About regression-kriging: from equations to case studies

Peer reviewd paper in the journal Computers and Geosciences. This paper discusses the characteristics of regression-kriging (RK), its strengths and limitations, and illustrates these with a simple example and three case studies. RK is a spatial interpolation technique that combines a regression of the dependent variable on auxiliary variables (such as land surface parameters, remote sensing imagery and thematic maps) with simple kriging of the regression residuals. It is mathematically equivalent to the interpolation method variously called “Universal Kriging” (UK) and “Kriging with External Drift” (KED), where auxiliary predictors are used directly to solve the kriging weights. The advantage of RK is the ability to extend the method to a broader range of regression techniques and to allow separate interpretation of the two interpolated components. Data processing and interpretation of results are illustrated with three case studies covering the national territory of Croatia. The case studies use land surface parameters derived from combined Shuttle Radar Topography Mission and contour-based digital elevation models and multitemporal-enhanced vegetation indices derived from the MODIS imagery as auxiliary predictors. These are used to improve mapping of two continuous variables (soil organic matter content and mean annual land surface temperature) and one binary variable (presence of yew). In the case of mapping temperature, a physical model is used to estimate values of temperature at unvisited locations and RK is then used to calibrate the model with ground observations. The discussion addresses pragmatic issues: implementation of RK in existing software packages, comparison of RK with alternative interpolation techniques, and practical limitations to using RK. The most serious constraint to wider use of RK is that the analyst must carry out various steps in different software environments, both statistical and GIS.

Website:

http://www.sciencedirect.com/science/article/pii/S0098300407001008

Date (Creation): 2007-10-01

Date (Publication): 2007-10-01

Date (Revision)

Citation identifier: https://www.mica-project.eu/ / MICA_B2-5
MICA WP3 2017-09-25T10:33:00 Record extracted from Batch 2 spreadsheet

Other citation details: No additional information provided about the dataset

Purpose: Purpose for data generation is not known

Status: Completed

Point of contact

Organisation name	Individual name	Electronic mail address	Role
European Commission, Institute for Environment and Sustainability			Resource provider

Maintenance and update frequency: Not planned

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MICA

MICA ontology (TemporalScheme)

Recent (includes data from 2006 onwards)

MICA ontology (DataScheme)

General descriptive information / Paper

Keywords

Requirement for data generation: Voluntary

Keywords

Method of data or information generation: Academic research

GEMET Concepts

Exploration