DMS Bias Correction

Synopsis

This project contains the Python code for enhancing the dynamic LST range of sharpened LST scenes, by fusing them with Landsat LST imagery.

The project consists of:

1. a lower-level module dms_bias_correction.scale_lst.py with the basic functions needed for the bias correction approach
2. a higher-level module dms_bias_correction.landsat_collection_2_helper.pyfor easily running the correction by reading Landsat Collection 2 Level 2

Installation

Download the project to your local system, enter the download directory and then type

python setup.py install

if you want to install pyTSEB and its low-level modules in your Python distribution.

The following Python libraries will be required:

• Numpy
• Scipy
• scikit-image
• GDAL
• pyDMS, at https://github.com/radosuav/pyDMS

With conda, you can create a complete environment with conda env create -f environment.yml

Code Example

High-level example

The easiest way to get a feeling of the sharpening enhancement is throught the example script testdmscorrection In a terminal shell, navigate to your working folder and type

• python test_dms_correction.py

The script will read all the Landsat images and compute the reference dynamic range that will be used to correct the sharpened S3 LST image. Then it will compare the corrected and uncorrected DMS image to a Landsat scene acquired on the same date and plot the results in test/dmscorrbest_case

Low-level example

See documentation in dmsbiascorrection.scale_lst.py for details about using the low-level code

Main Scientific References

When using this sofware please cite the following refrences:

• R. Guzinski, H. Nieto, R. Ramo-Sánchez, J.M. Sánchez, I. Joma, R. Zitouna-Chebbi, O. Roupsard, R. and López-Urrea, Improving field-scale crop actual evapotranspiration monitoring with Sentinel-3, Sentinel-2, and Landsat data fusion (2023) International Journal of Applied Earth Observation and Geoinformation", volume 125, art. No. 103587, doi :10.1016/j.jag.2023.103587
• J. M. Sánchez, J. M. Galve, H. Nieto and R. Guzinski, Assessment of High-Resolution LST Derived From the Synergy of Sentinel-2 and Sentinel-3 in Agricultural Areas, (2024) IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 17, pp. 916-928, 2024, doi: 10.1109/JSTARS.2023.3335896.

Tests

The folder ./test contains an example for running the correction of a DMS sharpened image, at test/dms_raw, using a time series of downloaded Landsat Collection 2 Level 2 scenes, located at test/landsat. The output will be stored at test/dmscorrbest_case and could be compared to the actual LST Landsat scene of the same date in test/landsat_reference

Contributors

• Hector Nieto (hector.nieto@ica.csic.es, hector.nieto.solana@gmail.com) main developer
• Radoslaw Guzinski main developer, tester

License

dms-bias-correction: enhancing the dynamic LST range of sharpened LST scenes

Copyright 2023 Hector Nieto and contributors.

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.