Keywords

1 Introduction

One of the main aims of soil science is to understand the relationships between soil properties, processes, and functions, and recognize and predict soil changes in space and time. To be able to define differences and changes, accessible and reliable soil information is essential. Most soil classification systems have definitions and criteria that are based on field observations supplemented by laboratory analyses. Field observations are often subjective, while laboratory analyses are often time and resource demanding and are performed on samples taken from certain portions of the profile. Digital soil morphometrics is defined as the application of tools and techniques for measuring, mapping, and quantifying soil profile attributes and deriving continuous depth functions (Hartemink and Minasny 2014).

In this chapter, we discuss the potential applications of digital soil morphometrics to predict the building blocks of the major differentiation criteria in soil classification systems. The review is based on selected soil attributes that are part of the definitions of diagnostic units of internationally endorsed soil classification systems. The selected properties are the major differentiation criteria in the definitions of the diagnostic units, hence the taxa of the World Reference Base for Soil Resources (IUSS WG WRB 2014). This chapter will review the potential application of digital morphometrics based on available literature. Some of the reviews will be discussed in the Results section.

2 Materials and Methods

The selected attributes as the major differentiation criteria in the definitions of the diagnostic units of internationally used soil classification systems are based on the World Reference Base for Soil Resources (IUSS WG WRB 2014). The review of the potential application of digital morphometrics is based on the available literature. Hence, some of the materials will be discussed in the Results section.

An example is based on reflectance spectroscopic measurements to predict diagnostic horizons. Thirteen soil profiles from different locations in Hungary were investigated by traditional and Vis–NIR laboratory spectroscopic methods. Using the field descriptions and the auxiliary laboratory data, the soils were classified to the reference soil group (RSG) level according to the WRB classification system. Samples collected from fixed depth intervals were investigated by laboratory Vis–NIR spectroscopic methods to infer the main soil horizons and derive parameters whose distribution along the soil profile can be related to certain key soil properties (organic carbon, CaCO3, and clay content). For the spectral measurements, samples were collected at 5 cm depth intervals to 1.0 m depth and by 10 cm intervals between 1.0 and 1.5 m. The Vis–NIR reflectance spectra of the 325 air-dried, grounded, and sieved samples were acquired using the Analytical Spectral Devices (ASD) FieldSpec 3 MAX portable spectroradiometer with a contact probe attachment. The spectra were transformed to units of absorbance (log(1/reflectance)) and first derivatives were calculated using Savitzky–Golay method (Savitzky and Golay 1964). Principal component (PC) analysis was performed on the spectral dataset to reduce the high dimensionality. The PC scores were used as variables describing the spectral properties of the soils along the profile. To test the “profile description ability” of the spectral dataset, Fuzzy C-means clustering was performed on the matrix of the PC factor scores using KNIME software (Berthold et al. 2007). The number of clusters determined prior the analysis was determined by Silhouette analysis using the R statistical software package (R Development Core Team 2008).

For reference laboratory analysis (organic carbon, CaCO3, and clay content), samples from genetic horizons were collected from each soil profile. To estimate the reference soil parameters in the fixed depth intervals, mass-preserving spline functions were fitted on the reference soil properties using the SplineTool v2.0 software (ASRIS 2011). The spline estimated reference values and the Fuzzy-C membership values were plotted against the depth.

3 Results

3.1 Review of Some Key Soil Properties, Important for Diagnostic Soil Classification

Table 23.1 summarizes the diagnostic horizons, properties, and materials which play a key role in the differentiation of the RSGs in the WRB 2014. The table lists the soil parameters whose determination is necessary to define the reviewed diagnostic units. Based on the study of Hartemink and Minasny (2014), only the soil parameters which can be effectively determined by digital soil morphometric methods are indicated. The parameter list includes soil texture, soil texture variations along the profile, and clay content (combined indication of the three is ST); soil matrix color (MC); soil structure (SS); soil organic carbon content (OC); redoximorphic features and mottles (RF); and calcium carbonate content (CB).

Table 23.1 Summary of diagnostic horizons, properties, and materials whose presence or absence defines the reference soil groups (RSGs)
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ST plays key role in defining 15 horizons, 9 properties, and 1 material. MC plays key role in defining 15 horizons, 9 properties, and 3 materials. SS defines 15 horizons, 4 properties, and 1 material. OC defines 15 horizons, 1 property, and 3 materials. RF defines 8 horizons and 2 properties. Based on soil carbonate (CB), 6 horizons, 4 properties, and 2 materials are defined.

