Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging

Abstract

Subsoil organic carbon (OC) is generally lower in content and more heterogeneous than topsoil OC, rendering it difficult to detect significant differences in subsoil OC storage. We tested the application of laboratory hyperspectral imaging with a variety of machine learning approaches to predict OC distribution in undisturbed soil cores. Using a bias-corrected random forest we were able to reproduce the OC distribution in the soil cores with very good to excellent model goodness-of-fit, enabling us to map the spatial distribution of OC in the soil cores at very high resolution (~53 × 53 µm). Despite a large increase in variance and reduction in OC content with increasing depth, the high resolution of the images enabled statistically powerful analysis in spatial distribution of OC in the soil cores. In contrast to the relatively homogeneous distribution of OC in the plough horizon, the subsoil was characterized by distinct regions of OC enrichment and depletion, including biopores which contained ~2–10 times higher SOC contents than the soil matrix in close proximity. Laboratory hyperspectral imaging enables powerful, fine-scale investigations of the vertical distribution of soil OC as well as hotspots of OC storage in undisturbed samples, overcoming limitations of traditional soil sampling campaigns.

Non-Destructive Detection of Tea Leaf Chlorophyll Content Using Hyperspectral Reflectance and Machine Learning Algorithms

“…Random forest regression builds multiple decision trees called classification and regression trees (CART) based on randomly bootstrapped samples of the training data [76] via generalization of the binomial variance (using a Gini index) and with nodes that are split using the best split variable from a group of randomly selected variables [77]. Since previous research has demonstrated the effectiveness of RF [78,79], it was used as a benchmark in this study. The number of trees (ntree) and the number of variables used to split the nodes (mtry) are normally defined by the user.…”

Mathematical Functions to Model the Depth Distribution of Soil Organic Carbon in a Range of Soils from New South Wales, Australia under Different Land Uses

“…Undecomposed residue from plants and some animal products are added to the soil surface, which contribute to the formation of humus for soil organic matter [53]. In the case of cropping, the depth of this layer can be affected by cultivation, with tillage tending to produce a uniform SOC distribution with depth through the disturbed layer [15,[54][55][56][57][58]. However, this does depend on the depth of tillage and the implements used, as shallow cultivation (<10 cm) under cropping may not result in a surface layer with a uniform SOC [56,59].…”

“…Coarse soil structure can influence the root architecture and the flow patterns in the soil and so the distribution of dissolved SOM. As a consequence, some SOC can be concentrated in biopores, preferential flow paths or on the outside of peds and in cracks between peds as preferred paths for root growth [47,58,68]. As a consequence, the spatial variability in SOC is higher in subsoils than in the surface soils [58].…”

Combination of Imaging Infrared Spectroscopy and X-ray Computed Microtomography for the Investigation of Bio- and Physicochemical Processes in Structured Soils

“…OM is not evenly distributed in soils. For instance, the walls of biopores have been often reported as OM rich compared with bulk soil (Banfield et al, 2017;Hoang et al, 2017;Hobley et al, 2018). It is assumed that the heterogeneous location of OM in soil may be a significant factor controlling the extent of OM mineralization (Dungait et al, 2012;Steffens et al, 2017).…”

“…Imaging Vis-NIR (imVNIR) is an emerging technique that allows the spatial analyses of intact soil samples (Buddenbaum and Steffens, 2011;Steffens and Buddenbaum, 2013). Steffens and Buddenbaum (2013), Steffens et al (2014) and Hobley et al (2018) demonstrated the potential of imVNIR by improving our understanding in soil classification, chemical composition and carbon storage on the micrometer scale for whole pedons.…”