SenSE is a generic community framework for radiative transfer (RT) modelling in the active microwave domain. It implements different existing models for scattering and emission for different surfaces in a coherent framework to simulate SAR backscattering coefficients as function of surface biogeophysical parameters. In the microwave domain the surface and canopy contribution of the total backscatter is usually estimated separately. Within the SenSE framework different model combination of surface and canopy models can be easily brought together and moreover analyzed. The analysis of the different model combination within one framework can be seen as the biggest advantage of the developed SenSE package. Currently implemented surface models are: Oh1992, Oh2004, Dubois95 and IEM and Water Cloud. Currently implemented canopy models are: SSRT and Water Cloud.
Over the last several decades, various (empirical to physically based) RT models in the active microwave domain have been developed, tested, and further modified. However, an easy-to-use framework combining the most common microwave RT models (simulating backscatter responses of active microwave sensors) is lacking. Thus, every researcher must produce their own code implementation from the original source. This Python framework aims to serve as a first attempt to combine the most common active microwave-related RT models in a modular way. As a result, surface and volume scattering models can be easily exchanged with one another. Such a modular framework provides an opportunity to easily plug and play with different RT model combinations for various research questions and use cases. SenSE facilitates the application of RT models, especially for comparative analysis. Over time, the framework is expected to grow, incorporating more RT models (e.g., passive microwave domain) and supplementary functions (e.g., more dielectric mixing models).
Download and install Anaconda or Miniconda. Anaconda/Miniconda installation instructions can be found here
To install all required modules, use:
git clone https://github.com/McWhity/sense.git
cd sense
conda env create --prefix ./env --file environment.yml
conda activate ./env # activate the environment
To install SenSE into an existing Python environment, use:
python -m pip install .
To install for development, use:
python -m pip install --editable .
Install system requirements:
sudo apt install python3-pip python3-tk python3-virtualenv python3-venv virtualenv
Create a virtual environment:
git clone https://github.com/McWhity/sense.git
cd sense
virtualenv -p /usr/bin/python3 env
source env/bin/activate # activate the environment
pip install --upgrade pip setuptools # update pip and setuptools
To install SenSE into an existing Python environment, use:
python -m pip install .
To install for development, use:
python -m pip install --editable .
SenSE can also be run using Docker. To build it locally use :
git clone https://github.com/McWhity/sense.git
cd sense
docker build -t sense:latest .
For usage checkout the juypter notebook
We use Sphinx to
generate the documentation of SenSE on
ReadTheDocs.
- Alexander Löw (✝ 2 July 2017)
- Thomas Weiß <"[email protected]">
This project is licensed under the GPLv3 License - see the LICENSE.rst file for details.