindly give me some feedback or suggestions. Thank you very much for your time. Have a nice day!


ET4FAO - ReadTheDocs 2021-05-14 11:37:27

Updated docs sources for updated ReadTheDocs site

ET4FAO 2021-04-08 06:51:40

Version from which ESA ET4FAO code is based on

SENET 2019-08-22 09:52:03

  • Version from which ESA SENET software is based on
  • New module for computing potential ET

2018-02-12 18:09:07

Updated readthedocs

v1.4 2017-09-28 12:39:50

Changes from version 1.3

Mostly refactoring but also bug fixes and some new functions to bring the code in line with the development version.

Different LAI/F values for windspeed attenuation and canopy boundary layer resistance 2016-05-11 10:20:02

Changes from version 1.2

  • Included a 'Run PyTSEB' button to avoid some user confusion in the jupyter notebook.
  • Fixed indexing bug for LAI in the net sw radiation calculation.
  • Used different LAI/F values for windspeed attenuation and canopy boundary layer resistance
  • Removed windspeed indexing in Rx and Rs calculation
  • Use local LAI (F) instead of LAI in calculation of omega0
Héctor Nieto
GitHub Repository

source-energy-balance canopy soil radiometric-temperatures heat satellite-imagery evapotranspiration