Hartemink and Minasny (2014) gave an overview of soil properties that have been successfully measured or predicted by the tools of digital soil morphometrics. The following chapter is summarizing how the new tools are supporting the establishment of criteria of the major elements of the WRB soil classification system.

3.1.1 Horizon Depth

Ever since Dokuchaev (1883) introduced the horizons as a basic feature in differentiation of soils, the concepts have been accepted by the soil science community (Bockheim et al. 2005). Horizon boundaries provide data about the conditions and processes that have formed the soil. There are great varieties in shape and depth of horizon boundaries ranging from abrupt to diffuse and from smooth to broken. The depth and width of horizons are the criteria for almost all diagnostic units in many soil description or classification systems. Soil scientists spend significant time and often argue during the establishment of depth and width of the horizon depth based on key soil properties, it is expected that digital soil morphometrics may enhance soil horizon determination. Encouraging research results have been published by Doolittle and Collins (1995), Rooney and Lowery (2000), Legros (2006), Weindorf et al. (2012), Steffens and Buddenbaum (2013), and others on the application of the ground-penetrating radar (GPR), electrical resistivity (ER), hyperspectral imaging spectroscopy, and X-ray fluorescence (XRF) (all cited from Hartemink and Minasny 2014) (Table 23.2).

Table 23.2 Diagnostic horizons of the world reference base (WRB) with strong criteria related to horizon depth
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3.1.2 Soil Texture

Soil texture refers to the relative proportions of sand, silt, and clay within the fine earth fraction. Flowcharts are available presenting the way soil texture can be estimated (Rowell 1994; Thien 1979). A frequently used way to describe soil texture in the field is the “finger test” or determining by feel. Texture can be estimated by gently pushing the soil out between the thumb and the forefinger. The success greatly depends on the senses and the experience of the expert, performing the estimation, hence is subjective and final results can be concluded only after laboratory determination. The initial field decision on several diagnostic units and taxa has to be followed after the laboratory results are available. This often does not happen and causes inconsistences in data bases.

Texture plays a major role in the differentiation of albeluvic glossae, retic, vertic properties, fluvic material, lithic discontinuity, abrupt textural difference, further in the case of argic, cambic, fragic, irragric, natric, nitic, vertic horizons, and for the Vertisols reference soil group. Texture differences have significant importance as a criterion for argic horizon in the case of Acrisols, Alisols, Lixisols, and Luvisols, natric horizon in the case of Solonetz; further texture differences are a diagnostic criterion for fluvic material, abrupt textural difference, and retic properties.

Digital morphometrics provides tools to improve objectivity with regard to the determination of the soil texture in the field, making the establishment of many classification units.

Weindorf et al. (2012) tested portable XRF for the determination of soil texture in situ and on cores ex situ in the laboratory. Zhu et al. (2011) measured samples which covered a wide range of soils, and concluded that in situ determination of soil texture with pXRF yielded promising results for relatively dry soils as well as wet soils supplemented with portable moisture sensors. Ge et al. (2005) stated that soil moisture can affect the XRF signal but also offered an algorithm to mitigate similar problems. This issue is discussed further in Stockmann et al. (2015).

Diffuse reflectance spectroscopy was tested by Waiser et al. (2007) for in situ quantification of clay content of soils from a wide range of parent material types. A method based on in situ spectroscopic measurements coupled with chemometric methods was successfully applied by Viscarra Rossel et al. (2009) to estimate soil color, mineral composition, and clay content of samples from multiple depths. Lagacherie et al. (2008) showed how reflectance spectrometry can be used in the laboratory to estimate clay and calcium carbonate content (Table 23.3).

Table 23.3 Diagnostic units (horizons, properties, materials) of the WRB with criteria related to soil texture
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3.1.3 Soil Color

The result of soil color assessment in the field is affected by personal experience. The Munsell Color Theory has brought standardization to color communication as within the system each color has a logical and visual connection to the other colors. Color readings in the field depend on the moisture status of the current soil profile and the quality of light (Pendleton and Nickerson 1951; Post et al. 1993; Simonson 1993). The determination of color is difficult even for experts due to several factors affecting the readings including the quality and age of Munsell charts. Soil color is a diagnostic criterion in WRB for anthraquic horizon, cambic, chernic, fragic, fulvic, hortic, melanic, plaggic, pretic, sombric, umbric horizons, albeluvic glossae, gleyic, retic, sideralic, stagnic properties, and albic material (IUSS WG WRB 2006).

