Introduction
SenSE is a comprehensive community framework designed for radiative transfer (RT) modeling in the active microwave domain. It summarizes various RT models developed for synthetic aperture radar (SAR) to simulate backscatter responses from open soil and vegetated land surfaces, primarily in agricultural settings. This integration encompasses different models for scattering and emission across various surfaces, providing a cohesive operational structure.
One of the framework’s most significant advantages is its modular design, which allows for the easy substitution and analysis of different surface and canopy scattering models within a single system. This flexibility facilitates seamless model exchange, enhancing the framework’s adaptability and utility. The SenSE package currently includes several surface models such as Oh92 [1], Oh04 [2], Dubois95 [3], IEM [4], and the surface component of the Water Cloud Model (WCM) [5]. For canopy modeling, it supports models like SSRT [6] [7] and WCM [5].
Additionally, the framework incorporates the dielectric mixing model by Dobson et al. [8], available in various versions for converting soil moisture content to a dielectric constant. SenSE also includes essential utility functions, such as those for frequency-wavelength conversion and calculating Fresnel reflectivity coefficients, further enhancing its analytical capabilities.
Statement of need
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).
Getting Started
Please find instructions on how to download and install SenSE in the Installation section.
Support, contributing and testing
Please contribute using Github Flow. Create a branch, add commits, and open a pull request.
Reporting bugs
If you find a bug in SenSE, please open an new issue and tag it “bug”.
Suggesting enhancements
If you want to suggest a new feature or an improvement of a current feature, you can submit this on the issue tracker and tag it “enhancement”. Or you can use the discussions.
Testing
The package is currently tested for Python >= 3.10 on Unix-like systems. To run unit tests, execute the following line from the root of the repository:
pytest
References
Y. Oh, K. Sarabandi, and F.T. Ulaby. An empirical model and an inversion technique for radar scattering from bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 30(2):370–381, March 1992. doi:10.1109/36.134086.
Yisok Oh. Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces. IEEE Transactions on Geoscience and Remote Sensing, 42(3):596–601, March 2004. doi:10.1109/TGRS.2003.821065.
P.C. Dubois, J. van Zyl, and T. Engman. Measuring soil moisture with imaging radars. IEEE Transactions on Geoscience and Remote Sensing, 33(4):915–926, July 1995. Conference Name: IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/36.406677.
A.K. Fung, Z. Li, and K.S. Chen. Backscattering from a randomly rough dielectric surface. IEEE Transactions on Geoscience and Remote Sensing, 30(2):356–369, March 1992. URL: http://ieeexplore.ieee.org/document/134085/ (visited on 2019-10-22), doi:10.1109/36.134085.
E. P. W. Attema and Fawwaz T. Ulaby. Vegetation modeled as a water cloud. Radio Science, 13(2):357–364, March 1978. doi:10.1029/RS013i002p00357.
R.D. de Roo, Yang Du, F.T. Ulaby, and M.C. Dobson. A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion. IEEE Transactions on Geoscience and Remote Sensing, 39(4):864–872, April 2001. Conference Name: IEEE Transactions on Geoscience and Remote Sensing. doi:10.1109/36.917912.
Fawwaz Tayssir Ulaby and David G. Long. Microwave radar and radiometric remote sensing. University of Michigan press, Ann Arbor, 2014. ISBN 978-0-472-11935-6.
Myron C. Dobson, Fawwaz T. Ulaby, Martti T. Hallikainen, and Mohamed A. El-rayes. Microwave Dielectric Behavior of Wet Soil-Part II: Dielectric Mixing Models. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1):35–46, January 1985. doi:10.1109/TGRS.1985.289498.