Soil Profile Classification Research Articles

Physico-chemical Characterization and Taxonomic Classification of Soil Profiles in a Toposequence Located in RRTTS and KVK Farm, Keonjhar, Odisha, India

The study investigates the physico-chemical characteristics and taxonomic classification of soil profiles along a toposequence at the RRTTS and KVK farm, Keonjhar, Odisha. Three distinct land types - upland, medium land, and lowland - were selected for the study, representing varying topographic positions. The investigation focused on soil properties such as bulk density, particle density, pH, organic carbon content, electrical conductivity, cation exchange capacity (CEC), base saturation, and available macro and micronutrients. Results indicated that bulk and particle densities were lower in upper horizons and increased with depth, while soil pH exhibited an increasing trend with increasing depth, likely due to the movement of basic cations during intensive rainfall. Organic carbon content was higher in surface horizons and declined with depth, whereas available potassium increased with depth, attributed to parent material and clay content. The exchangeable cations, primarily calcium and magnesium, dominated the soil profiles. The soils were classified into Loamy-skeletal, mixed, hyperthermic Typic Ustorthents (Pedon 1), Fine-loamy, mixed, hyperthermic Udic Haplustalfs (Pedon 2), and Fine-loamy, mixed, hyperthermic Udic Paleustalfs (Pedon 3). The study concluded that different landforms within the toposequence require specific land-use planning and conservation measures to enhance soil productivity and sustainability. Upland areas are recommended for plantation and agroforestry, medium land for crop production, and low land for paddy cultivation and pisciculture. Tailored conservation strategies, including contour cultivation and water harvesting, are essential to mitigate soil erosion and optimize land use in the region.

Proximal sensing approach for soil characterization and discrimination: a case of study in Brazil

The potential of using spectroscopy for the quantification of soil attributes through its spectral signature is widely documented in the literature. However, a protocol to support formal soil classification systems combining spectral data has not been established. This research proposed a protocol for soil profile classification by combining spectral data from the near visible, shortwave infrared (Vis-NIR-SWIR) and mid infrared (MIR). For this purpose, we used 15 soil profiles located in the Pernambuco State, Brazil. A quantitative analysis between soil attributes and spectral curves was performed for the selection of bands with the best correlations (method 1). In addition, the recursive feature elimination (RFE) function was used for the selection of discriminant bands between soil profiles (method 2). The results of this research indicated that the combined use of spectra is efficient to successfully grouping Ferralsol, Gleysol, and Acrisol. The integrated use of sensors, pedometric techniques, and the expertise of soil scientists can lead to an advanced understanding of soil science.

Seismic site classification of the Costa Rican Strong-Motion Network based on VS30 measurements and site fundamental period

In this paper, a new seismic site classification for the Costa Rican Strong-Motion Network (CRSMN) is proposed. The soil profile classification of the Costa Rican Seismic Code based on the average shear-wave velocity of the top 30 m (VS30) is used as a reference. The site fundamental period (Tf) is included as a parameter to complement the existing characterization. For this, the VS30 measurements from 52 accelerometric stations are related to the site fundamental period obtained through horizontal-to-vertical spectral ratios (HVSR) using ground motion records from the Costa Rican Strong-Motion Database. The H/V ratios are estimated with 5% damped acceleration response spectra and with traditional Fourier amplitude spectra from the S-wave window. From the relation between VS30 and Tf, different ranges of Tf are assigned to the existing soil profile classification and a graph with three-lines and four-areas is proposed to classify the stations of the CRSMN.

Pedometric tools for classification of southwestern Amazonian soils: A quali-quantitative interpretation incorporating visible-near infrared spectroscopy

The southwestern region of the Amazon has great environmental variability, presents a great complexity of pedoenvironments due to its rich variability of geological and geomorphological environments, as well as for being a transition region with other two Brazilian biomes. In this study, the use of pedometric tools (the Algorithms for Quantitative Pedology (AQP) R package and diffuse reflectance spectroscopy) was evaluated for the characterization of 15 soil profiles in southwestern Amazon. The AQP statistical package—which evaluates the soil in-depth based on slicing functions—indicated a wide range of variation in soil attributes, especially in the superficial horizons. In addition, the results obtained in the similarity analysis corroborated with the description of physical, chemical components and oxide contents in-depth, aiding the classification of soil profiles. The in-depth characterization of visible-near infrared spectra allowed inference of the pedogenetic processes of some profiles, setting precedents for future work aiming to establish analytical strategies for soil classification in southwestern Amazon based on spectral data.

