tmart 2.5.2

Modelling and correcting for the adjacency effect in aquatic remote sensing

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

🔗 List of publications

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 [e-mail]