Investigating the accuracy of vegetation index-based models for estimating the fractional vegetation cover and the effects of varying soil backgrounds using in situ measurements and the PROSAIL model

ABSTRACT

Fractional vegetation cover (FVC) is an important variable for describing the quality and changes of vegetation in terrestrial ecosystems. The simplest and most widely used model for the estimation of FVC is the dimidiate pixel model. The normalized difference vegetation index (NDVI) is commonly used as a vegetation index (VI) in this model. A range of VIs is possible alternative to the use of NDVI in the dimidiate pixel model. In this article, six VI-based dimidiate pixel models were compared using in situ measurements and canopy reflectances simulated by the PROSAIL model over nine different soil backgrounds. A comparison with in situ measurements showed that the Gutman–Ignatov method overestimated FVC, with a mean root mean square error (RMSE) of 0.14. The mean RMSE had an intermediate value of 0.08 in the Carlson–Ripley method and was further reduced to 0.05 in the method proposed by Baret et al. The use of both modified soil-adjusted vegetation index (MSAVI) and a mixture of NDVI and the ratio vegetation index (RVI) to replace NDVI in the Gutman–Ignatov model reduced the RMSE to 0.06. The mean RMSE in the difference vegetation index (DVI)-based model was 0.08. The simulated results indicated that soil backgrounds have significant effects on these VI-based models. The sensitivity of the first three models and the NDVI plus RVI-based model to soil backgrounds decreased with an increase in soil reflectance. In contrast, the DVI-based model is sensitive to soil backgrounds with high reflectances. MSAVI, which is less sensitive to soil backgrounds, represents a feasible alternative to the use of NDVI in the Gutman–Ignatov model.

Acknowledgements

The authors wish to express their sincere appreciation for the critiques and suggestions from two anonymous reviewers that helped improve the quality of the article.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This study was supported by the National Natural Science Foundation of China under Grant [number 41501408]; Fundamental Research Funds for the Central Universities under Grant [number 2412016KJ024]; and China Postdoctoral Science Foundation under Grant [number 2016M590244].