An overview of pedometric techniques for use in soil survey

McBratney, Alex B.; Odeh, Inakwu O. A.; Bishop, Thomas; Dunbar, Marian S.; Shatar, Tamara M.

doi:10.1016/s0016-7061(00)00043-4

Cited by 496 publications

(277 citation statements)

References 43 publications

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“…This approach was termed ''environmental correlation'' by McKenzie and Ryan (1999), or spatial prediction by multiple regression with auxiliary variables (Odeh et al, 1994(Odeh et al, , 1995. McBratney et al (2000) coined the term CLORPT techniques. Geostatistics and the CLORPT techniques are two somewhat distinct approaches to spatial prediction and can both give satisfactory results independently one from another.…”

Section: Introductionmentioning

confidence: 99%

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A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

914

558

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A methodological framework for spatial prediction based on regression-kriging is described and compared with ordinary kriging and plain regression. The data are first transformed using logit transformation for target variables and factor analysis for continuous predictors (auxiliary maps). The target variables are then fitted using step-wise regression and residuals interpolated using kriging. A generic visualisation method is used to simultaneously display predictions and associated uncertainty. The framework was tested using 135 profile observations from the national survey in Croatia, divided into interpolation (100) and validation sets (35). Three target variables: organic matter, pH in topsoil and topsoil thickness were predicted from six relief parameters and nine soil mapping units. Prediction efficiency was evaluated using the mean error and root mean square error (RMSE) of prediction at validation points. The results show that the proposed framework improves efficiency of predictions. Moreover, it ensured normality of residuals and enforced prediction values to be within the physical range of a variable. For organic matter, it achieved lower relative RMSE than ordinary kriging (53.3% versus 66.5%). For topsoil thickness, it achieved a lower relative RMSE (66.5% versus 83.3%) and a lower bias than ordinary kriging (0.15 versus 0.69 cm). The prediction of pH in topsoil was difficult with all three methods. This framework can adopt both continuous and categorical soil variables in a semi-automated or automated manner. It opens a possibility to develop a bundle algorithm that can be implemented in a GIS to interpolate soil profile data from existing datasets. D

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Section: Introductionmentioning

confidence: 99%

“…Here, two main paths can be recognised: co-kriging and kriging combined with regression (McBratney et al, 2000). The latter path was shown to be more attractive for combination of kriging and CLORPT techniques, among others because fewer model parameters need to be estimated (Knotters et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

A generic framework for spatial prediction of soil variables based on regression-kriging

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

914

558

View full text Add to dashboard Cite

show abstract

“…Soil variability has been extensively studied, approached from both a theoretical, modeling basis (Heuvelink and Webster 2001;McBratney et al 2000) and by evaluation of variability for specific soil properties based on scale of mapping (Leenhardt et al 1994). Geochemical variability by soil order has been evaluated (Ma et al 1997;Chen et al 2002;Burt et al 2003b) and geochemical variability between different series has been documented in statewide studies.…”

Section: Trace Element Variation Within Seriesmentioning

confidence: 99%

Geochemistry in the modern soil survey program

Wilson¹,

Burt²,

Indorante³

et al. 2007

Environ Monit Assess

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“…Digital soil mapping (DSM) provides an effective way to obtain high-precision and high-resolution soil distribution information (McBratney et al, 2003). Because it requires complicated soil inference models (McBratney et al, 2000) and tedious data preparation processes (e.g., RS and GIS data [Ryan and McKenize, 1999;Dobos et al, 2000;Mulder et al, 2011]), DSM typically requires software tools.…”

Section: Introductionmentioning

confidence: 99%

CyberSoLIM: A cyber platform for digital soil mapping

et al. 2016

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In recent years, new demands and trends have emerged in the digital soil mapping (DSM) field: the range of applications as well as the range of users has become much diverse. Users of DSM include not only experts in soil science community but also those from relevant domains (e.g., hydrology, ecology). In addition, the rapid expansion of areas for DSM and the ever increasing spatial resolution of covariates call for an accelerated level of computation. These new trends have raised the bar for DSM software platforms. This paper presents CyberSoLIM, a prototype system to illustrate an idea of easy-of-use and high performance enabled cyber environment for DSM. CyberSoLIM is implemented to have five main features: (1) heuristic modeling, which allows digital soil mappers to construct DSM workflow easily; (2) visualized modeling with which the conceptual workflow of DSM is expressed by graphic icons; (3) workflow reuse, which increases the efficiency of DSM deployment; (4) online execution and high-performance computing, which can use the advantage of cyber infrastructure and high-performance computing; and (5) web service enabled, which provides effective and easy means to share and integrate models and algorithms. As an illustration of such environment, CyberSoLIM was used to infer the silt content in topsoil (0-20 cm) in China's Anhui Province. The case study confirms that software platform as illustrated by CyberSoLIM is easy to use and efficient in terms of mapping productivity.

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An overview of pedometric techniques for use in soil survey

Cited by 496 publications

References 43 publications

A generic framework for spatial prediction of soil variables based on regression-kriging

A generic framework for spatial prediction of soil variables based on regression-kriging

Geochemistry in the modern soil survey program

CyberSoLIM: A cyber platform for digital soil mapping

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