Estimation of andic properties from Vis-NIR diffuse reflectance spectroscopy for volcanic soil classification

Volcanic soils show peculiar characteristics related to the presence of poorly ordered crystalline minerals. These minerals are highly sensitive to specific spectral bands in the visible and near-infrared (Vis-NIR) regions and could be used to establish important relationships between andic properties and soil classification. In order to overcome the expense of traditional soil laboratory analysis and the limitations of pedotransfer functions, this research was designed to test i) the possibility of using diffuse reflectance spectra (DRS) for a supervised chemometric classification of soils with different degrees of andicity, ii) the possibility of using diffuse reflectance spectra (DRS) to estimate the andic properties needed for soil classification (iron and aluminum forms, allophane, phosphate retention, vitric content etc.), and iii) the different multivariate statistical approaches for the analysis of spectra. Diffuse reflectance spectra were measured between 200 and 2500 nm in the laboratory with a Jasco spectrophotometer equipped with an integrated sphere.Chemometric analysis was carried out by discriminant analysis that correctly divided 86% of the horizon samples in 5 classes, representing different levels of expression of the andic properties. Chemometric calibration was obtained by pre-processing Vis-NIR spectra with the application of partial least-squares regression (PLSR). Andic soil attributes were then predicted using calibrated models through PLSR methods and compared with the findings from the Supported Vector Machine (SVM). SVM generally outperformed PLSR on predicting andic properties by Vis-NIR. The most accurate predictive models were obtained for Al and Fe dithionite extracted, both with PLSR (ratio of performance to deviation, RPD 1.9) and SVM (1.7 < RPD < 1.9). The total amount of i) non-crystalline Al and Fe forms (oxalate extractable, Alo, Feo), ii) Alo + 1/2Feo (1.7 < RPD < 1.8), iii) non-crystalline Si (Sio) (RPD 1.5), and iv) Al associated with organic matter (sodium pyrophosphate extractable, Alp; RPD 1.5), Alo-Alp (RPD 1.5), allowed a first soil Andosol qualification, when obtained through SVM. Therefore, the results of this research highlight that Vis-NIR can be used as a preliminary analysis to estimate diagnostic parameters of Andosols. Results indicate that Vis-NIR is a promising technique for volcanic soil classification according to soil properties (especially Fe and Al forms) and horizons, which can be used in lieu of complex chemical and physical analyses involved in routine soil profile classification. Further studies should confirm Vis-NIR ability to discriminate Andosols at more detailed classification levels.

How qualitative spectral information can improve soil profile classification?

Soil classification is important to organize the knowledge of soil characteristics. Spectroscopy has increased in the last years as a technique for descriptive and quantitative evaluation of soils. Thus, our objective was to assess qualitative and quantitative methods on soil classification, based on model profiles. Soils in different environments in the Roraima state, Brazil, were evaluated and represented by 16 profiles, providing 109 soil samples, which were analyzed for particle size distribution, chemical attributes and spectral measurement. Visible-near infrared spectra (350–2500 nm) of soil samples were interpreted in terms of intensity, shape and features. The soil color obtained using a spectroradiometer and a colorimeter, and by a soil expert was compared. Descriptive and qualitative analyses were performed for all spectra of the soil profile samples. The descriptive evaluations of the spectral curves from all horizons of the same profile were used to identify the diagnostic attributes and assign a profile to a taxonomic class. This was possible because spectra of samples had specific shapes, features and intensities that combined to present a specific signature. The Outil Statistique d’Aide à la Cartogénèse Automatique and cluster quantitative analyses could not correctly group similar soil classes and they still need to be improved in order to extract all the variability of the spectral data to discriminate soil classes. Soil color quantification by the Munsell system using both equipments showed greater R2 and lower error than that achieved by a soil expert, due to influences of subjectivity inherent in human assessments. Based on this specific case, it was clear that the automatic system may be more consistent than the pedologist’s visual method. Future studies should focus on the development of an online tool that integrates a descriptive approach and spectral information of a given soil profile to determine its probable taxonomic class.