Soil color is the major differentiation criterion for the mollic and umbric horizons which defines Chernozems, Kastanozems, Phaeozems, and Umbrisols reference soil groups.

In the case of cambic and fragic horizons, MC has a basic significance. Fulfillment of the criteria depends on the defined color change compared to the directly underlying layer (WRB). The stagnic properties’ criteria fulfillment also depends on the defined differences in Munsell colors to the surrounding material.

Viscarra Rossel (2009) used Vis–NIR to define soil color in the field and in the laboratory and their results were compared to Munsell color. They have found compatibility between spectroscopic measurements and Munsell readings (Table 23.4).

Table 23.4 Diagnostic units (horizons, properties, materials) of the WRB with criteria related to soil color
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3.1.4 Soil Structure

Soil structure refers to the arrangement of the soil particles into soil units (ped, aggregates) resulting from several pedogenic processes (FAO 2006). Alternation of the dry and wet conditions, root activity, and fauna is important in the formation of SS (Materechera et al. 1992).

Structure is a differentiation criterion in the WRB in the case of mollic and umbric horizons; anthraquic, cambic, chernic, nitic, vertic, irragric, petrocalcic, calcic, further, in the case of Solonetz columnar or prismatic (or blocky) structure should present to fulfill the criteria.

The correct determination of SS is critical especially in the case of natric—columnar, prismatic (or blocky) structure required—because it determines the Solonetz reference soil group.

The notion of “strong structure” for mollic and umbric surface horizons is required because they are diagnostic for Umbrisols, Chernozems, Kastanozems, and Phaeozems reference soil groups. The definition of “strong” is too broad and the determination can be subjective even with expert knowledge. Either the clarification of phrasing of the definition “sufficiently strong structure” or the reformation of tools used for the structure determination is needed.

NIR and MIR spectroscopy have been applied to estimate soil organic carbon and clay content (Gomez et al. 2013) but no device is available that can measure the distinct aspects of the SS in the field (Hartemink and Minasny 2014). Hirmas and Hasiotis (2010) used laser imaging for measurement of structure (Table 23.5).

Table 23.5 Diagnostic units (horizons, properties, materials) of the WRB with criteria related to soil structure
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3.1.5 Organic Matter

Organic matter plays a crucial role in each existing classification system.

Organic matter content of surface horizons can determine Histosols, Chernozems, Kastanozems, and Phaeozems through mollic, chernic, and umbric surface horizons.

There are several measurement methods for determining organic matter and organic carbon in the laboratory but two results of two different measurement methods cannot be compared with each other.

As the present definitions are hard to handle, clarification or simplification of limits are recommended (Michéli et al. 2014). Steffens et al. (2014) studied the soil organic matter content and composition applying imaging spectroscopy. They concluded that Vis–NIR imaging spectroscopy is an effective tool for mapping soil organic matter quality even if the layers are not distinguishable visually.

Viscarra Rossel and Hicks  (2015) concluded that Vis–NIR spectroscopy is a useful, cheap technique to observe and monitor organic carbon composition. Other studies used Vis–NIR spectroscopy to estimate organic layers in forests (Chodak et al. 2002). Viscarra Rossel et al. (2008) applied a simple digital camera and found correlations for OC and Fe contents (Table 23.6).

Table 23.6 Diagnostic units (horizons, materials) of the world reference base (WRB) with criteria related to organic carbon (OC) content
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3.1.6 Mottling

Mottles are differently colored spots in a soil matrix and are mostly the result of reduction and oxidation of Fe. Concreted mottles of oxides are diagnostic for the hydragric, ferric, plinthic, petroplinthic, and pisoplinthic horizons and for the stagnic color pattern. Fe or Mn coatings or concentrations or redox depletions are diagnostic criteria for hydragric horizon according to WRB. Mottles and redoximorphic features are key differentiation criteria for Stagnosols and Gleysols.

The presence of FeII ions can be determined in the field with a 0.2 % α, α dipyridyl solution in 10 % acetic acid solution, but these chemicals are slightly toxic. Steffens and Buddenbaum (2013) concluded that laboratory imaging spectroscopy facilitate the spatially correct soil classification including the quantification of soil mottling (Table 23.7).

