Atmospheric and Glint Correction of Sentinel-2 Imagery for Marine and Coastal Machine Learning
By: Rachel Keay
Tags: Image Processing, Data Science, Sentinel 2, Marine, Machine Learning

Hedley et al sun glint correction is performed on the DOS and Sen2Cor images and requires pixels over deep water to calibrate the AC. To perform ACOLITE AC with glint correction the user setting glint_correction is set as True, glint_force_band is set to 1600 (short-wave infrared) and I experimented with the glint_mask_rhos_threshold (default 0.05). Observations of the glint corrected images and histograms above are as follows:

The DOS and Sen2Cor with Hedley glint correction removes some but not all sun glint as shown by the turquoise class in the deep-water locations.

The ACOLITE DSF with glint removal does a good job of removing glint over water, although some pixels appear so dark they are classed as the land class. There also appears better defined near-shore class clusters as shown by the turquoise and green class.

The ACOLITE DSF glint correction with a threshold of 0.05 (default) identified some cirrus cloud (shown in the north-west of the image that was missed from masking) and some bright shallow water areas as glint, as shown by the dark brown class.

The ACOLITE DSF glint correction with a threshold of 0.07 removed more glint in the deeper water and did not identify as much cirrus cloud nor shallow water as glint compared to threshold of 0.05.

The ACOLITE DSF glint correction with threshold 0.07 appears to outperform the other glint corrections. The ACOLITE DSF glint correction when set at a lower threshold (0.05) mistakenly identified some cirrus cloud for glint which had been missed from masking, which is useful information to improve the masking algorithm.

By assessing the impact of AC and glint correction algorithms on the structure of the image data this study can inform algorithmic decision-making for marine and coastal habitat mapping. Machine learning is data driven and the final model will only be as good or bad as the data that goes into is. Improvements to this work include comparison to other high regarded AC algorithms (e.g. C2RCC, Polymer) and statistical analysis. A robust statistical analysis of how effective AC would require ground truth Rrs values, collected using a hand-held or shipborne spectrometer, to compare and evaluate AC and glint corrected pixels to.

For any future ACOLITE users out there, I found a lot of useful information about ACOLITE in the forum: https://odnature.naturalsciences.be/remsem/acolite-forum/ and User Manual https://odnature.naturalsciences.be/downloads/remsem/acolite/acolite_manual_20190326.0.pdf

And a big thank you to Scottkaczor for contributing to this work!

References

[1] Warren, M., Simis, S., Martinez-Vicente, V., et al. (2019). Assessment of atmospheric correction algorithms for the Sentinel-2A MultiSpectral Imager over coastal and inland waters. Remote Sens. Environ. 225, 267–289.

[2] Mobley, C. (2020). The Atmospheric Correction Problem. https://oceanopticsbook.info/view/remote-sensing/the-atmospheric-correction-problem

[3] ESA. (2019). Sen2cor Configuration and User Manual. http://step.esa.int/thirdparties/sen2cor/2.8.0/docs/S2-PDGS-MPC-L2A-SUM-V2.8.pdf

[4] Chavez, P. S. (1988). An Improved Dark-Object Subtraction Technique for Atmospheric Scattering Correction of Multispectral Data. Remote Sensing of Environment, vol. 24, pp. 450–479.

[5] Royal Belgian Institute of Natural Sciences. (2020). OD Nature Remote Sensing and Ecosystem Modelling team. https://odnature.naturalsciences.be/remsem/software-and-data/acolite

[6] Hedley, J. D., Harborne, A. R., Mumby, P. J. (2005). Simple and robust removal of sun glint for mapping shallow-water benthos. Int. J. Remote Sens., 26 (10), pp. 2107–2112.

[7] Vanhellemont, Q. (2019). Adaptation of the dark spectrum fitting atmospheric correction for aquatic applications of the Landsat and Sentinel-2 archives. Remote Sensing of Environment 225, 175–192.