Mapping bedrock with vegetation spectral features using time series Sentinel-2 images

Abstract

Vegetation hinders the acquisition of bedrock spectra and makes it difficult to classify bedrock with remote sensing data. Previous studies indicated bedrock can control vegetation growth through soluble nutrients and water-holding capacity. However, the potential of using vegetation spectral features to map bedrock has been rarely explored. This study first derived reflectance and vegetation indices from time series Sentinel-2 products, then did a spatiotemporal analysis of vegetation spectral features on different bedrock, and finally combined vegetation features and random forest classifiers to map bedrock. The results demonstrated that (a) the close relationship between vegetation growth and bedrock can be captured by Sentinel-2 images; (b) both VIs’ combination and reflectance derived from the growing season can achieve reasonably classified maps, with classification accuracies of 70.06% and 73.46%, respectively; (c) NDVI was more sensitive to the bedrock than other VIs. Overall, vegetation spectral features showed great potential to map bedrock underneath vegetation.