SMETool stands for Soil Moisture Estimation Tool. It is a complete open source and web based for soil moisture estimation platform using Machine Learning methods and EO-Learn framework. It is a generic pipeline for soil moisture estimation using earth observation data (Sentinel products).
- It combine ML techniques, EO data and Eo-Learn library.
- The main workflow has main data download and processing functions using EO-Learn library.
- It is easily customizable and extensible thanks to a high number of adds-on and web services to integrate.
- It offers mass import tools to help you being even more efficient soil moisture estimation task.
- It allows the user to access the web application through a web interface and use the map viewer to select any location in the study area in order to make queries about the geographic database.
- SMETool data flow  The requests sent and received by the user establish a link with several197processes that are executed in the background without the intervention of198the user (steps 1 and 5). The services executed in the background are the operations applied to process the satellite images preprocessing, the ML training models and the storage data in a geospatial database (steps 2,3 and 4)
- SMETool Structure The SMETool service is composed of six main components illustrated as follows 
- Home interface The SMETool home interface, as presented follows 
- Input data interface The SMETool input selection interface is shown as follows 
- Results visualization interface The SMETool results, which naturally includes all previous information, can be further accessed by results visualisation interface 
The pipeline python file that uses the eolearn packages can be found at : https://github.com/jarray01/SMETool/tree/main/Complete%20pipeline%20script
To see list of available web services, see : https://github.com/jarray01/SMETool/blob/main/src/main/java/tn/noureddine/controller/Shapecontroller.java