Journal
GEODERMA
Volume 189, Issue -, Pages 540-553Publisher
ELSEVIER
DOI: 10.1016/j.geoderma.2012.05.028
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Funding
- Dutch Ministry of Economic Affairs, Agriculture and Innovation
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We present the generalised linear geostatistical model (GLGM) for soil type mapping and investigate if spatial prediction with this model results in a soil map of greater accuracy than a map obtained using a non-spatial model, i.e. a model that ignores spatial dependence in the soil type variable. The GLGM is central to the framework of model-based geostatistics. We adopted a pragmatic approach in which the five soil types in a cultivated peatland were separately modelled with a binomial logit-linear GLGM. Prediction with soil type-specific GLGMs resulted in five binomial probabilities at each prediction location, which were standardised to multinomial probabilities by selecting the soil type with maximal probability. A soil map was created from the predicted probabilities. In addition, two non-spatial models were used to map soil type. These were the multinomial logit model and the generalised linear model for Bernoulli-distributed data. Validation with independent probability sample data showed that use of a spatial model for digital soil type mapping did not result in more accurate predictions than those with the non-spatial models. (c) 2012 Elsevier B.V. All rights reserved.
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