f66583ff-fe86-4817-900e-83394e8eee6c English MD_CharacterSetCode_utf8 Individual item (e.g. a one-off academic paper or single website) British Geological Survey Contact for the metadata enquiries@bgs.ac.uk pointOfContact 2024-11-07 ISO19115 2003/Cor.1:2006 About regression-kriging: from equations to case studies 2007-10-01 creation 2007-10-01 publication revision MICA WP3 2017-09-25T10:33:00 Record extracted from Batch 2 spreadsheet MICA_B2-5 https://www.mica-project.eu/ No additional information provided about the dataset 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 Purpose for data generation is not known European Commission, Institute for Environment and Sustainability Contact for the resource described by the metadata Ispra I-21020 Italy http://www.sciencedirect.com/science/article/pii/S0098300407001008 WWW:LINK-1.0-http--link http://www.sciencedirect.com/science/article/pii/S0098300407001008 Link for further information on the resource Party that supplies the resource https://resources.bgs.ac.uk/images/minerals/micaGraphic/B2/MICA_B2-5.JPG MICA dataCentre Recent (includes data from 2006 onwards) MICA ontology (TemporalScheme) 2 General descriptive information / Paper MICA ontology (DataScheme) 2 Requirement for data generation: Voluntary Method of data or information generation: Academic research Exploration GEMET Concepts 2014-07-25 revision 2 Detailed geochemistry MICA ontology (DomainScheme) 2 Geostatistical estimates MICA ontology (DomainScheme) 2 2D predictive mapping MICA ontology (DomainScheme) 2 Regional geochemistry MICA ontology (DomainScheme) 2 Resource assessment MICA ontology (DomainScheme) 2 Geostatistics MICA ontology (DomainScheme) 2 Resource assessment MICA ontology (ValueSupplyChainScheme) 2 Available for free for all purposes Not confidential English geoscientificInformation economy no geographical coverage Other group of countries (please provide more details) Individual item (e.g. a one-off academic paper or single website) Data uncertainty Are any uncertainty measures provided (e.g. standard errors, confidence intervals, etc.)? true Quality assurance procedures Are quality assurance procedures described? true Information generation methods Are data or information generation methods formally described? true Record review Reviewed by: British Geological Survey Record validation false Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services 2010-12-08 Organisation source implies that information should be of good quality Metadata is distributed with a CC BY-ND 4.0 (Attribution-NoDerivs 4.0 International) licence