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<p>Kriging is a widely used geostatistical technique for spatial interpolation and uncertainty modelling, supporting diverse applications in earth and environmental sciences. This study analyses 3,515 Scopus-indexed publications from 1980 to 2020, with global patterns indicating China as the leading contributor with 847 publications, followed by the United States and France. The bibliometric assessment outlines the field’s intellectual structure, thematic evolution, and emerging research directions. Major themes include engineering design, reliability analysis, environmental modelling, and optimisation, alongside increasing integration with machine learning, remote sensing, and earth-observation systems. These methodological developments are reflected in the keyword co-occurrence results, where terms relating to spatiotemporal kriging, co-kriging with remote-sensing variables, data fusion, and uncertainty modelling appear within interconnected thematic clusters, indicating the field’s alignment with key earth science informatics challenges. Overall, the findings highlight kriging’s growing relevance for environmental monitoring, resource assessment and climate-related decision-making, while providing a consolidated foundation for future research and applications in advanced geospatial analysis.</p>

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Bibliometric insights into kriging research from 1980 to 2020: global trends and earth science connections

  • 期刊论文

  • 发表日期：

    2025年12月12日

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  • Azizi Abu Bakar,
  • Noor Zalina Mahmood,
  • Minoru Yoneda

摘要

Kriging is a widely used geostatistical technique for spatial interpolation and uncertainty modelling, supporting diverse applications in earth and environmental sciences. This study analyses 3,515 Scopus-indexed publications from 1980 to 2020, with global patterns indicating China as the leading contributor with 847 publications, followed by the United States and France. The bibliometric assessment outlines the field’s intellectual structure, thematic evolution, and emerging research directions. Major themes include engineering design, reliability analysis, environmental modelling, and optimisation, alongside increasing integration with machine learning, remote sensing, and earth-observation systems. These methodological developments are reflected in the keyword co-occurrence results, where terms relating to spatiotemporal kriging, co-kriging with remote-sensing variables, data fusion, and uncertainty modelling appear within interconnected thematic clusters, indicating the field’s alignment with key earth science informatics challenges. Overall, the findings highlight kriging’s growing relevance for environmental monitoring, resource assessment and climate-related decision-making, while providing a consolidated foundation for future research and applications in advanced geospatial analysis.