Identification of soil profile classes using depth-weighted visible–near-infrared spectral reflectance

Soil visible–near-infrared (Vis-NIR) diffuse reflectance, which can be collected rapidly and cheaply, contains rich information of the properties that are useful for soil taxonomic diagnosis. Vis-NIR spectroscopy has the potential to identify soil taxonomic classes in an efficient and cost-effective way. This study explores the potential of Vis-NIR spectroscopy to identify soil profile classes at the order, suborder, group, and subgroup levels of the Chinese Soil Taxonomy (CST).The authors measured the Vis-NIR (350–2500 nm) diffuse reflectance of 2260 legacy soil samples, which were sampled by genetic horizon and collected from 527 soil profiles (75% for training and 25% for validation). To represent the overall spectral pattern for each soil profile, the depth-weighted average was used to combine the spectral reflectance of genetic horizons. The synthetic minority over-sampling technique (SMOTE) was used to obtain balanced training data. Principal component analysis (PCA) was performed to extract spectral predictors used for random forest (RF) modeling in soil classification.Vis–NIR spectral reflectance exhibited acceptable overall performance for identification of soil orders and suborders, with overall validation accuracies of 0.63 and 0.62, respectively, but low overall performance at group and subgroup levels, with overall validation accuracies of 0.40 and 0.28, respectively. The overall performance at different taxonomic levels was affected by the number of soil classes and the class distribution of the soil profiles. Soil classes with pedogenic processes associated closely with spectrally active soil properties or with characteristic profile patterns were most accurately identified, even if they were minority classes. The results show that the Vis–NIR spectral pattern of soil profile can be used to identify soil profile classes at higher taxonomic levels in the CST system. Combined with machine-learning techniques, the soil Vis–NIR spectral library will serve as an efficient tool for digital soil survey mapping and updating with the use of legacy soil samples and the reduction of conventional laboratory analyses.

Comparing traditional and digital soil mapping at a district scale using residual maximum likelihood analysis

Conventional soil mapping uses field morphological observations to classify soil profiles into predefined classification systems and extrapolates the classified soils to make a map based on aerial photographs and the experience of the surveyor. A criticism of this approach is that the subjectivity of the surveyor leads to non-reproducible maps. Advances in computing and statistical analysis, and an increased availability of ancillary data have cumulatively led to an alternative, referred to as digital soil mapping (DSM). In this research, two agriculturally productive areas (i.e. Warren and Trangie) located in central New South Wales, Australia, were considered to evaluate whether pedoderms and soil profile classes defined according to the traditional approach can also be recognised and mapped using a DSM approach. First, we performed a fuzzy k-means analysis to look for clusters in the ancillary data, which include data from remote-sensed gamma-ray (γ-ray) spectrometry and proximal-sensed electromagnetic (EM) induction. We used the residual maximum likelihood method to evaluate the maps for various numbers of classes (k = 2–10) to minimise the mean square prediction error (σ2p,C) of soil physical (i.e. clay content, field capacity (FC), permanent wilting point (PWP) and available water content (AWC)) and chemical (pH, EC of 1 : 5 soil water extract (EC1:5) and cation exchange capacity (CEC)) properties of topsoil (0–0.3 m) and subsoil (0.6–0.9 m). In terms of prediction, the calculated σ2p,C was locally minimised for k = 8 when accounting for topsoil clay, FC, PWP, pH and CEC, and subsoil FC, EC1:5 and CEC. A comparison of σ2p,C of the traditional (seven pedoderm components) and DSM approach (k = 8) indicated that only topsoil EC1:5 and subsoil pH was better accounted for by the traditional approach, whereas topsoil clay content, and CEC and subsoil clay, EC1:5 and CEC were better resolved using the DSM approach. The produced DSM maps (e.g. k = 3, 6 and 8) also reflected the pedoderm components identified using the traditional approach. We concluded that the DSM maps with k = 8 classes reflected the soil profile classes identified within the pedoderms and that soil maps of similar accuracy could be developed from the EM data independently.

Visible and Near-Infrared Spectroscopy as a Tool for Soil Classification and Soil Profile Description

This paper presents preliminary results of the use of visible and near-infrared (VIS -NIR) spectroscopy for soil classification and soil profile examination. Three experiments involving (1) three different soil types (Albic Luvisol, Gleyic Phaeozem, Brunic Arenosol), (2) three artificial micro-plots with similar texture (loamy sand, Gleyic Phaeozem) but different soil organic carbon (SOC) content and (3) a soil profile (Fluvisol) have been investigated using VIS -NIR spectroscopy. Results indicated that VIS -NIR is a promising technique for preliminary soil description and can classify soils according to soil properties (especially SOC ) and horizons. Instead of complex chemical and physical analyses involved in routine soil profile classification, VIS-NIR spectroscopy is suggested as a useful, rapid, and inexpensive tool for soil profile investigation.

