Bibliometric and Visualization Analysis of the Literature on the Remote Sensing Inversion of Soil Salinization from 2000 to 2023

Chengshen Yin, Quanming Liu (), Teng Ma, Yanru Shi and Fuqiang Wang
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Chengshen Yin: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Quanming Liu: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Teng Ma: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China
Yanru Shi: Taipusi Banner Meteorological Bureau, Taipusi Banner 027000, China
Fuqiang Wang: College of Water Conservancy and Civil Engineering, Inner Mongolia Agricultural University, Hohhot 010018, China

Land, 2024, vol. 13, issue 5, 1-25

Abstract: Tracing the historical development of soil salinization and monitoring its current status are crucial for understanding the driving forces behind it, proposing strategies to improve soil quality, and predicting future trends. To comprehensively understand the evolution of research on the remote sensing inversion of soil salinity, a scientific bibliometric analysis was conducted on research from the past two decades indexed in the core scientific databases. This article analyzes the field from various perspectives, including the number of publications, authors, research institutions and countries, research fields, study areas, and keywords, in order to reveal the current state-of-the-art and cutting-edge research in this domain. Special attention was given to topics such as machine learning, data assimilation methods, unmanned aerial vehicle (UAV) remote sensing technology, soil inversion under vegetation cover, salt ion inversion, and remote sensing model construction methods. The results indicate an overall increase in the volume of publications, with key authors such as Metternicht, Gi and Zhao, Gengxing, and major research institutions including the International Institute for Geoinformatics Science and Earth Observation and the Chinese Academy of Sciences making significant contributions. Notably, China and the USA have made substantial contributions to this field, with research areas extending from Inner Mongolia’s Hetao irrigation district to the Mediterranean region. Research in the remote sensing domain focuses on various methods, including hyperspectral imaging for salinized soil inversion, with an increasing emphasis on machine learning. This study enriches researchers’ knowledge of the current trends and future directions of remote sensing inversion of soil salinization.

Keywords: remote sensing technology; soil salinity; machine learning; data assimilation; summarize; bibliometric analysis (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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