*4.3. Shortcomings and Benefits of Spectral Measurements*

Our study exposes that quantitative colour measurements during fieldwork can help with stratigraphic delineation and interpretation, be it based on selective or extensive data. Combining these measurements with semi-automated classification algorithms, we were able to increase objectivity and transparency of the delineation of stratigraphic (sub)layers in a similar manner to that if using common sedimentological data. In particular, depth-constrained hierarchical clustering should be considered a useful tool when analysing stratigraphic sequences quantitatively. Depending on the respective research question, future studies should consider the measurement of VIS-NIR spectral data as an alternative to other analytical methods, which are often time-consuming and destructive. For example, spectral measurements could offer a fast and quantitative way of documentation on rescue excavations.

We were able to successfully transfer the image analysis workflow proposed by Haburaj et al. [12] to the archaeological profile SD17P1 from the burial mound of Seddin. Moreover, we want to stress the significant improvements we could add to this process by adding CIELAB data and SOC information, both derived from the multispectral data. The observed similarities between the clustering results

of band combinations, including the SOC raster and, alternatively, the CIE lightness raster, suggest that time-consuming and expensive SOC prediction can be replaced with a fast transformation of the image data to the CIELAB colour space. The noticed benefits should especially be kept in mind when one is working under heterogeneous fieldwork conditions, as CIELAB and slope rasters clearly help to overcome uneven lighting conditions. If intra- or inter-site comparability of the colour and spectral measurements is required, image acquisition should include distinct colour calibration [80,81].

The output of the conducted comparisons suggests that the selective spectral information measured from sediment samples produces similar clustering results to those of the p-ED-XRF data. Increasing the spectral range and resolution of the image data would therefore clearly improve the identification of (sub)layers based on image data. This is consistent with the results of laboratory analyses of, e.g., Steffens et al. [62] or Hobley et al. [63].

Taking the characteristics of the examined profile into account, we consider its stratigraphy complex: While it consists only of a few major layers, the overall heterogeneity of the profile is quite high. This complexity renders sediment sampling rather complicated, as one has to decide which and how many samples to take. At this point, analyses based on image data could fill a gap. This would, however, require a device that offers an improved spectral range and quality. Spectral imaging devices are still in a crucial phase of development though. Imaging technology becomes more sophisticated: Systems become smaller and are more easy to use, as recent studies suggest, using either snapshot (e.g., [12,82]) or push broom (e.g., [83]) technology. However, the offered devices are still often limited to wavelengths below 1000 nm (cf. [84]), rendering the examination of many sediment properties problematic. Established hyperspectral scanners that capture wavelengths beyond 1000 nm are most often expensive and difficult to use under fieldwork conditions due to their dimensions, weight, and power consumption. While the limited capabilities of the presented spectral imaging devices render the extensive analysis of sediment data not yet applicable for most excavations, selective spectral measurements as performed in this study may constitute an easy-to-apply additional step towards a more transparent way of field documentation during archaeological excavations.

Keeping in mind the complex stratigraphy of the profile at hand, applicability of the proposed approach to profiles and plana that include more distinct stratigraphic features (e.g., post-holes, pits, trenches) appears likely. The good performance of RGB image analysis for the examination of high-contrast soil and sediment data was lately described by Haburaj et al. [12] and Zhang and Hartemink [28], indicating a high potential of image classification for archaeological excavations in general. Our results of the selectively measured colour and spectral data indicate that hyperspectral measurements (as we measured with a spectroradiometer) could offer supportive information when analysing more complex or low-contrast stratigraphies (e.g., paleosols).

The sedimentological record obtained in the laboratory is still necessary to describe and interpret the layers. However, the good clustering results of the image data and the general performance of the selective spectral data stresses the potential of more advanced spectral image sensors for future stratigraphic studies. By extrapolating sediment properties from samples to image data, one could combine the benefits of both worlds: A sedimentological record that is not limited to selected sediment samples. While several laboratory studies were already able to produce extensive parameter maps (e.g., [62]), transferability of this approach to on-site recordings still has to be examined.

Based on our results, future studies should either focus on the optimisation of an RGB-camera-based workflow or the in-depth review of more potent spectral sensors. While RGB images delineate layers in the presented study, our results show that image data that would involve spectral information of the same quality as our selective VIS-NIR measurements could prove to be as suitable for assisting stratigraphic delineation and interpretation as labour-intensive laboratory analyses.
