Uncertainty in the spatial prediction of soil texture

Ließ, Mareike; Glaser, Bruno; Huwe, Bernd

doi:10.1016/j.geoderma.2011.10.010

Cited by 250 publications

(63 citation statements)

References 37 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Li et al, 2011;Liess et al, 2012;Adhikari et al, 2013;Vaysse and Lagacherie, 2015;Were et al, 2015;Lacoste et al, 2016;Mulder et al, 2016;Somarathna et al, 2016;Taghizadeh-Mehrjardi et al, 2016;Yang et al, 2016). Geodata availability and deemed importance often determine what covariates are used for DSM.…”

Section: Introductionmentioning

confidence: 99%

“…Unless combined with either lasso or boosting, large sets of covariates are difficult to process through LM, EDK or GAM. But these methods allow for simple model interpretation with partial effects plots (Faraway, 2005, p. 73) Generally, more complex approaches seem to yield more accurate predictions than simpler DSM methods (Liess et al, 2012;Brungard et al, 2015). The effects of interactions between covariates on responses can be modelled by tree-based methods, but single trees (CART; e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The effects of interactions between covariates on responses can be modelled by tree-based methods, but single trees (CART; e.g. Liess et al, 2012;Heung et al, 2016) tend to be noisy (large variance). Cubist, an extension of CART with LM at the terminal nodes of the tree, was only occasionally applied to large covariate sets (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…Viscarra Rossel et al, 2015;Miller et al, 2015). Forming ensembles of trees aims to reduce their variance and likely outperforms its single components (Liess et al, 2012). RF seems stable for large sets of covariates (Behrens et al, 2010a(Behrens et al, , b, 2014, while BRT, compared to RF by Yang et al (2016), yields similar model accuracy.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Nussbaum¹,

Spiess²,

Baltensweiler³

et al. 2018

SOIL

210

View full text Add to dashboard Cite

Abstract. The spatial assessment of soil functions requires maps of basic soil properties. Unfortunately, these are either missing for many regions or are not available at the desired spatial resolution or down to the required soil depth. The field-based generation of large soil datasets and conventional soil maps remains costly. Meanwhile, legacy soil data and comprehensive sets of spatial environmental data are available for many regions.Digital soil mapping (DSM) approaches relating soil data (responses) to environmental data (covariates) face the challenge of building statistical models from large sets of covariates originating, for example, from airborne imaging spectroscopy or multi-scale terrain analysis. We evaluated six approaches for DSM in three study regions in Switzerland (Berne, Greifensee, ZH forest) by mapping the effective soil depth available to plants (SD), pH, soil organic matter (SOM), effective cation exchange capacity (ECEC), clay, silt, gravel content and fine fraction bulk density for four soil depths (totalling 48 responses). Models were built from 300-500 environmental covariates by selecting linear models through (1) grouped lasso and (2) an ad hoc stepwise procedure for robust external-drift kriging (georob). For (3) geoadditive models we selected penalized smoothing spline terms by component-wise gradient boosting (geoGAM). We further used two tree-based methods: (4) boosted regression trees (BRTs) and (5) random forest (RF). Lastly, we computed (6) weighted model averages (MAs) from the predictions obtained from methods 1-5.Lasso, georob and geoGAM successfully selected strongly reduced sets of covariates (subsets of 3-6 % of all covariates). Differences in predictive performance, tested on independent validation data, were mostly small and did not reveal a single best method for 48 responses. Nevertheless, RF was often the best among methods 1-5 (28 of 48 responses), but was outcompeted by MA for 14 of these 28 responses. RF tended to over-fit the data. The performance of BRT was slightly worse than RF. GeoGAM performed poorly on some responses and was the best only for 7 of 48 responses. The prediction accuracy of lasso was intermediate. All models generally had small bias. Only the computationally very efficient lasso had slightly larger bias because it tended to under-fit the data. Summarizing, although differences were small, the frequencies of the best and worst performance clearly favoured RF if a single method is applied and MA if multiple prediction models can be developed.Published by Copernicus Publications on behalf of the European Geosciences Union.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Nussbaum¹,

