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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 install

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

The following Python libraries will be required:

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 and, 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 <configuration file>

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

  • python <configuration file>

where <configuration file> 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 ( as res, netRadiation as rad, as MO, as CI and as met)

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

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

Basic Contents

High-level modules

  • .pyTSEB/, class object for TSEB scripting

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

  • and, 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/

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/

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

  • .pyTSEB/

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

  • .pyTSEB/

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

  • .pyTSEB/

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

  • .pyTSEB/

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