Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/hectornieto/pyTSEB

A python Two Source Energy Balance model for estimation of evapotranspiration with remote sensing data
https://github.com/hectornieto/pyTSEB

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

Last synced: about 2 months ago
JSON representation

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

Awesome Lists containing this project

README 

        
# PyTSEB



## Synopsis



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.



## Installation



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



`pip 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 `

> 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 `

> 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

http://pytseb.readthedocs.org/en/latest/index.html



## 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.



## Tests

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/*



## Contributors

- **Hector Nieto**   main developer

- **Radoslaw Guzinski** main developer, tester

- **William P. Kustas** TSEB modeling, tester 

- **Ana Andreu** tester



## License

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 .