Soil structure is of central importance for soil functions. Besides its relevance for plant growth, this is also true for the storage and movement of water and solutes inside the soil pore system for biochemical matter cycling and for soil as habitat for a myriad of interacting organisms .

For a long time, a crucial hurdle in exploring soil structure was that soil is opaque so that soil structural properties were hardly accessible. This was especially true with respect to quantitative analysis as required for any scientific evaluation.

During the last three decades, with the development and increasing availability of X-ray CT scanners, we are now in the position to quantify soil structure without disturbance in full three dimensions and with a spatial resolution of a few micrometers or even below. This boosted an enormous amount of scientific insight especially with respect to the soil pore structure in relation to water dynamics and solute transport . More recently, also the importance of soil structure for the turnover of organic matter and as habitat for soil organisms is studied based on 3D images.

In parallel, software tools especially tailored for the analysis of 3D porous media analysis were developed partly as commercial products , partly as open source applications and program libraries such as QuantIm (https://www.quantim.ufz.de, last access: 22 July 2022) or ImageJ/FIJI with dedicated 3D structure analysis plugins like SoilJ , BoneJ , MorpholibJ and the Python software scikit-image .

Fortunately, the available computing power increased together with the size of the images generated by X-ray CT scanners. However, when it comes to the calculation of distance distributions and connectivity measures, a considerable amount of computing power is required which often exceeds the capacity of standard computers. Another difficulty is the lack of comparability of the results since the detailed algorithms to calculate soil structural attributes such as connectivity or pore size distribution are not always obvious. Hence some standardized analysis would be beneficial to generate results that are comparable among different studies.

The motivation of the Soil Structure Library (https://structurelib.ufz.de/, last access: 22 July 2022) introduced in this paper is to offer some standardized analysis of the 3D pore structure obtained from X-ray CT together with the required computing power. The price we charge for this service is that the analyzed structures are made freely available through our website together with the metadata describing the soil. This should generate some substantial benefit for both the data providers who get standardized analysis for their CT images and for the wider scientific community who gets access to a wide range of soil structure data including additional information on the specific climate, land use and soil type.

It should be noted that the provided analysis is limited to the analysis of binary images. This means that the user needs to upload images which are already segmented into pore and solid. We are aware that segmentation is a crucial step in image analysis and there are no objective procedures on how to do it . This is why we prefer to leave this step to the data owner who, however, has to upload at least one 2D image of the original gray scale CT image so that the effect of the segmentation process is illustrated and can be understood by others.

Our Soil Structure Library is open for anybody with a clear focus on the soil science community. For uploading image data, the user has to subscribe and to ascertain the data policy. For each uploaded data set, metadata on soil and site properties are requested. A part of this information is mandatory, another part is optional. Table  gives an overview of the metadata.

Although many of the parameters are listed as optional, it is highly recommended to provide a rather complete list as this information will make the data much more valuable for others. The complete content of the database, including the 3D binarized X-ray images, is provided under Creative Commons and thus can be used for further research by the entire scientific community.

The architecture of the library comprises three servers: a web server, a database server and an image processor. The web server hosts the user frontend and manages the user administration, data input, file uploads and the presentation of results and metadata. It is implemented in Django which integrates data modeling and web service based on Python. Django communicates with the database server configured as a MySQL server. The image processing is triggered as soon as the data server receives new data from the frontend. It gets transferred to the image processor consisting of a Linux workstation where an ImageJ macro is launched. Upon completion, the results are uploaded to the database server. Simultaneously, the web server sends an email to the submitter to inform them about the completion of the calculations.

All machines but the web server are behind a firewall. The only connection across this firewall is through the database connection. All other required connections are realized behind the wall. The modular structure makes it possible to provide further computing power (i.e., a computing cluster) when needed.

The Soil Structure Library can be harnessed in various ways. First, it is a data repository that can be used by scientists to upload their segmented X-ray CT data and make it available to the public. This data availability is becoming more important as, for good reasons, an increasing number of scientific journals have introduced a stricter policy in this respect.

Second, segmented X-ray CT data for a large number of different soils are a valuable source of realistic scenarios which can be used for development and testing of three-dimensional, image-based modeling approaches. Examples of image-based modeling could be water flow and matter transport by convection and dispersion , matter turnover by reaction and diffusion or maintenance of biodiversity by habitat modeling. The model results can be put in a broader context by regression analysis with the morphological properties of the pore structure and with the uploaded meta-information that characterize basic soil properties. In the long run, the Soil Structure Library can host a similar number and variety of pore structures in soil, like popular repositories such as the digital rocks portal offer for pore structures in rocks mainly for the petroleum engineering science community.

Third, the Soil Structure Library can be a reference for the suitability of soil as habitat for soil organisms. Structural attributes can be linked to biological activity or the abundance of various species . If such relations are found and can be expressed in the metrics provided, the Structure Library provides the database to identify soil types or soil management practices that are expected to impact the soil biome and its activity in one way or another. This also includes soil processes that emerge from the interplay of pore structure and microbial activity like the formation of anaerobic soil volumes and greenhouse gas formation .

