Updating the 1:50,000 Dutch soil map using legacy soil data: A multinomial logistic regression approach

The 1:50,000 national soil survey of the Netherlands, completed in the early 1990s after more than three decades of mapping, is gradually becoming outdated. Large-scale changes in land and water management that took place after the field surveys have had a great impact on the soil. Especially oxidation of peat soils has resulted in a substantial decline of these soils. The aim of this research was to update the national soil map for the province of Drenthe (2680 km(2)) without additional fieldwork through digital soil mapping using legacy soil data. Multinomial logistic regression was used to quantify the relationship between ancillary variables and soil group. Special attention was given to model-building as this is perhaps the most crucial step in digital soil mapping. A framework for building a logistic regression model was taken from the literature and adapted for the purpose of soil mapping. The model-building process was guided by pedological expert knowledge to ensure that the final regression model is not only statistically sound but also pedologically plausible. We built separate models for the ten major map units, representing the main soil groups, of the national soil map for the province of Drenthe. The calibrated models were used to estimate the probability of occurrence of soil groups on a 25 m grid. Shannon entropy was used to quantify the uncertainty of the updated soil map, and the updated soil map was validated by an independent probability sample. The theoretical purity of the updated map was 67%. The estimated actual purity of the updated map, as assessed by the validation sample, was 58%, which is 6% larger than the actual purity of the national soil map. The discrepancy between theoretical and actual purity might be explained by the spatial clustering of the soil profile observations used to calibrate the multinormal logistic regression models and by the age difference between calibration and validation observations. (C) 2009 Elsevier B.V. All rights reserved.