T-Mart: Topography-adjusted Monte-carlo Adjacency-effect Radiative Transfer Code
Description
T-Mart solves radiative transfer in a 3D surface-atmosphere system. It supports customizable surface and atmosphere models and enables simulation and correction for the adjacency effect (AE) in optical aquatic remote sensing. AE correction substantially improves satellite-based retrieval of water-leaving reflectance in nearshore environments (Wu et al., 2024).
Links
Home page: https://github.com/yulunwu8/tmart
User guide: https://tmart-rtm.github.io
Installation
1 - Create a conda environment and activate it:
conda create --name tmart python=3.9
conda activate tmart
2 - Install dependencies:
conda install -c conda-forge Py6S rasterio==1.3.9
3 - Install tmart:
pip3 install tmart
Quick start: adjacency-effect correction
T-Mart supports AE correction for Sentinel-2 MSI and Landsat 8/9 OLI/OLI-2 products. Correction is performed directly on level-1 products and can be followed by any atmospheric correction tools.
Minimal input:
import tmart
file = 'user/test/S2A_MSIL1C_20160812T143752_N0204_R096_T20MKB_20160812T143749.SAFE'
# NASA EarthData Credentials, OB.DAAC Data Access needs to be approved
username = 'abcdef'
password = '123456'
# T-Mart uses multiprocessing, which must be wrapped in 'if __name__ == "__main__":' for Windows systems. This is optional for Unix-based systems
if __name__ == "__main__":
tmart.AEC.run(file, username, password)
The tool takes approximately 20 min to process a Landsat 8/9 scene and 30 min for a Sentinel-2 scene on an 8-core personal computer. See Instruction - Adjacency-Effect Correction for detailed instructions.
Video tutorials:
Publications
Primary references
Wu, Y., Knudby, A., & Lapen, D. (2023). Topography-adjusted Monte Carlo simulation of the adjacency effect in remote sensing of coastal and inland waters. Journal of Quantitative Spectroscopy and Radiative Transfer, 108589. https://doi.org/10.1016/j.jqsrt.2023.108589
Wu, Y., Knudby, A., Pahlevan, N., Lapen, D., & Zeng, C. (2024). Sensor-generic adjacency-effect correction for remote sensing of coastal and inland waters. Remote Sensing of Environment, 315, 114433. https://doi.org/10.1016/j.rse.2024.114433
Studies using T-Mart
Funding
T-Mart was funded by the Canadian Space Agency (Grant 22AO2-LIQU to Liquid Geomatics Ltd.) and Agriculture and Agri-Food Canada (Grants J-001839 and J-002305 to D.R. Lapen).
Others
For questions and suggestions (which I'm always open to!), please open an issue or email Yulun at yulunwu8@gmail.com
1 maintainer
Release files for tmart 2.6.5
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| tmart-2.6.5.tar.gz | 154.6 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| tmart-2.6.5-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 334.1 kB
Release files / tmart-2.6.5.tar.gz
| Download URL | tmart-2.6.5.tar.gz |
|---|---|
| Size | 154.6 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.9.23
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Release files / tmart-2.6.5-py3-none-any.whl
| Download URL | tmart-2.6.5-py3-none-any.whl |
|---|---|
| Size | 179.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
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No |
| Uploaded via |
twine/6.2.0 CPython/3.9.23
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