Elsevier

Geoderma

Volume 152, Issues 3–4, 15 September 2009, Pages 195-207
Geoderma

Review
Multi-criteria characterization of recent digital soil mapping and modeling approaches

https://doi.org/10.1016/j.geoderma.2009.06.003Get rights and content

Abstract

The history of digital soil mapping and modeling (DSMM) is marked by adoption of new mapping tools and techniques, data management systems, innovative delivery of soil data, and methods to analyze, integrate, and visualize soil and environmental datasets. DSMM studies are diverse with specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions. Research-focused DSMM contrasts with need-driven DSMM and agency-operated soil surveys. Since there is no universal equation or digital soil prediction model that fits all regions and purposes the proposed strategy is to characterize recent DSMM approaches to provide recommendations for future needs at local, national and global scales. Such needs are not solely soil-centered, but consider broader issues such as land and water quality, carbon cycling and global climate change, sustainable land management, and more. A literature review was conducted to review 90 DSMM publications from two high-impact international soil science journals — Geoderma and Soil Science Society of America Journal. A selective approach was used to identify published studies that cover the multi-factorial DSMM space. The following criteria were used (i) soil properties, (ii) sampling setup, (iii) soil geographic region, (iv) spatial scale, (v) distribution of soil observations, (vi) incorporation of legacy/historic data, (vii) methods/model type, (viii) environmental covariates, (ix) quantitative and pedological knowledge, and (x) assessment method. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modeling programs, research gaps, and future trends are discussed. Modeling of soils in 3D space and through time will require synergistic strategies to converge environmental landscape data and denser soil datasets. There are needs for more sophisticated technologies to measure soil properties and processes at fine resolution and with accuracy. Although there are numerous quantitative models rooted in factorial models that predict soil properties with accuracy in select geographic regions they lack consistency in terms of environmental input data, soil properties, quantitative methods, and evaluation strategies. DSMM requires merging of quantitative, geographic and pedological expertise and all should be ideally in balance.

Introduction

Soil properties are continuously modified by internal factors and anthropogenic impacts generating complex spatial soil patterns. Our understanding and ability to describe them have undergone tremendous change as outlined by Hartemink and McBratney (2008) who argued that we are in an age called “soil science renaissance”. Introduction of digital technologies, such as remote and soil sensing, computer processing speed, management of spatial data, quantitative method to describe soil patterns and processes, and scientific visualization methods have provided new opportunities to predict soil properties and processes. The history of digital soil mapping and modeling (DSMM) is marked by adoption of new tools and techniques to analyze, integrate, and visualize soil and environmental datasets (Grunwald, 2006).

To quantify soil patterns mapping or modeling methods can be used. Modeling is defined as “use of mathematical equations to simulate and predict real events and processes”, whereas mapping emphasizes to “make a map” or “to depict something on a map”. Digital soil mapping (DSMa) [or predictive soil mapping] can be defined as “computer-assisted production of digital representations of soil type or soil properties, which involve the creation and population of spatially-explicit information by the use of field and laboratory methods, coupled with spatial and non-spatial soil inference systems”. A comprehensive review of DSMa was provided in McBratney et al. (2003) and an overview of pedometric techniques used in DSMa by McBratney et al. (2000) and Grunwald (2006).

To predict soil properties and classes the SCORPAN approach (Eq. (1)) has been formulated by McBratney et al. (2003) rooted in earlier works by H. Jenny and V.V. Dokuchaev.Sa[x,y,~t]orSc[x,y,~t]=f(s[x,y,~t],c[x,y,~t],o[x,y,~t],r[x,y,~t],p[x,y,~t],a[x,y,~t],n)where

    Sa

    soil attribute

    Sc

    soil class

    s

    soils, other attributes of the soil at a point

    c

    climate factor

    o

    organisms, vegetation or fauna or human activity

    r

    relief (topography, landscape attributes)

    p

    parent material, lithology

    a

    age, the time factor

    n

    space, spatial position

    t

    time (where t is defined as an approximate time).

Digital soil modeling (DSMo) is more comprehensive than DSMa because it entails description and/or prediction of soils in space and through time usually implemented using mechanistic simulation models. Pedometrics integrates DSMa and DSMo into a spatially and temporally explicit framework merging soil science, environmental science, GIScience, cartography, statistics, geostatistics, and mathematics for the study of the distribution and genesis of soils.

Hartemink et al. (2001) noted a strong decline in soil mineralogy, soil morphology and soil genesis research in comparison to a strong increase in pedometrics applications from 1967 to 2001. Historically, soil science has been rooted in agriculture, geology, and chemistry. But the emphasis has shifted from classification and inventory to understanding and quantifying spatially and temporally soil patterns in relation to hydrologic cycle and ecosystems health. This environmental-centered approach views soils as integral part of an ecosystem interacting with environmental factors generating complex patterns and processes that co-evolve through time.