Bottom-up digital soil mapping. II. Soil series classes

Continuous classification of soil has generally focussed on the classification of soil profiles as single entities. This is because most continuous classification algorithms (for example the fuzzy k-means algorithm) are not able to cope with “composite objects”, i.e. entities that are composed of sub-entities arranged in a specific sequence, as in the case of soil profiles being composed of sequences of soil layers. An algorithm has recently become available that enables the continuous classification of soil profiles as sequences of soil layers. This is the “Outil Statistique d'Aide à la Cartogénèse Automatique” (OSACA) application developed by Carré and Jacobson (Carré, F. and Jacobson, M., 2009. Numerical classification of soil profile data using distance metrics. Geoderma, 148: 336–345). Until now it has been used to carry out the continuous classification of soil profiles using soil properties of the profiles' layers, but in this work the soil layers are characterised by their membership to the continuous layer classes created in Part I of this paper. We used OSACA to create what we call “soil series classes”, which are classes of soil profiles whose layers consist of common sequences of membership to the soil layer classes created in Part I of this paper (Odgers, N. P., McBratney, A. B. and Minasny, B. Bottom-up digital soil mapping. I. Soil layer classes. Geoderma, in press). 9 soil series classes were found to be optimal for the set of soil profiles used in our study. The taxonomic distance to the modal profiles of the soil series classes was mapped using regression-kriging, then the membership to each class was calculated from the taxonomic distance.

High-resolution continuous soil classification using morphological soil profile descriptions

Soil grid data were gathered from 156 points in the 30-ha Muizen forest (Ranst, Belgium). At each grid point, soil profiles were examined morphologically by augering to 120-cm depth. In the laboratory, pH(KCl) was determined on samples from every horizon. To allow numerical analyses, all the morphological attributes were given ordinal scores. The analysis consisted of two parts. First, the master horizons were split up into subtypes using Principal Components Analysis and a non-hierarchical clustering technique. This was necessary to overcome the problem of the anisotropy of the soil profiles, which makes it impossible to pool the data of all the horizons and analyse them together. Next, the distinguished horizon subtypes were used as input for the continuous soil profile classification with the ‘fuzzy k-means with extragrades’ algorithm. Five different soil classes plus an extragrade class were distinguished. The distinguished soil classes exhibited a fair degree of spatial autocorrelation and correlated well with the Belgian Soil Map. The technique developed ensures the compatibility with national or global soil classification systems based on diagnostic horizons and properties on the one hand and the production of high-resolution soil classes for local use on the other. Furthermore, the developed technique allows reanalysis and optimisation of data from previous surveys.

Water movement and stability of profiles in drained, clayey and swelling soils: at saturation, the structural stability determines the profile porosity

In clayey, swelling and more or less sodic soils, cultivation and seasonal climatic cycles induce variations in soil moisture which in turn cause variations in the soil structure. In particular, when the soil profile is saturated, some soils become impermeable because the soil porosity value does not remain sufficiently high throughout the drainage period to be effective for water movement. In the marshland soils of the French Atlantic coast, this ability is linked with the soil stability. We showed that soil profile stability and porosity are linked. First, we produced a classification of soil profiles ordered according to their degree of stability. Then, after describing the main features of the soil profile structure at saturation period, we proposed the concept of ‘porosity profile’. This classification and concept were tested on farm fields by measuring two parameters: the water table in winter and the rooting depth of winter wheat. The values assumed by these parameters are indicators of field hydraulic and agronomic behaviour and more precisely of the depth of the soil layers where macroporosity allows water circulation and root colonisation. These results enabled us to validate the concept of ‘porosity profile’.

A geostatistical descriptor of the spatial distribution of soil classes, and its use in predicting the purity of possible soil map units

A geostatistical descriptor of the spatial distribution of soil profile classes is discussed, the Sum of Indicator Variograms (SIV). This function may be estimated for a region from observations of the soil profile class occurring at a systematic array of sample sites, and fitted by an authorised variogram model. It is shown that the fitted variogram model can be used, given some simplifying assumptions, to predict the average purity of map units for a soil map of the region with a legend of simple units corresponding to the profile classes. An indication of the mean boundary interval of alternative maps is also given by the variogram model, although this should be treated with caution. The method proposed could therefore be useful when analysing observations from a reconnaissance soil survey, as an aid to selecting appropriate methods for more detailed survey.

