Soil information on soil profiles described in the field, with different degrees of detail, both of the place where the observation is made, and of the profile described. It includes its composition in horizons, morphological description and physical-chemical characterization, such as the type of soil.

The soil profile data files are generated from the cartography program of the Soil map (MS25M) and the 1:250.000 Soil map of Catalonia (MSC250M). This information constitutes the basis for the preparation of the soil cartography and nourishes the soil catalog that defines the main types of soils in Catalonia.

The main objective of this geoinformation is to facilitate the study of soils, as well as to disseminate their constitution and composition. Thanks to this data, it is possible to analyze the diversity of soils, understand the processes and forming factors, discover their functions and understand and contextualize their uses in the territory. In addition, it provides a solid reference base for soil mapping, essential for territorial planning projects, environmental management or scientific research.

Last update

• Publication date: 12/06/2025.
• Information date: 08/06/1990 - 19/11/2015.
• Version: v1.0.
• Data: 3419 records.

Cartography subject to a Creative Commons Attribution 4.0 International license  More information

Description

A soil profile is the vertical description of the different layers -called horizons- that make up a soil, from the surface to the bedrock, a minimum of 1.5 m deep (soil cores) or as far as the cut allows (observations). This profile reflects the evolution of the soil over time, influenced by forming processes such as weathering, decomposition of organic matter, water movement and biological activity.

The methodology for the location, description of the observations, specification of the description criteria and classification standards used are detailed in the Guide for the drafting of soil mapping projects at a scale of 1:25,000. 

The preparation process follows the following steps:

• Direct field observations (soil cores and soil cores).
• Registration of more than 70 attributes per horizon according to the standards of the Guide.
• Data validation and quality control (attribute consistency, error detection).
• Structuring in CSV format compatible with GIS.
• Verification of the relationship between profiles and horizons.
• The resulting data represent the state of the soil at the time of sampling.

The result is a data set structured in two related tables:

• Profiles: They represent the soil observation points. Each profile is described by more than fifty attributes, which include information on the geographical location, geological context, environmental conditions (humidity, vegetation), land use, among others.
• Horizons: They are the differentiated layers of the soil, characterized by their physical, chemical and biological properties resulting from the forming processes. Each horizon constitutes the unit of description and sampling within a profile, and is characterized by more than seventy detailed attributes, both morphological and physicochemical data.

The data is distributed in CSV format, organized in two files (one for the profiles and the other for the horizons) compatible with GIS environments for spatial visualization and analysis. The relationship between profiles and horizons is established through the 'Reference' attribute, which allows each horizon to be uniquely related to its corresponding profile.

Technical characteristics

This geoinformation is distributed in CSV format.

Positional accuracy: A root mean square error (RMSE) of ≤50 m and a maximum horizontal position error (MHPE) of ≤100 m are estimated.

The coordinate system used is the official one in Catalonia (ETRS89 UTM 31 North) and the altitudes refer to the mean sea level in Alicante.

Applications of Soil profile data modeling in Catalonia

Soil profile data is not only useful for the knowledge and distribution of soils in the territory. They constitute the essential basis for various types of data modeling. These data allow the application of a wide variety of models and techniques ranging from simple empirical models (RUSLE) to process-based dynamic simulations (RothC, Century, DNDC), digital cartography (DSM), soil property prediction (PTF), pollutant transport (HYDRUS) or crop growth (DSSAT).

Modeling can be classified according to the following scientific criteria:

• Empirical and simplified models:
  • RUSLE: Estimation of soil loss due to water erosion.
  • PTF (Edaphotransfer Functions): Prediction of soil properties (physical, chemical, hydraulic) from properties that are easier to measure.
• Models based on dynamic processes:
  • RothC, Century, DNDC, Daycent, ICBM: Simulation of organic carbon dynamics, nutrients and GHG emissions.
  • D-RUSLE: Dynamic version of RUSLE with seasonal variability.
  • HYDRUS-1D/2D/3D: Water, heat and pollutant transport in vadose zone.
  • DSSAT, APSIM: Crop growth, agricultural yield.
  • DRAINMOD: Agricultural drainage and water table dynamics.
• Spatial prediction models (Digital Cartography):
  • DSM (Digital Soil Mapping) with Geostatistics: Kriging, Co-kriging.
  • DSM with Machine Learning: Random Forest, Gradient Boosting, Neural Networks.

Soil test pits and sobservations [Catalan]

Main model references

• APSIM. Keating, B. A., et al. (2003). An overview of APSIM, a model designed for farming systems simulation. European Journal of Agronomy, 18(3-4), 267-288.
• Century. Parton, W. J., et al. (1987). Analysis of factors controlling soil organic matter levels in Great Plains grasslands. Soil Science Society of America Journal, 51(5), 1173-1179.
• Daycent. Parton, W. J., et al. (1998). DayCent: Daily timestep carbon and nitrogen models. Global Change and Terrestrial Ecosystems, 5, 439-443.
• DNDC. Li, C., et al. (1992). A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity. Journal of Geophysical Research: Atmospheres, 97(D9), 9759-9776.
• DRAINMOD. Skaggs, R. W. (1980). Methods for design and evaluation of drainage-water management systems for soils with high water tables. DRAINMOD Reference Report.
• DSM. McBratney, A. B., et al. (2003). On digital soil mapping. Geoderma, 117(1-2), 3-52.
• DSSAT. Jones, J. W., et al. (2003). The DSSAT cropping system model. European Journal of Agronomy, 18(3-4), 235-265.
• Geoestadística. Goovaerts, P. (1997). Geostatistics for Natural Resources Evaluation. Oxford University Press.
• HYDRUS. Šimůnek, J., van Genuchten, M. Th., & Šejna, M. (2012). HYDRUS: Model use, calibration, and validation. Transactions of the ASABE, 55(4), 1261-1274.
• ICBM. Andrén, O., & Kätterer, T. (1997). ICBM: The Introductory Carbon Balance Model for exploration of soil carbon balances. Ecological Applications, 7(4), 1226-1236.
• PTF. Bouma, J. (1989). Using soil survey data for quantitative land evaluation. Advances in Soil Science, 9, 177-213.
• RothC. Coleman, K., & Jenkinson, D. S. (1996). RothC-26.3: A model for the turnover of carbon in soil. A: Evaluation of Soil Organic Matter Models (pp. 237-246). Springer.
• RUSLE. Renard, K. G., et al. (1997). Predicting soil erosion by water: a guide to conservation planning with the Revised Universal Soil Loss Equation (RUSLE). USDA Agricultural Handbook No. 703.