I'm working with drone images which separated to red, green and blue bands. My task is to determine bare soil pixels. I've tried to swap bands in the composite, and calculate different differences between pixel values, but It wasn't successful.

I'm asking that is there any spectral index which working only with the 3 visible bands?

  • 1
    Have you tried doing supervised classification? The result will lack, but it will be a start.
    – Erik
    Jul 1, 2022 at 7:59
  • @Erik Yes this is a reasonable approach, there is a plugin in QGIS that facilitates the process
    – Kartograaf
    Jul 1, 2022 at 15:17

2 Answers 2


I found mention of a RGBVI in Lussem, Ulrike & Hollberg, J. & Menne, Jannis & Schellberg, Jürgen & Bareth, G.. (2017). USING CALIBRATED RGB IMAGERY FROM LOW-COST UAVS FOR GRASSLAND MONITORING: CASE STUDY AT THE RENGEN GRASSLAND EXPERIMENT (RGE), GERMANY. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLII-2/W6. 229-233. 10.5194/isprs-archives-XLII-2-W6-229-2017.

They give the formula for a RGBVI from Bending et al, Bendig, J., Yu, K., Aasen, H., Bolten, A., Bennertz, S., Broscheit, J., Gnyp, M.L., and Bareth, G., 2015. Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. International Journal of Applied Earth Observation and Geoinformation, 39, pp. 79-87. doi: 10.1016/j.jag.2015.02.012.

Unfortunately I cannot really comprehend the formula given by Lussem et. al. because they say "reflectance %" without more explanation and without code and I don't have access to Bending et. al. because it's not open access.


There are a bunch of RGB derived indices in table 1 of this manuscript. The titular index (TGI) has shown interesting results for vegetation classificaiton in my experience.

The research paper is Hunt, E. Raymond Jr.; Doraiswamy, Paul C.; McMurtrey, James E.; Daughtry, Craig S.T.; Perry, Eileen M.; and Akhmedov, Bakhyt, "A visible band index for remote sensing leaf chlorophyll content at the canopy scale" (2013). Publications from USDA-ARS / UNL Faculty. 1156. https://digitalcommons.unl.edu/usdaarsfacpub/1156

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