In this paper a comprehensive review of DSMM is provided covering a wide range of specialized, mathematical prototype models tested on limited geographic regions and/or datasets and simpler, operational DSMM used for routine mapping over large soil regions.

The aim of this study was to assess the usefulness of recent DSMM approaches to meet specific needs at local, national, and global scales. Such needs are not solely soil-centered, but consider broader issues such as soil and water quality, food security, carbon cycling and sequestration, global climate change, sustainable land management, and more. Strengths and weaknesses of current DSMM, their potential to be operationalized in soil mapping/modeling programs, research gaps, and future trends are discussed.

Section snippets

Materials and methods

Publications of two high-impact and international soil science journals – Geoderma and Soil Science Society of America Journal – were reviewed covering the period 2007–2008 to identify studies focused on DSMM. The Soil Sci. Soc. Am. J. Issues 2007 Vol. 71 No. 6 to 2008 Vol. 72 No. 5 and Geoderma 2008 Vol. 140 Issue 4 to Vol. 146 Issues 1–2 with a total number of 90 papers were considered in the analysis. A selective approach was used to identify published DSMM studies that cover the

Prediction of soil properties and classes at plot/field and coarse landscape scales

Prediction of soil properties and classes at plot/field scale encompassed 35.6% (N: 32) of all reviewed studies, of which 87.5% (N: 28) predicted soil properties and 12.5% (N: 4) soil classes (Table 1). A total of 31.3% (N: 10) of plot/field scale studies used a mechanistic modeling approach (DSMo) to predict soil properties. For example, Bricklemyer et al. (2007) employed the CENTURY model and Causarano et al. (2007) EPIC to model soil organic carbon. At coarser landscape scales a total of

External and internal factors imparting control on soil properties and classes

Most DSMM studies focused on external drivers (controlling factors) such as climate, vegetation, and land use that modulate SOC, TP or other soil properties that were predicted (compare Rivero et al. (2007), Grimm et al. (2008), and Schulp and Veldkamp (2008)). Environmental factors such as land cover and terrain can be rapidly and cost-effectively mapped using remote sensing and digital elevation models covering large areas. But these models did not incorporate intrinsic soil properties such

Conclusion and outlook

A holistic understanding of soil patterns and processes is critical because soils are the matrix which all transport and transformation processes must pass. Scaling of soil properties and processes to an appropriate scale to address societal needs and evaluate ecosystem services will be critical in the future. DSMM can contribute to enhancing the decision making process to resolve local, national, and global environmental problems of significance by providing accurate soil predictions that

Notes

The ranking of DSMM studies into different categories was done to the best knowledge and understanding of the author. High, average or low scores were intended to classify a given study relative to other DSMM studies and there was no intention to reflect on the quality of a specific paper.

Acknowledgements

I would like to thank Dr. Janis Boettinger (organizer and host) and the program committee who invited me to give a keynote talk at the 3rd Global Conference on Digital Soil Mapping organized by the International Union of Soil Sciences, Soil Science Society of America and Utah State University, Sept. 30–Oct. 3, 2008, Logan, UT. This provided the stimulus to conduct the review analysis on DSMM presented in this manuscript. I am very grateful for their encouragement. I would also like to thank Bob

References (112)

  • BrownD.J. et al.

    Global soil characterization with VNIR diffuse reflectance spectroscopy

    Geoderma

    (2006)
  • CarraraM. et al.

    Mapping of penetrometer resistance in relation to tractor traffic using multivariate geostatistics

    Geoderma

    (2007)
  • CorstanjeR. et al.

    Inferences from fluctuations in the local variogram about the assumptions of stationarity in the variance

    Geoderma

    (2008)
  • DuC. et al.

    Identification of agricultural soils using mid-infrared photoacoustic spectroscopy

    Geoderma

    (2008)
  • FariftehJ. et al.

    Spectral characteristics of salt-affected soils: a laboratory experiment

    Geoderma

    (2008)
  • FinkeP.A. et al.

    Modelling soil genesis in calcareous loess

    Geoderma

    (2008)
  • GenxuW. et al.

    Effects of permafrost thawing on vegetation and soil carbon pool losses on the Qinghai — Tibet Plateau, China

    Geoderma

    (2008)
  • GomezC. et al.

    Soil organic prediction by hyperspectral remote sensing and field VIS-NIR spectroscopy: an Australian case study

    Geoderma

    (2008)
  • GrimmR. et al.