Table 23.7 Diagnostic units (horizons, properties, materials) of the WRB with criteria related to redoximorphic features and mottles
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3.1.7 Carbonates

Determination of calcium carbonate content in the field is established by adding a few drops of 10 HCl to the soil. The degree of effervescence refers to the presence and amount of calcium carbonate. The rate of reaction depends on soil texture and other materials such as plant tissues. Determination of the 15 % calcium carbonate content—which is the required amount for calcic horizon—has a decisive role in differentiation for Calcisols, Chernozems, Kastanozems, and Leptosols. Furthermore, determination of the origin of the carbonate in the field also requires field experience and could provide information about the processes under the current soil has been formed (FAO 2006).

In WRB, evidence of the leaching of carbonates from the cambic horizon is a diagnostic criterion for Cambisols. Differences in calcium carbonate content between parts of a horizon are part of the definition of the irragric horizon. Calcic horizon or a layer with protocalcic properties is also a requirement for Calcisols, Chernozems, and Kastanozems (WRB) (Table 23.8).

Table 23.8 Diagnostic units (horizons, properties, materials) of the WRB with criteria related to CaCO3
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3.2 Vis–NIR Spectroscopy for Distinguishing Soil Horizons

A previous study (Csorba et al. 2014) showed that Vis–NIR reflectance spectroscopy coupled with principal component variables (PC factor scores) can be effectively used as variables describing the spectral properties along the soil profile. This study focuses on the definition of diagnostic horizons.

The Silhouette analysis performed prior to the Fuzzy C-means clustering showed that the PC factor score values can be classified into three clusters (Clusters A, B, and C). Figure 23.1 shows the distribution of the samples along the first three principal components that explained 92 % of the total variance. The color coding and the symbols in Fig. 23.1a refer to the field-determined WRB diagnostic horizons, while Fig. 23.1b shows the classes obtained from the Fuzzy C-means clustering. Based on the visual inspection of the scatterplots, the clustering of samples is in good accordance with the determined diagnostic horizons. Major part of Cluster A samples were taken from a calcic, Cluster B from a mollic, and Cluster C from an argic horizon.

Fig. 23.1
figure 1

The 3D scatterplots showing the distribution of samples along first three principal components. a The coloring and symbology refer to the WRB diagnostic horizon the samples belong to. Calcic filled blue circle; Argic filled red square; Mollic filled green triangle; Mollic—Calcic open circle Non-diagnostic horizon black circle. b The coloring and symbology refer the Fuzzy C-means clusters the samples belong to Cluster A filled ash circle; Cluster B filled red square; and Cluster C filled green triangle

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Three examples of the comparison of the Fuzzy-C membership values and the spline-resampled organic carbon, CaCO3, and clay content values versus the depth are shown in Fig. 23.2. The cluster membership values of the Cluster A show similar pattern as the spline estimated CaCO3 values. The membership values of the Cluster B show similar pattern as the spline estimated organic carbon values. The explanation of the distribution of the membership values of the Cluster C along the profile needs a different approach. Their distributions show similarity with the clay content only in the case of soil profiles where considerable clay illuviation has occurred.

Fig. 23.2
figure 2

Three examples of the distribution of the cluster membership values and the reference spline-resampled OC, CaCO3, and clay content values (with circles). On the plot showing the membership values versus the depth, the depth intervals of the clusters are also indicated (with the rectangles of different shades of gray)

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4 Summary and Conclusions

During this study, the digital soil morphometric tools proved to be efficient in the determination of soil parameters playing key role in the definition of diagnostic units of the WRB were reviewed. Six soil parameters were investigated based on their role of defining the diagnostic criteria. The reviewed digital soil morphometrics tools and methods are supporting the prediction of properties that are part of the criteria of diagnostic units of WRB. Some of these attributes are determined or estimated in the field with subjective element and supported by laboratory analysis. The new tools can bring a revolution to soil classification and to soil science in general, as they provide cost effective and quick measurements and results to assist in the field decisions and the process of soil classification.

Effectiveness is not the only benefit of these methods; compared to the standard methods, these tools can provide a cleaner technology with minimizing or cease the environmental impacts of measurements.

The example study demonstrated the significance of Vis–NIR reflectance measurements in predicting diagnostic horizons. Because the technology supplies integrative measurements of soil, it can facilitate the collection of large amount of soil data and provide more information than the conventional—accurate but expensive—survey methods.

In summary, digital morphometrics provides the potential of less subjective, more time and cost efficient and environment friendly support or replacement of field and laboratory methods applied in soil classification.