Chemical Characterization of Soils of a Tropical Humid Forest Zone: A Methodology

AbstractA methodology, based on a combination of routinely performed analyses and investigation of fundamental charge and anion sorption properties, was used to characterize the soils of the humid forest zone of Cameroon. In general, the soils have about 2 cmol kg−1 permanent negative charge, with about 1 cmol kg−1 from variable‐charge sources at current soil pH values. Furthermore, they are improverished with respect to Ca, Mg, and K, while Al frequently dominates the exchange complex. Thus, the ability of these soils to retain base cations is more limited than is suggested by the cation‐exchange capacity (CEC). Therefore we propose the concept of a degradation index (DI) defined as: DI = 100(CEC5.5 − sum of basic cations)/CEC5.5, where CEC5.5 is the CEC measured at pH 5.5. This index encompasses degradation a soil may have experienced from natural or man‐made causes. Extractable PO4 concentrations are considered very low and the soils have a moderate to high capacity to fix added PO4. Surface soil SO4 concentrations are considered marginal to deficient for plant growth, though adequate reserves of SO4 are held in the subsoil by SO4 sorption. The approach used demonstrated that the five morphologically different soil profile classes identified in the zone have similar chemical characteristics. Thus, the results of experimentation conducted on one of the soil profile classes will be applicable throughout the zone. Furthermore, the approach has provided a means of identifying comparable soil types in other parts of the world and will guide technology transfer. The analytical methods used in this study are relatively simple and require no specialized equipment, and are therefore within the capabilities of many laboratories in the developing world.

A regular pattern in the relative areas of soil profile classes and possible applications in reconnaissance soil survey

The relative areas of soil profile classes are found to show an unexpected regularity in various landscapes (where relative area is defined as the proportion of the total soil-covered area of a region which is occupied by a particular soil class). If i classes are ordered by their relative areas ( A 1, A 2, …, A i ) then a plot of the cumulative relative area CA n (the sum of relative areas for classes 1 to n in the sequence) against A n , may be approximated by a straight line. The slope of this line, which must pass through the origin, may be estimated from the relative areas of the commoner soil classes which may be identified in a reconnaissance soil survey of a region. From the slope it is then possible to predict the total number of soil classes in the region whose relative areas exceed some threshold. This procedure is offered as a technique to aid reconnaissance soil survey, particularly where a surveyor must derive a soil classification from a set of samples obtained by a random sampling procedure. An estimate of the number of soil classes which occur may help in the planning of additional purposive sampling to produce a satisfactory classification on which to base a map legend.

Fuzzy classification methods for determining land suitability from soil profile observations and topography

SUMMARYBecause conventional Boolean retrieval of soil survey data and logical models for assessing land suitability treat both spatial units and attribute value ranges as exactly specifiable quantities, they ignore the continuous nature of soil and landscape variation and uncertainties in measurement which can result in the misclassification of sites that just fail to match strictly defined requirements. This paper uses fuzzy classification to determine land suitability from (i) multivariate point observations of soil attributes, (ii) topographically controlled site drainage conditions, and (iii) minimum contiguous areas, and compares the results obtained with conventional Boolean methods. The methods are illustrated using data from the Alberta Agricultural Department experimental farm at Lacombe in Alberta, Canada. Data on site elevation and soil chemical and physical properties measured at 154 soil profiles were interpolated by ordinary block kriging to 15 m × 15 m cells on a 50 × 50 grid. The soil property data for each cell were classified by Boolean and fuzzy methods. The digital elevation model created by interpolating the elevation data was used to determine the surface drainage network and map it in terms of the numbers of cells draining through each cell on the grid. This map was reclassified to yield Boolean and fuzzy maps of surface wetness which were then intersected with the soil profile classes. The resulting classification maps were examined for contiguity to locate areas where a block of minimum size (45m × 45m) could be located successfully. In this study Boolean methods reject larger numbers of cells than fuzzy classification, and select cells that are insufficiently contiguous to meet the aims of the land classification. Fuzzy methods produce contiguous areas and reject less information at all stages of the analyses than Boolean methods. They are much better than Boolean methods for classification of continuous variation, such as the results of the drainage analysis.