Fourth,the Structure Library can be mined in order to deduce general patterns, relationships or tipping points that may exist among structural properties or between basic soil properties and structural properties. A short example shall suffice here to demonstrate such a data mining approach.

Several metrics have been implemented that quantify different aspects of pore connectivity. Percolation represents the existence of a continuous path between image borders, i.e., long-range connectivity between distant locations in the pore space. The critical pore diameter dcr indicates the size of the smallest pore neck in this path. The Euler characteristic reflects the intrinsic connectivity independent of location or distance. It does not provide any information on the length scale of connections, but about the internal number of connections independent of whether they are percolating or not. It has been conjectured that, under certain conditions of structural homogeneity, the percolation threshold the number of isolated objects and redundant loops are exactly balanced, i.e., χ=0 . The corresponding minimum pore diameter when this balance is reached shall be denoted as dχ0. The transition in connection probability Γ from fully connected to completely fragmented is expected to occur in a similar pore diameter range. It will decrease monotonically when small pores are removed sequentially in a series of increasing minimum pore diameters. Until now, there has been no comprehensive analysis as to (1) whether long-range connectivity and intrinsic connectivity break apart around the same pore diameter and (2) what the remaining pore volume Vv and connection probability Γ at these pore diameters (dcr and dχ0) are. In addition, these connectivity metrics may serve as a fingerprint of the pore structure that can distinguish between pore systems that are generated by different processes. Such an approach will be demonstrated here for a selection of soil samples with identical resolution (20 µm) and similar soil properties (texture, SOM content, climate, etc.) but managed as long-term conventional tillage (CT) or no-tillage (NT). Samples for both treatments originate from different locations . All samples without a percolating pore cluster were sorted out beforehand and all isolated pores in the original pore network were removed in order to ensure that the analyzed pore space is well connected and χ of the entire percolating cluster is negative. The complete data set comprises 104 samples (CT: 34, NT: 70).

It turns out that dχ0 is much smaller than dcr irrespective of tillage treatment (Fig. a–b). This is because dχ0 indicates at which pore diameter the removal of pore necks due to morphological openings has created as many isolated pores as remaining redundant connections, whereas dcr indicates when the last pore object that still sustains vertical percolation is lost towards the end of this succession of pore removal steps. Both dχ0 and dcr are significantly higher in pore structures produced by plowing (Fig. b). That is, the fragmentation of the soil through mechanical disturbance forms a network of large macropores with higher connectivity. There are occasional outliers for dcr in the NT treatment that represent samples with at least one large continuous biopore from top to bottom that is not refilled by casts or soil fragments. In fact, these outliers even lead to similar dcr averages (crosses in violin plots) despite significantly different populations in terms of rank metrics (Fig. b).

The very different pore morphology with and without plowing also manifests itself in the remaining porosity at the minimum pore diameter where dχ0 and dcr occur (Fig. c). The pore structure in undisturbed soil (NT) is dominated by cracks and biopores with elongated, planar or cylindrical shapes. That is, they stretch across long distances with rather small volumes. The pore structure after plowing, in turn, is dominated by more bulky and isotropic packing pores with a lower spatial extent per volume. This is why the pore structure in NT soils needs significantly less porosity to sustain both dχ0 and dcr.

As a consequence, the connection probability of the remaining porosity at dχ0 is larger in CT soils (Fig. d) because more of the NT soils have already reached a critical macroporosity range around 0.05–0.1 at which Γ decreases sharply. At dcr, both NT and CT pore structures are in this critical macroporosity range. As a result, there is a huge variability in Γ with fluctuations across the entire possible range. In addition, both treatments exhibit a distinct bimodal distribution of Γ(dcr) enforced by the non-linear relationship between visible porosity and connection probability.

Finally, it has been conjectured before that biopores produced by fine roots with typical diameters of 0.1–0.2 mm are the main contributor to pore connectivity in no-till samples. This is confirmed with this case study by the fact that (i) dcr falls into this root diameter range and (ii) Γ also reaches 0.5 around dcr.

With the Soil Structure Library, we have established a free platform for sharing segmented X-ray CT images of pore images among the soil science community. The library can be used as a conventional data repository to provide access to 3D large image data, which is a service that has not been available until now but is becoming more important with updated data policies of many journals. Likewise, the Soil Structure Library is a rich source of realistic three-dimensional pore structures for image-based modeling on a large range of image resolutions and domain sizes. Access to such image data is appealing especially to scientists with no or limited access to imaging facilities. In a similar vein, the Soil Structure Library offers free, standardized and reproducible soil structure analysis to users who lack the computing infrastructure or expertise for pore structure analysis. The full potential of the Soil Structure Library unfolds, however, when harnessed for data mining and regression analysis with complementary meta-information in order to better understand the relationship between soil structure and soil functions.