Spiess²,

Baltensweiler³

et al. 2018

SOIL

210

View full text Add to dashboard Cite

show abstract

“…Classification and regression trees (CARTs; e.g. Liess et al, 2012;Heung et al, 2016), Cubist (e.g. Henderson et al, 2005;Adhikari et al, 2013;Lacoste et al, 2016) and ensemble tree methods like random forest (RF; e.g.…”

Section: Introductionmentioning

confidence: 99%

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Nussbaum

Walthert

Fraefel

et al. 2017

SOIL

View full text Add to dashboard Cite

Abstract. High-resolution maps of soil properties are a prerequisite for assessing soil threats and soil functions and for fostering the sustainable use of soil resources. For many regions in the world, accurate maps of soil properties are missing, but often sparsely sampled (legacy) soil data are available. Soil property data (response) can then be related by digital soil mapping (DSM) to spatially exhaustive environmental data that describe soilforming factors (covariates) to create spatially continuous maps. With airborne and space-borne remote sensing and multi-scale terrain analysis, large sets of covariates have become common. Building parsimonious models amenable to pedological interpretation is then a challenging task.We propose a new boosted geoadditive modelling framework (geoGAM) for DSM. The geoGAM models smooth non-linear relations between responses and single covariates and combines these model terms additively. Residual spatial autocorrelation is captured by a smooth function of spatial coordinates, and non-stationary effects are included through interactions between covariates and smooth spatial functions. The core of fully automated model building for geoGAM is component-wise gradient boosting.We illustrate the application of the geoGAM framework by using soil data from the Canton of Zurich, Switzerland. We modelled effective cation exchange capacity (ECEC) in forest topsoils as a continuous response. For agricultural land we predicted the presence of waterlogged horizons in given soil depths as binary and drainage classes as ordinal responses. For the latter we used proportional odds geoGAM, taking the ordering of the response properly into account. Fitted geoGAM contained only a few covariates (7 to 17) selected from large sets (333 covariates for forests, 498 for agricultural land). Model sparsity allowed for covariate interpretation through partial effects plots. Prediction intervals were computed by model-based bootstrapping for ECEC. The predictive performance of the fitted geoGAM, tested with independent validation data and specific skill scores for continuous, binary and ordinal responses, compared well with other studies that modelled similar soil properties. Skill score (SS) values of 0.23 to 0.53 (with SS = 1 for perfect predictions and SS = 0 for zero explained variance) were achieved depending on the response and type of score. GeoGAM combines efficient model building from large sets of covariates with effects that are easy to interpret and therefore likely raises the acceptance of DSM products by end-users.

show abstract

Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model

Valocchi

et al. 2017

Water Resources Research

View full text Add to dashboard Cite

Groundwater model structural error is ubiquitous, due to simplification and/or misrepresentation of real aquifer systems. During model calibration, the basic hydrogeological parameters may be adjusted to compensate for structural error. This may result in biased predictions when such calibrated models are used to forecast aquifer responses to new forcing. We investigate the impact of model structural error on calibration and prediction of a real‐world groundwater flow model, using a Bayesian method with a data‐driven error model to explicitly account for model structural error. The error‐explicit Bayesian method jointly infers model parameters and structural error and thereby reduces parameter compensation. In this study, Bayesian inference is facilitated using high performance computing and fast surrogate models (based on machine learning techniques) as a substitute for the computationally expensive groundwater model. We demonstrate that with explicit treatment of model structural error, the Bayesian method yields parameter posterior distributions that are substantially different from those derived using classical Bayesian calibration that does not account for model structural error. We also found that the error‐explicit Bayesian method gives significantly more accurate prediction along with reasonable credible intervals. Finally, through variance decomposition, we provide a comprehensive assessment of prediction uncertainty contributed from parameter, model structure, and measurement uncertainty. The results suggest that the error‐explicit Bayesian approach provides a solution to real‐world modeling applications for which data support the presence of model structural error, yet model deficiency cannot be specifically identified or corrected.

show abstract

Uncertainty in the spatial prediction of soil texture

Cited by 250 publications

References 37 publications

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Evaluation of digital soil mapping approaches with large sets of environmental covariates

Mapping of soil properties at high resolution in Switzerland using boosted geoadditive models

Quantifying model structural error: Efficient Bayesian calibration of a regional groundwater flow model using surrogates and a data‐driven error model

Contact Info

Product

Resources

About