『Abstract
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gradually becoming outdated.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 km2) 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 multinomial
logistic regression models and by the age difference between calibration
and validation observations.
Keywords: Digital soil mapping; Sampling; Soil survey; Expert
knowledge; Validation; the Netherlands』
1. Introduction
2. Methods
2.1. Study area
2.2. Data sources
2.2.1. Soil data
2.2.2. Environmental ancillary data
2.3. Multinomial logistic regression
2.3.1. The logistic model
2.3.2. Assessing model significance and contribution of predictors
2.4. Model-building
2.4.1. Pedological knowledge for regression modeling
2.4.2. Model-building strategy
2.4.2.1. Definition of a conceptual model of pedogenesis
2.4.2.2. Collection of predictors from available environmental
ancillary
2.4.2.3. Univariate analysis and selection of candidate predictors
2.4.2.4. Multivariate analysis of selected candidate predictors
2.4.2.5. Evaluation of adequacy of the multivariate model(s)
2.4.2.6. Checking the assumption of linearity in the logit
2.4.2.7. Checking for interactions between predictors
2.4.2.8. Statistical and visual assessment of the final model
2.5. Model application
2.6. Model validation
2.6.1. Sampling strategy
2.6.2. Statistical inference
3. Results
3.1. Model-building
3.1.1. Definition of a conceptual model of pedogenesis
3.1.2. Collection of predictors from available environmental
ancillary data
3.1.3. Univariate analysis of candidate predictors
3.1.4. Multivariate analysis of selected candidate predictors
3.1.5. Evaluation of adequacy of the multivariate model(s)
3.1.6. Checking the assumption of linearity in the logit
3.1.7. Checking for interactions between predictors
3.1.8. Statistical and visual assessment of the final model
3.2. Model application
3.3. Model validation
4. Discussion
4.1. Multinomial logistic regression for soil mapping
4.2. Soil spatial prediction
4.3. Legacy soil data
4.4. Validation of soil maps
5. Conclusions
Acknowledgements
References