*3.3. Clustering Results—Image Data*

Multiple band combinations were tested as input for the performed image classification via k-means clustering. While all combinations have problems capturing the thin horizontal layer C, the results shown in Figure 7 indicate clear benefits when using specific bands: Using the RGB data, the most homogeneous classification results were derived from a combination of the raw RGB bands, two principle components, and two slope rasters (Figure 7b). The summarised groups produced by the cluster analysis roughly represent the stratigraphic (sub)layers. Clustering the same band combination of the multispectral data (Figure 7c) produced results of similar quality. Nevertheless, two major differences have to be pointed out: (1) Multispectral data succeed in separating layer A from the rest of the profile, and (2) RGB data capture layer C more precisely in the spatial domain. Layer C is grouped with materials from layer A in the results produced using RGB data, while the multispectral data clustering results group it together with parts of layers B and D (see Online Resource 1 (in Supplementary Materials) for all band combinations).

**Figure 7.** Results of the performed image classification of the upper part of SD17P1; stone layers were masked manually prior to processing. (**a**) Profile as perceived during fieldwork with layer borders (white) according to Figure 2 (layers A, B, C and D). Classification results of (**b**) an RGB composite (raw data, slopes, PCA results), (**c**) a multispectral composite (raw data, slopes, PCA results), (**d**) a multispectral composite (slopes, CIELAB), and (**e**) a multispectral composite (slopes, predicted SOC, CIELAB chromaticity coordinates a and b). Colour shades represent individual groups produced from k-means clustering, while summarised groups are indicated by a common hue.
