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Estimating Soil Properties from Thematic Soil Maps

The Bayesian Maximum Entropy Approach

Patrick Bogaert* and Dimitri D'Or

Dep. of Environmental Sciences and Land Use Planning–Environmetrics. Université catholique de Louvain. Place Croix du Sud 2, bte 16. 1348 Louvain-la-Neuve, Belgium



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Fig. 1. Belgian textural triangle (Tavernier and Maréchal, 1958). Each point on the triangle refers to a specific composition of sand (50 µm–2 mm), silt (2–50 µm), and clay (<2 µm).

 


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Fig. 2. Textural triangle for the sand, silt, and clay fractions expressed in proportion. Permissible joint values for the sand, silt, and clay fractions must lie on the triangle, so that sand + silt + clay = 1.

 


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Fig. 3. Texture map for (a) the study zone and (b) location of the hard data points (for the signification of the texture classes, see Fig. 2).

 


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Fig. 4. Experimental variograms and cross-variograms for the sand, silt, and clay contents (dots) and fitted model (plain line). The model is composed of a nugget and a spherical structure with a range of 4000 m.

 


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Fig. 5. Maps of the estimates for the sand fraction (expressed in percentages). (a) Regional Mean, (b) ordinary kriging (OK), (c) Bayesian maximum entropy (BME), and (d) the Monte Carlo procedure (BME/MC).

 


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Fig. 6. Error distributions for (a) sand, (b) silt, and (c) clay estimates. Regional mean (RM) is displayed with dotted line, ordinary kriging (OK) with dash dotted line, Bayesian maximum entropy (BME) with dashed line and the Monte Carlo procedure (BME/MC) with plain line.

 


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Fig. 7. Relative root mean square error (RMSE) for sand (stars), silt (open squares), and clay (open circles) estimates.

 


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Fig. 8. (a) Reference texture map, (b) estimated texture maps with ordinary kriging (OK), (c) Bayesian maximum entropy (BME), and (d) Monte Carlo procedure (BME/MC).

 





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Copyright © 2002 by the Soil Science Society of America.