MAGO Senet

MAGO senet is a tool that allows users to assess actual evapotranspiration using multispectral Sentinel-2 data and thermal Sentinel-3 data.


End Users

This solution is intended for water managers, irrigation communities, farmers, and scientists focused on the fast and accurate estimation of actual evapotranspiration in plot level, thus the water needs of the plants. For the use of the software a user must be familiar with Python and to know how to install a library in Python.

Solution Overview

MAGO senet package is an open source Python toolbox for the estimation of actual evapotranspiration for Copernicus Sentinel-2 and Sentinel-3 data. The package utilizes energy balance models such as TSEB-PT for the crop water demand assessment. The software is based on the ESA-SNAP Sen-ET plugin and most functionalities depend on ESA-SNAP software. Four types of data are required, (1) Sentinel-2 L2A images, (2) Sentinel-3 SLSTR thermal images, (3) ERA-5 Land reanalysis data, (4) ESA-CCI land cover data and (5) SRTM DEM data. These data are retrieved automatically by the software. Users can install the software using the analytical instructions in the project's Gitlab repository and use the software in Jupyter notebooks or by simple custom Python scripts. Along with the instructions a number of examples (as notebooks) and wikis were implemented for help of the users.

Key innovation

MAGO senet is free and open source and enables for the users the ability for bulk processing of large streams of satellite data for the accurate estimation of actual evapotranspiration. It is designed to work on MAGO CreoDIAS environment, but can as well work in any kind of Linux system without the automated data retrieval.

Key Features

The MAGO senet Python package is created to work with data from Copernicus Sentinel-2 and Sentinel-3, along with ERA-5 Land reanalysis, ESA-CCI land cover, and SRTM DEM. It estimates actual evapotranspiration without the need of the user to set specific parameters. The process is simple: users provide an area of interest (e.g a shapefile) and a start and end date for the satellite data. After retrieving the data, the package processes Sentinel-2 images to produce biophysical parameters (LAI, Chl, FaPAR, etc.) and structural parameters like vegetation height using ESA-CCI land cover, fractional vegetation cover, and more. The analysis of Sentinel-3 includes sharpening thermal images with the Data Mining Sharpener, along with Sentinel-2 reflectance, high-resolution DEM and the cloud mask. The result is a 20-meter representation of LST used as input for the land-surface energy flux model. Finally, meteorological data from the ERA-5 reanalysis dataset is downloaded and analyzed to determine daily energy fluxes and estimate evapotranspiration.

Case Study

As part of the MAGO project, the MAGO senet has been tested in Tunisia and Jordan. 

Technology Stack and Methodology

The software is implemented for optimal performance within the CreoDIAS environment, but can be deployed on various Linux systems as well. It's important to note that the modules dedicated to retrieving Copernicus Sentinel-2 and Sentinel-3 data are specifically configured for CreoDIAS. However, the other features seamlessly operate on any Linux system.

Collaboration and Partnerships

The solution has been developed by UTH-NTUA and tested in Jordan and Tunisia with the collaboration of different partners such as CETAQUA and INRGREF.

Visuals and Demonstrations

In the image below a map of the actual ET of Cap Bon in Tunisia is presented. This map was produced during the MAGO project and presents the average evapotranspiration of the years 2018-2022 for April. 


Alekos Falagas, Geospatial Software Developer|Remote Sensing Specialist @NTUA

Research Publications

 Ongoing work

Open Code, Access and Licensing: The MAGO senet is free and open source and is available under GNU GENERAL PUBLIC LICENSE Version 3.