Prediction of soil properties by digital terrain modelling

Florinsky, Igor V.; Eilers, R. G.; Manning, G.; Fuller, L. G.

doi:10.1016/s1364-8152(01)00067-6

Cited by 305 publications

(185 citation statements)

References 27 publications

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“…Note that there are three more aspects of soil variability that also play a role: temporal variability, depth (3D) and support size (Florinsky et al, 2002). Spatial prediction of PH was probably limited due to measurement errors, high .…”

Section: Conclusion and Discussionmentioning

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: Conclusion and Discussionmentioning

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

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“…These indices describe the spatial variability Article of specific processes, which are occurring in the watershed, such as soil water content or the potential for erosion (Moore et al, 1993). Some of the secondary categories used in this study are stream power index (Florinsky et al, 2002), wetness index and sediment transport index (Moore et al, 1993;Wilson and Gallant, 2000), the D8 (deterministic eight-node) algorithm and its derivatives (D8 contributing area, D8 slope, D8 distance to stream, D8 longest upslope length, D8 total upslope length, D8 slope averaged and D8 flow direction grid), and finally D∞ algorithm and its derivatives (D∞ flow direction and D∞ slope) (Tarboton, 1997;Tesfa et al, 2009;Hass, 2010) (Table 1).…”

Section: Data Derived From Digital Elevation Modelmentioning

confidence: 99%

Soil depth modelling using terrain analysis and satellite imagery: the case study of Qeshlaq mountainous watershed (Kurdistan, Iran)

Zahedi

Shahedi

Rawshan

et al. 2017

J Agricult Engineer

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Soil depth is a major soil characteristic, which is commonly used in distributed hydrological modelling in order to present watershed subsurface attributes. This study aims at developing a statistical model for predicting the spatial pattern of soil depth over the mountainous watershed from environmental variables derived from a digital elevation model (DEM) and remote sensing data. Among the explanatory variables used in the models, seven are derived from a 10 m resolution DEM, namely specific catchment area, wetness index, aspect, slope, plan curvature, elevation and sediment transport index. Three variables landuse, NDVI and pca1 are derived from Landsat8 imagery, and are used for predicting soil depth by the models. Soil attributes, soil moisture, topographic curvature, training samples for each landuse and major vegetation types are considered at 429 profiles within four subwatersheds. Random forests (RF), support vector machine (SVM) and artificial neural network (ANN) are used to predict soil depth using the explanatory variables. The models are run using 336 data points in the calibration dataset with all 31 explanatory variables, and soil depth as the response of the models. Mean decrease permutation accuracy is performed on Variable selection. Testing dataset is done with the model soil depth values at testing locations (93 points) using different efficiency criteria. Prediction error is computed for both the calibration and testing datasets. Results show that the variables landuse, specific surface area, slope, pca1, NDVI and aspect are the most important explanatory variables in predicting soil depth. RF and SVM models are appropriate for the mountainous watershed areas that have been limited in the depth of the soil and ANN model is more suitable for watershed with the fields of agricultural and deep soil depth.

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“…These phenomena are also affected by plant coverage that intercepts raindrops, provides a protective cover of leaf litter, binds soil with roots, and decreases runoff velocity. Topographic attributes and vegetation cover affect soil moisture in the uppermost soil horizons as they exert some influence on infiltration [11].…”

Section: Estimating Soil Depthmentioning

confidence: 99%

“…the hydrologically active zone of soil. Florinsky et al [11] split a study area into three major parts depending on the relevant topographic attributes: accumulation zones, dissipation zones, and transit zones. This splitting makes easier the analyses of different hydrologic features and enables us to quantify the influence exerted by topographic attributes on soil depth and soil water content variations.…”

Section: Introductionmentioning

confidence: 99%

A Simplified Approach for Determining Hydrologic Behavior and Depth of Soils at Basin Scale

Carriero¹,

Romano²,

Fiorentino³

2007

J Agricult Engineer

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Land-surface models at basin scale usually employ various combinations of land-use and soil class data to parameterize the major hydrologic processes. Even though soil exerts a major influence on hydrologic processes, soil hydraulic behavior is still difficult to quantify at relatively large scales. Recently soil hydraulic parameterization for hydrologic modeling can be conveniently carried out using pedotransfer rules, which are based on the knowledge of basic soil physical and chemical variables, such as bulk density, textural composition, and organic matter content. However, the availability of this basic information can be rather limited over large land areas. Therefore, in this work we explore the feasibility of employing morphological features to retrieve basic soil data being then used in pedotransfer functions (PTFs), as well as soil depth being viewed here as an indicator of the lower boundary condition. The proposed approach is applied in the Agri River basin (approximately 1770 km2), in Southern Italy (Basilicata Region), where we assessed relationships among soil depth and texture with morphological attributes, such as elevation, slope gradient, slope orientation (aspect), and surface curvature. Climatic conditions were defined using mean annual rainfall and climatic indices. The results show an enhanced correlation between soil depth and slope when accounting for the local vegetation types. As expected, texture can be conveniently linked to parent material obtained from lithology. We show an application of the method to derive soil parameters from morphological and vegetation coverage data (DEM, Corine Land Cover, etc.), which are actually available for almost all national basins

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Prediction of soil properties by digital terrain modelling

Cited by 305 publications

References 27 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

Soil depth modelling using terrain analysis and satellite imagery: the case study of Qeshlaq mountainous watershed (Kurdistan, Iran)

A Simplified Approach for Determining Hydrologic Behavior and Depth of Soils at Basin Scale

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