author	address
Cristian Iranzo	Universidad de Zaragoza

GEOSAT Image Processing

Apply atmospheric corrections to GEOSAT images. The code is adaptable to all types of images, but it has been developed to work with GEOSAT ones and with the following characteristics:

• PM4 or Bundle: panchromatic image (1m GSD), and the four bands of the multispectral image (3m GSD).
• Processing level L1C (Ortho): Calibrated and radiometrically corrected scaled ToA radiance, orthorectified and resampled.

Band	Min $\lambda$ (nm)	Max $\lambda$ (nm)	GSD (m)
PAN	560	900	0.75
BLUE	466	525	3
GREEN	532	599	3
RED	640	697	3
NIR	770	892	3

Important: The directory containing the image(s) must maintain the original structure and names of the tif and dim files:

DE2_PM4_L1C_000000_20210629T102606_20210629T102608_DE2_38083_838B
|_ DE2_MS4*.dim
|_ DE2_MS4*.tif
|_ DE2_PAN*.dim
|_ DE2_PAN*.tif

Installation

The code was executed using miniconda within the following environment:

conda create -n geosat_atmcorr python py6s earthengine-api gdal -c conda-forge

The py6s and earthengine-api modules are specific to the 6S correction.

Once miniconda (or anaconda) is installed, download the two python scripts into the same folder:

• atmcorr_utils.py: Classes and functions to apply the correction.
• geosat_atmcorr.py: Code that generates the corrections.

It has been tested with the following versions:

earthengine-api=0.1.401 (pyhd8ed1ab_0)
gdal=3.9.0 (py312hea5013e_2)
py6s=1.9.2 (pyhd8ed1ab_0)
python=3.12.3 (h2628c8c_0_cpython)

Unzipping files

The GEOSAT images are downloaded in zip folders. To unzip them, execute the following code in python:

import os
import zipfile

main_folder = r"F:\GEOSAT\2022"

def unzip(folder):
    """Unzip folders"""
    if folder.endswith('.zip'):
        # Select the location to unzip the folder content
        unzip_folder_name = os.path.basename(folder).split('.')[0]
        unzip_folder = os.path.join(os.path.dirname(folder), unzip_folder_name)
        if not os.path.exists(unzip_folder):
            # Unzip
            print(f'Extracting {unzip_folder_name}...')
            with zipfile.ZipFile(folder, 'r') as zip_ref:
                zip_ref.extractall(unzip_folder)

# List to save folders with errors in the unzipping process
exceptions = []
for folder in os.listdir(main_folder):
    try:
        unzip(os.path.join(main_folder, folder))
    except Exception as ex:
        exceptions.append(folder)
        print(ex)
    
print('The following folders were badly unzipped:\n', exceptions)

Applying the correction

Use the following commands:

• folder

  Directory of the GEOSAT images to correct.

• folders

  Main directory containing the folders of the GEOSAT images. If this parameter is included instead of folder, a loop correction of all the images is executed.

  • It allows for other files that are not image directories, which will be ignored.

• atm_key

  Atmospheric correction code to use. Choose from DOS, COST, and 6S.

  Important: All corrections will require an image in radiance levels, i.e., the resulting product from applying the ARC transformation. To apply these, the previous transformation must be executed first, which will create a file with the prefix <original_imgname>_ARC.tif in the folder containing the images.

  python D:\geosat\geosat_atmcorrection.py -folder=D:\geosat\DE2_* -atm_key=ARC
  

• geecloud_id

  If the 6S correction is applied, this command should be used to include the Google Cloud project ID for executing GEE API functions.

  Note: You must previously include the earthengine authenticate command in the conda console and sign in with the Google account associated with the project ID.

After the correction, a .log file is generated with the applied values and the formulas corresponding to the corrections for each band.

Important: All paths must be absolute (starting from the disk drive), unless the python scripts with the functions are located in the conda environment folder.

Important: If the image to be corrected already exists, it will not be overwritten, and the next one will be processed.

Note: The code must be run within the environment containing the required packages. In the example provided:

conda activate geosat_atmcorr

Note: The Google Cloud account ID can be found in the Google Cloud Console.

Examples

python D:\geosat\geosat_atmcorrection.py -folders=D:\geosat\data -atm_key=6S -geecloud_id=id

Pending Tasks

• Functionality to execute 6S with parameters extracted from ground weather stations (without the need for EearthEngine API).
• Consolidate all functionalities into an executable. Use tools like PyInstaller.

References

Chavez, P. S. & others. (1996). Image-based atmospheric corrections-revisited and improved. Photogrammetric Engineering and Remote Sensing, 62(9), 1025–1035.

Fernández, C., de Castro, C., Garcı́a, L., Calleja, M. E., Niño, R., Fraile, S., & Sousa, R. (2023). Evaluación del impacto de la superresolución sobre imágenes multiespectrales GEOSAT-2. Revista de Teledetección, 61, 83–96.

Fernández, C., De Castro, C., Calleja, M. E., Sousa, R., Niño, R., García, L., Fraile, S., & Molina, I. (2023). GEOSAT 2 Atmospherically Corrected Images: Algorithm Validation. ECRS 2023, 64. https://doi.org/10.3390/ECRS2023-16296

Mahiny, A. S., & Turner, B. J. (2007). A comparison of four common atmospheric correction methods. Photogrammetric Engineering & Remote Sensing, 73(4), 361–368.

Murphy, S. & Hård, J. gee-atmcorr-S2 repository

Thuillier, G., Hersé, M., Labs, D., Foujols, T., Peetermans, W., Gillotay, D., Simon, P., & Mandel, H. (2003). The solar spectral irradiance from 200 to 2400 nm as measured by the SOLSPEC spectrometer from the ATLAS and EURECA missions. Solar Physics, 214, 1–22.

Vermote, E. F., Tanré, D., Deuze, J. L., Herman, M., & Morcette, J.-J. (1997). Second simulation of the satellite signal in the solar spectrum, 6S: An overview. IEEE Transactions on Geoscience and Remote Sensing, 35(3), 675–686.

Wilson, R. T. (2013). Py6S: A Python interface to the 6S radiative transfer model. Computers & Geosciences, 51, 166–171.