tmart 2.3.3

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 the radiative transfer in a 3D surface-atmosphere system through a Monte-Carlo approach. T-Mart features arbitrary surface models which allow simulating and correcting for the adjacency effect in aquatic remote sensing.

Links

Home page: https://github.com/yulunwu8/tmart

User guide: https://tmart-rtm.github.io

Publication

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

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

3 - Install tmart:

pip3 install tmart

Quick Start: Adjacency-Effect Correction

T-Mart supports adjacency-effect correction for Sentinel-2 MSI and Landsat 8/9 OLI/OLI-2 products. Correction is performed directly on level-1 products therefore can be followed by any amtospheric-correction tools. Minimal inputs are:

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'

### Multiprocessing needs to be wrapped in 'if __name__ == "__main__":' for Windows systems, this is optional for Mac OS
if __name__ == "__main__":
    tmart.AEC.run(file, username, password)

See Instruction - Adjacency-Effect Correction for more detailed instructions.

Quick Start: Adjacency-Effect Modelling

import tmart
import numpy as np
from Py6S.Params.atmosprofile import AtmosProfile

# Specify wavelength in nm
wl = 400

### DEM and reflectance ###
image_DEM = np.array([[0,0],[0,0]]) # in meters
image_reflectance = np.array([[0.1,0.1],[0.1,0.1]]) # unitless     
image_isWater = np.zeros(image_DEM.shape) # 1 is water, 0 is land

# Synthesize a surface object
my_surface = tmart.Surface(DEM = image_DEM,
                           reflectance = image_reflectance,
                           isWater = image_isWater,
                           cell_size = 10_000)  
                               
### Atmosphere ###
atm_profile = AtmosProfile.PredefinedType(AtmosProfile.MidlatitudeSummer) 
my_atm = tmart.Atmosphere(atm_profile, aot550 = 0, aerosol_type = 'Maritime')

### Create T-Mart Object ###
my_tmart = tmart.Tmart(Surface = my_surface, Atmosphere= my_atm, shadow=False)
my_tmart.set_geometry(sensor_coords=[51,50,130_000], 
                      target_pt_direction=[180,0],
                      sun_dir=[0,0])

### Multiprocessing needs to be wrapped in 'if __name__ == "__main__":' for Windows systems. 
### This can be skipped for Unix-based systems. 
if __name__ == "__main__":
    results = my_tmart.run(wl=wl, band=None, n_photon=10_000)
    
    # Calculate reflectances using recorded photon information 
    R = tmart.calc_ref(results)
    for k, v in R.items():
        print(k, '     ' , v)

Output should be similar to this:

========= Initiating T-Mart =========
Number of photons: 10000
Using 10 core(s)
Number of job(s): 100
Wavelength: 400
target_pt_direction: [180, 0]
sun_dir: [0, 0]
=====================================
Jobs remaining = 102
Jobs remaining = 72
Jobs remaining = 42
Jobs remaining = 12
=====================================
Calculating radiative properties...
R_atm       0.12760589889823587
R_dir       0.06046419017201067
R_env       0.012888590547129805
R_total       0.20095867961737635

See user guide for more detailed instructions.

Other

T-Mart can calculate reflectances of various units, see Table 1 in Wu et al. (2023) for examples.

For questions and suggestions (which I'm always open to!), please email Yulun at [e-mail]