Title: Accounting for Scale-Dependent Correlation in the Spatial Prediction of Soil Properties
Abstract: This paper compares the prediction performances of three multivariate algorithms that allow the incorporation of secondary information that is known at all locations to be estimated (linear regression, simple krig-ing with varying local means derived from the secondary attribute, kriging with an external drift) and the more general cokriging with one or two un-biasedness constraints. A case study shows that cokriging performs better than the three other algorithms, in particular when a single unbiasedness constraint is considered. For all methods, the prediction error is reduced by replacing the raw measurements of the secondary variable (topsoil Co) by their regional components, which are estimated using factorial kriging, because of their better correlation with the primary variable (topsoil Cu).
Publication Year: 1999
Publication Date: 1999-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
Access and Citation
AI Researcher Chatbot
Get quick answers to your questions about the article from our AI researcher chatbot