An advanced coverage estimation method to quantify biological soil crust coverage using Sentinel-2 imagery in desert and sandy land of China

ABSTRACT

Monitoring the distribution and area change of biological soil crusts (BSCs) can enhance our understanding of the interactions between nonvascular plants and the environment in drylands. However, using only pixel-based binary classification methods results in large-area estimation errors at large scales. The lack of available calculation methods for directly measuring BSC coverage using multispectral satellite images makes it challenging to obtain BSC area data for further studies at large scales. To address these issues, this study developed feature space conceptual models for desert and sandy land based on the characteristics of BSC in drylands. The desert feature space comprised the normalized difference vegetation index (NDVI) combined with the brightness index (BI), encompassing moss, lichen, and non-BSC. The sandy land feature space relied on the biological soil crust index (BSCI) and the NDVI, including vegetation, mixed BSCs and sandy soil. Using Sentinel-2 satellite imagery and a spectral unmixing model, the abundance of BSCs was quantified in four BSC growth areas located in the Gurbantunggut Desert and Mu Us Sandy Land of China. Validation of the method indicated that the root mean square error (RMSE) of the BSC coverage estimation results was 10% and 8% in desert and sandy land, respectively (estimation accuracies of 79% and 81%, respectively). This demonstrated that the proposed method can effectively estimate BSC coverage at a subpixel scale. The resulting BSC coverage data can provide the possibility to evaluate the functions of regional ecosystems.

KEYWORDS:

• Biological soil crust
• BSC coverage
• feature space
• spectral unmixing model

Acknowledgments

This research was financially supported by the National Natural Scientific Foundation of China (Grant No: 41991232), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA19050504-01) and the National Key Research and Development Plan of China (Grant No: 2016YFC0500201).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data available on request from the authors.

Additional information

Funding

This work was supported by the National Natural Scientific Foundation of China [41991232].