title: Updating categorical soil maps using limited survey data by Bayesian Markov chain cosimulation.
abstract: Updating categorical soil maps is necessary for providing current, higher-quality soil data to agricultural and environmental management but may not require a costly thorough field survey because latest legacy maps may only need limited corrections. This study suggests a Markov chain random field (MCRF) sequential cosimulation (Co-MCSS) method for updating categorical soil maps using limited survey data provided that qualified legacy maps are available. A case study using synthetic data demonstrates that Co-MCSS can appreciably improve simulation accuracy of soil types with both contributions from a legacy map and limited sample data. The method indicates the following characteristics: (1) if a soil type indicates no change in an update survey or it has been reclassified into another type that similarly evinces no change, it will be simply reproduced in the updated map; (2) if a soil type has changes in some places, it will be simulated with uncertainty quantified by occurrence probability maps; (3) if a soil type has no change in an area but evinces changes in other distant areas, it still can be captured in the area with unobvious uncertainty. We concluded that Co-MCSS might be a practical method for updating categorical soil maps with limited survey data.
authors.affiliation: Department of Geography and Center for Environmental Sciences & Engineering, University of Connecticut, Storrs, CT 06269, USA.
authors.forename: Weidong
authors.initials: W
authors.lastname: Li
authors.forename: Chuanrong
authors.initials: C
authors.lastname: Zhang
authors.forename: Dipak K
authors.initials: DK
authors.lastname: Dey
authors.forename: Michael R
authors.initials: MR
authors.lastname: Willig
mesh_terms: D000383:Agriculture; D001499:Bayes Theorem; D003247:Conservation of Natural Resources; D004784:Environmental Monitoring; D008390:Markov Chains; D012987:Soil / Q000737:chemistry*
chemical_list: D012987:Soil
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references.pmid: 12710927
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