Investigating Fractional Vegetation Cover (fcover) from Sentinel-2 as Input for Bare Surface Composites

Keywords: Sentinel-2, EnMAP, Fractional Vegetation Cover, Temporal Composites, Bare surface

The creation of reliable bare surface composites from Sentinel-2 (S2) data presents a significant challenge due to the variability of surface conditions and vegetation dynamics. This study aims to develop a novel approach for generating such composites by leveraging fractional vegetation cover (fCover) maps derived from a neural network trained on paired S2 and EnMAP observations. This approach is compared to traditional methods that rely on spectral indices alone. Such composites hold significant potential for various downstream applications, including the development of digital soil property models.

Bare surface composites from temporally stacked data can be generated through various methodologies. A previous approach (Heiden et al., 2022) relied on rule-based classification using spectral indices to separate multispectral pixels into different surface classes. In contrast, this study proposes a machine learning-driven method leveraging paired Sentinel-2 (S2) and EnMAP observations acquired within close temporal proximity.

Fractional vegetation cover (fCover) maps are first derived from EnMAP scenes using the EnMAP fCover processor (developed by Martin Bachmann, David Marshall and Kevin Kühl, DLR). These fCover maps serve as labelled training data for a neural network, which learns to predict fCover directly from Sentinel-2 spectra. The trained network is then applied to the full temporal stack of Sentinel-2 data, providing pixel-wise probabilities of bare surface conditions. Finally, these probabilities are aggregated to create a bare surface composite.

The neural network demonstrated convergence, with training and validation losses stabilizing at 0.015 and 0.021 mean squared error (MSE), respectively, after 5 epochs and 24 hours of training. The resulting composites, derived from predicted vegetation fractions, closely resemble those produced using traditional spectral indices. Further analysis will include explaining the network's decision-making process using SHAP and a detailed comparison of the composites generated by both methods. To enhance interpretability, auxiliary data such as the acquisition date of each contributing pixel is tracked. Additionally, uncertainty estimates for each band and pixel in the composites will be provided, offering insight into the reliability of the results.

Future work will integrate the generated bare surface composites with additional datasets, such as the soil map of Bavaria provided by the Landesamt für Umwelt (LfU). By combining these resources, we aim to establish correlations between the composite spectra and specific soil properties, like soil colour. This approach has the potential to enhance our understanding of surface-soil relationships and broaden the applicability of the composites for soil property modelling and environmental studies.