Spatial prediction of soil properties using environmental correlation

McKenzie, N. J.; Ryan, Philip

doi:10.1016/s0016-7061(98)00137-2

Cited by 556 publications

(283 citation statements)

References 25 publications

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“…In the next phase, the predictors were extended to a set of environmental variables and remote sensing images. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

902

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%

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

Hengl¹,

Heuvelink

Stein³

2004

Geoderma

902

558

View full text Add to dashboard Cite

show abstract

“…Recent studies using linear models, particularly multi-linear regression models, have revealed the validity of estimating a soil property from surrogates or easy-to-measure morphological properties (McKenzie and MacLeod, 1989;McKenzie et al, 1991;Manrique et al, 1991;Brubaker et al, 1994;Odeh et al, 1994;McKenzie and Ryan, 1999). For instance, Moore et al (1993) and Hontoria et al (1999) have used the stepwise regression method including a combination of forward stepwise and backward elimination procedures in estimating soil attributes with terrain analysis, and in examining the relationship between soil organic carbon and site characteristics, respectively.…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China

Yang

Wang

et al. 2003

CATENA

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Three basic multiple-linear regression models with different sets of more readily observed environmental variables (land use, topographic and meteorological factors) were developed for the prediction of soil moisture content in space and time. The model performances were evaluated in the Danangou catchment (3.5 km 2 ) on the Loess Plateau, China, with soil moisture content measurements. The soil moisture content measurements were performed biweekly at five depths in the soil profile (0 -5, 10 -15, 20 -25, 40 -45 and 70 -75 cm) from May to October 1998 and from May to September 1999, using a Delta-T theta probe. It was indicated that the regression models could describe the relationships of soil moisture content with environmental attributes. It was found that the spatiotemporal-SM model showed the best goodness of fit since it explained the greatest fraction of soil moisture variation in both space and time, and the predicted mean, standard deviation, minimum and maximum soil moisture were closest to the observed values. This model was also either the most precise or the most economical in prediction of soil moisture content in space and time since it gives the lowest values in mean absolute error of prediction (MAE) and Akaike information criterion (AIC). There is little difference in performance and cost-benefit between the spatial-GM model and landuse-GM model. The superior robustness of the spatiotemporal-SM model over the other two models is most significant in the prediction of soil moisture content at 0 -5 cm, and decreases with increasing soil depth. D

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“…In addition to soil mapping capabilities of radiometrics, it has the potential to provide a more cost-effective alternative than actually measuring the soil property of interest (McKenzie and Ryan, 1999). Radiometrics had the highest perceived land management benefit at the catchment scale through a soil map when compared to other geophysical based products (George et al, 1998(George et al, , 2000.…”

Section: Discussionmentioning

confidence: 99%

A strategy to extract information from airborne gamma ray spectrometry for applications in plant based industries

Pracilio¹,

Smettem²,

Street³

2006

ASEG Extended Abstracts

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Spatial prediction of soil properties using environmental correlation

Cited by 556 publications

References 25 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

Spatiotemporal prediction of soil moisture content using multiple-linear regression in a small catchment of the Loess Plateau, China

A strategy to extract information from airborne gamma ray spectrometry for applications in plant based industries

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