    Soil organic carbon concentrations and stocks on Barro Colorado Island — digital soil mapping using Random Forest analysis

    Geoderma

    (2008)
  • GrinandC. et al.

    Extrapolating regional soil landscapes from an existing soil map: sampling intensity, validation procedures, and integration of spatial context

    Geoderma

    (2008)
  • GrunwaldS. et al.

    Understanding spatial variability and its application to biogeochemistry analysis (chapter 20)

  • GrunwaldS. et al.

    Temporal trajectories of phosphorus and pedo-patterns mapped in Water Conservation Area 2, Everglades, Florida, USA

    Geoderma

    (2008)
  • De GryzeS. et al.

    The relationship between landform and the distribution of soil C, N and P under conventional and minimum tillage

    Geoderma

    (2008)
  • HarteminkA.E. et al.

    A soil science renaissance

    Geoderma

    (2008)
  • HarteminkA.E. et al.

    Developments and trends in soil science 100 volumes of Geoderma (1967–2001)

    Geoderma

    (2001)
  • HenglT. et al.

    Methods to interpolate soil categorical variables from profile observations: lessons from Iran

    Geoderma

    (2007)
  • HillJ. et al.

    Mapping complex patterns of erosion and stability in dry Mediterranean ecosystems

    Remote Sens. Environ.

    (2000)
  • JordanovaN. et al.

    Application of magnetometry for delineation of anthropogenic pollution in areas covered by various soil types

    Geoderma

    (2008)
  • KazemiH.V. et al.

    Spatial variability of bromide and atrazine transport parameters for a Udipsamment

    Geoderma

    (2008)
  • KooistraL. et al.

    The potential of field spectroscopy for the assessment of sediment properties in river floodplains

    Anal. Chim. Acta

    (2003)
  • LiuJ. et al.

    Mapping within-field soil drainage using remote sensing, DEM and apparent soil electrical conductivity

    Geoderma

    (2008)
  • LuJ. et al.

    Assessing soil quality data by positive matrix factorization

    Geoderma

    (2008)
  • LufafaA. et al.

    Carbon stocks and patterns in native shrub communities of Senegal's Peanut Basin

    Geoderma

    (2008)
  • MabitL. et al.

    Spatial variability of erosion and soil organic matter content estimated from 137Cs measurements and geostatistics

    Geoderma

    (2008)
  • MacMillanR.A. et al.

    Automated predictive ecological mapping in a forest region of B.C., Canada, 2001–2005

    Geoderma

    (2007)
  • MarchantB.P. et al.

    The Matérn variogram model: implications for uncertainty propagation and sampling in geostatistical surveys

    Geoderma

    (2007)
  • McBratneyA.B. et al.

    An overview of pedometric techniques for use in soil survey

    Geoderma

    (2000)
  • McBratneyA.B. et al.

    On digital soil mapping

    Geoderma

    (2003)
  • MinasnyB. et al.

    Spatial prediction of soil properties using EBLUP with the Matérn covariance function

    Geoderma

    (2007)
  • MinasnyB. et al.

    Incorporating taxonomic distance into spatial prediction and digital mapping of soil classes

    Geoderma

    (2007)
  • NyssenJ. et al.

    Spatial and temporal variation of soil organic carbon stocks in a lake retreat area of the Ethiopian Rift Valley

    Geoderma

    (2008)
  • OuimetR.

    Using compositional change within soil profiles for modeling base cation transport and chemical weathering

    Geoderma

    (2008)
  • ReindsG.J. et al.

    Bayesian calibration of the VSD soil acidification model using European forest monitoring data

    Geoderma

    (2008)
  • ReynoldsW.D. et al.

    Optimal soil physical quality inferred through structured regression and parameter interactions

    Geoderma

    (2008)
  • RiveroR.G. et al.

    Incorporation of spectral data into multivariate geostatistical models to map soil phosphorus variability in a Florida wetland

    Geoderma

    (2007)
  • SadeghiS.H.R. et al.

    Development, evaluation and interpretation of sediment rating curves for a Japanese small mountainous reforested watershed

    Geoderma

    (2008)
  • SantraP. et al.

    Pedotransfer functions for soil hydraulic properties developed from a hilly watershed of Eastern India

    Geoderma

    (2008)
  • SchulpC.J.E. et al.

    Long-term landscape–land use interactions as explaining factor for soil organic matter variability in Dutch agricultural landscapes

    Geoderma

    (2008)
  • SommerM. et al.

    Modelling soil landscape genesis — a “time split” approach for hummocky agricultural landscapes

    Geoderma

    (2008)
  • StevensA. et al.

    Laboratory, field and airborne spectroscopy for monitoring organic carbon content in agricultural soils

    Geoderma

    (2008)
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