On digital soil mapping

McBratney, Alex B.; Mendonça-Santos, Maria de Lourdes; Minasny, Budiman

doi:10.1016/s0016-7061(03)00223-4

Cited by 2,792 publications

(1,756 citation statements)

References 213 publications

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“…In this study, covariates of the 'scorpan' model (McBratney et al, 2003) were used related to the soil relief (r) and organism (o) formation factors. The organism factor was represented by the standard deviation covariate of the Normalized Difference Vegetation Index (NDVI), hereinafter called STAN.…”

Section: Predictive Covariatesmentioning

confidence: 99%

“…The 'scorpan' model (McBratney et al, 2003) assumes that existing information about soil classes and properties can assist in the prediction and digital soil mapping (DSM) in areas where spatial information of soils is missing or unavailable at the scale required. According to Qi & Zhu (2003), the basic idea of formalization of the soil-landscape relationships contained in choropleth soil maps consists of recovering pedological knowledge contained in the map by application of data mining techniques.…”

Section: Introductionmentioning

confidence: 99%

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Digital soil mapping: strategy for data pre-processing

Caten

Dalmolin

Ruiz

2012

Rev. Bras. Ciênc. Solo

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SUMMARYThe region of greatest variability on soil maps is along the edge of their polygons, causing disagreement among pedologists about the appropriate description of soil classes at these locations. The objective of this work was to propose a strategy for data pre-processing applied to digital soil mapping (DSM). Soil polygons on a training map were shrunk by 100 and 160 m. This strategy prevented the use of covariates located near the edge of the soil classes for the Decision Tree (DT) models. Three DT models derived from eight predictive covariates, related to relief and organism factors sampled on the original polygons of a soil map and on polygons shrunk by 100 and 160 m were used to predict soil classes. The DT model derived from observations 160 m away from the edge of the polygons on the original map is less complex and has a better predictive performance.

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Section: Predictive Covariatesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Digital soil mapping: strategy for data pre-processing

Caten

Dalmolin

Ruiz

2012

Rev. Bras. Ciênc. Solo

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“…Several approaches have been used for fitting quantitative relationships between soil types and/or properties and their environment in order to predict their spatial distribution and variability (spatial inference models) (McBratney et al, 2003). Such models are divided into data-driven (Pedometric approach) and knowledge-driven (Shi et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Menezes

Silva

Mello

et al. 2014

Sci. agric. (Piracicaba, Braz.)

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Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledgebased digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soillandscape relationship explicit.

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“…information on land use, rainfall or variables derived from a digital elevation model). This is what McBratney et al [12] call the SCORPAN approach to spatial prediction of soil properties, using covariates (categorical or continuous) as predictors. Incorporating the SCORPAN predictors as fixed effects in a linear mixed model allows the remaining variation in the target soil properties to be modelled as spatially dependent random effects.…”

Section: Linear Mixed Models: Soil Knowledge In the Fixed Effectsmentioning

confidence: 97%

Towards soil geostatistics

Lark

2012

Spatial Statistics

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In a brief survey of some issues in the application of geostatistics in soil science it is shown how the recasting of classical geostatistical methods in the linear mixed model (LMM) framework has allowed the more effective integration of soil knowledge (classifications, covariates) with statistical spatial prediction of soil properties. The LMM framework has also allowed the development of models in which the spatial covariance need not be assumed to be stationary. Such models are generally more plausible than stationary ones from a pedological perspective, and when applied to soil data they have been found to give prediction error variances that better describe the uncertainty of predictions at validation sites. Finally consideration is given to how scientific understanding of variable processes in the soil might be used to infer the likely statistical form of the observed soil variation.

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On digital soil mapping

Cited by 2,792 publications

References 213 publications

Digital soil mapping: strategy for data pre-processing

Digital soil mapping: strategy for data pre-processing

Solum depth spatial prediction comparing conventional with knowledge-based digital soil mapping approaches

Towards soil geostatistics

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