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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. [ABSTRACT FROM AUTHOR]

China boasts a vast expanse of mountainous terrain, characterized by intricate geological conditions and structural features, resulting in frequent geological disasters. Among these, landslides, as prototypical geological hazards, pose significant threats to both lives and property. Consequently, conducting a comprehensive landslide inventory in mountainous regions is imperative for current research. This study concentrates on the Yinshan Mountains, an ancient fault-block mountain range spanning east–west in the central Inner Mongolia Autonomous Region, extending from Langshan Mountains in the west to Damaqun Mountains in the east, with the narrow sense Xiao–Yin Mountains District in between. Employing multi-temporal high-resolution remote sensing images from Google Earth, this study conducted visual interpretation, identifying 10,968 landslides in the Yinshan area, encompassing a total area of 308.94 km2. The largest landslide occupies 2.95 km2, while the smallest covers 84.47 m2. Specifically, the Langshan area comprises 331 landslides with a total area of 11.96 km2, the narrow sense Xiao–Yin Mountains include 3393 landslides covering 64.13 km2, and the Manhan Mountains, Damaqun Mountains, and adjacent areas account for 7244 landslides over a total area of 232.85 km2. This research not only contributes to global landslide cataloging initiatives but also serves as a robust foundation for future geohazard prevention and management efforts. [ABSTRACT FROM AUTHOR]