Fuzzy classification of soil profiles and horizons from the Lockyer Valley, Queensland, Australia

This paper examines the application of fuzzy classification and ordination to the characterization of soil units recognized in the alluvial plain of the Lockyer Valley in southeast Queensland, Australia. Soil profile classes and soil stratigraphic units were recognized initially using field criteria. Data from soil profiles and horizons at 133 equally spaced sites on a 3.4 km transect were analysed. The analyses aimed to assess how similar or separate profiles and horizons were regardless of their position along the transect, and to scrutinise field assumptions of a typical alluvial soil landscape sequence. Centroids of fuzzy groups (classes) were generated together with fuzzy group memberships for each profile or horizon. Distribution of fuzzy group membership on the transect and data patterns developed using ordination procedures were found to reflect changes in landscape position, soil profile classes, soil stratigraphic units and soil factors related to internal drainage. Analysis corroborated most soil stratigraphic units and profile classes recognized in the field but some profile classes were shown to be in need of re-examination. The distribution of horizon groups generated by fuzzy classification was generally unrelated to the distribution of profile fuzzy groups. It is suggested that for these soils it would be difficult to create a fixed number of profile classes based on homologous relationships between horizons. Classification of soil by horizons appears to be more meaningful than the classification of profiles but profile fuzzy group distribution along the transect was more mappable and easily interpreted than horizon fuzzy group distribution.

The application of fuzzy classification to soil pH profiles in the Lockyer Valley, Queensland, Australia

Summary A disadvantage of most numerical classifications of soil is the imposition of class boundaries for properties that in reality may vary continuously. Fuzzy classification has the advantage of allowing continuous membership of profiles to classes and can also cope with extragrading or unusual soil individuals. This paper examines the application of fuzzy classification to the pH profiles of soils in the alluvial plain of the Lockyer Valley, south-east Queensland in eastern Australia. The pH values at six fixed depths in 133 soil profiles on a 25 m spaced transect were used to generate the centroids of five fuzzy groups (classes) together with fuzzy group memberships for each pH profile. To estimate boundary positions between fuzzy groups along the transect, the digital gradient or degree of change between adjacent profiles was plotted. Results of the numerical analysis were compared with the distribution of soil profile classes recognised using field criteria. Fuzzy groups did not consistently relate to either soil profile classes or boundaries between profile classes. Distribution of fuzzy group membership on the transect was found to generally reflect changes in soil factors related to internal drainage, carbonate status, landscape position and irrigation regime. Several distinct soil profile classes with similar drainage status were commonly found to have high membership to the same fuzzy group. Fuzzy classification usefully demonstrated relationships between profiles among the transect and is a potentially valuable method for examining the variability of soil in complex landscapes.

Predictive relationships between pH and sodicity in soils of tropical Queensland

Abstract Interrelations between soil pH and exchangeable sodium percentage (ESP) were examined using soils from the Burdekin River area in tropical Queensland. Highly significant correlations were found but the goodness of fit differed between groups of soil profile classes. In general, Typic Natrustalfs of the flood plains had better relationships (r2 = 0.85) between these soil properties than did the Chromusterts (r2 = 0.50). The regression ESP = 1.935 × 10‐5 pH6.205 (r2 = 0.61; n= 288) for all soils and depths underestimated ESP in Typic Natrustalfs groupings and overestimated this soil property in the Chromusterts. By using the appropriate regression, pH levels associated with non‐sodic, sodic and strongly sodic horizons have been defined. Either laboratory or field determined estimates of pH may be used but the laboratory determined value is preferred. It is expected the predictive models will remain valid until soil ESP or pH levels are significantly modified as a consequence of agricultural develop...

Calculation of Steady State Capillary Rise from the Groundwater Table in Multi‐Layered Soil Profiles

AbstractA computer program CRISP is presented to calculate steady state capillary rise from the groundwater table in single‐ and multi‐layered soil profiles. The calculation is based on an integration of Darcy's equation and uses k‐v functions, described with a modified formula of Brooks and Corey (1964). The three constants in this formula can be evaluated from texture and organic matter content, accounting for hysteresis (Bloemen, 1980).Application of the program CRISP shows that the heights of capillary rise from the groundwater table in single‐layered soil profiles may be largely different. Of more interest for practical matters is capillary rise in multi‐layered soil profiles, which may be strongly influenced by the depth of the groundwater table. The deepest admissable level of the groundwater table that guarantees a certain water supply to the root zone appears to vary widely for different soil profiles. This can be the starting point for a classification of soil profiles on the basis of their potentialities for capillary water supply. Characteristic textures and humus contents of upper soils and subsoils of different geo‐genetical groups may thus become distinctive features. Possibly a subdivision of these groups is necessary. The depths of changes between distinguished layers are significant. The actual range of variations of the depth of the groundwater table should play part in a soil classification.