Regression modelGeostatistics
共克里金
共克里金将克里金方法扩展至利用一个或多个相关的次级变量来改进主变量的预测。当目标变量采样稀疏，但一个相关的、测量成本较低的变量采样密集时，共克里金通过次级变量与其的交叉相关性来借用其信息，从而比单独对主变量进行克里金插值获得更准确的插值结果和预测方差。
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来源
- Matheron, G. (1963). Principles of geostatistics. Economic Geology, 58(8), 1246–1266. DOI: 10.2113/gsecongeo.58.8.1246 ↗
- Cressie, N. A. C. (1993). Statistics for Spatial Data (Revised ed.). John Wiley & Sons. ISBN: 978-0-471-00255-0
如何引用本页
ScholarGate. (2026, June 2). Cokriging (Multivariate Geostatistical Interpolation). ScholarGate. https://scholargate.app/zh/spatial-analysis/cokriging
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