Bibliometric Analysis of Studies Conducted in the Field of Soil Survey and Mapping
MAS Journal of Applied Sciences, cilt.11, sa.3, ss.687-702, 2026 (Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 11 Sayı: 3
- Basım Tarihi: 2026
- Doi Numarası: 10.5281/zenodo.21932708
- Dergi Adı: MAS Journal of Applied Sciences
- Derginin Tarandığı İndeksler: Scopus, Applied Science & Technology Source, Central & Eastern European Academic Source (CEEAS)
- Sayfa Sayıları: ss.687-702
- Anahtar Kelimeler: Bibliometric analysis, Digital soil mapping, Machine learning, Remote sensing, Soil survey
- Van Yüzüncü Yıl Üniversitesi Adresli: Evet
Özet
In scientific investigations worldwide, bibliometric analysis is frequently employed to examine the processes of change. In this regard, bibliometric analysis is crucial for identifying research trends. This study presents an extensive bibliometric analysis of publications in the field of soil survey and mapping. The Web of Science Core Collection database provided the study's data, which covered the years 2000 through 2025. In order to assess publishing trends, citation structures, international partnerships, and keyword patterns, 4,688 publications were examined using bibliometric approaches and VOSviewer software. The results revealed a remarkable increase in scientific output, with annual publications rising from 54 in 2000 to 373 in 2025, representing nearly a sevenfold increase. The retrieved publications accumulated 141,051 citations, demonstrating the growing scientific influence of the field. The findings showed a marked rise in citation counts and publication output over time, suggesting an increase in the field's level of scientific interest. The most significant nations in terms of productivity and citation effect were found to be the United States, China, and Australia. Digital soil mapping, machine learning, and remote sensing are the most popular study subjects, according to keyword analysis. The results show a distinct shift from conventional soil survey techniques to computational and data-driven methodologies.