Remote Sensing-Enhanced Lithological Mapping for Predicting Shallow Landslide Susceptibility in Complex Terrains

Authors

  • Qixing Wang

    Mine Environment Research Institute, No.2 Geological Brigade of Hebei Bureau of Geology and Mineral Resources Exploration (Hebei Mine Environmental Restoration and Governance Technology Center), Tangshan 063000, China

DOI:

https://doi.org/10.30564/jees.v8i3.13222
Received: 10 January 2026 | Revised: 25 February 2026 | Accepted: 28 February 2026 | Published Online: 25 March 2026

Abstract

Shallow landslides are strongly controlled by near-surface lithological variability, yet conventional geological maps are often too generalized to support accurate susceptibility assessment in complex terrains. This review synthesizes recent advances in remote sensing–based lithological mapping and evaluates their integration into landslide susceptibility modeling. Evidence from the literature indicates that remote sensing-derived lithological products, particularly those incorporating mineralogical information and higher spatial resolution, consistently outperform traditional geological maps in improving model accuracy and spatial detail, especially in heterogeneous environments. However, key challenges remain, including scale mismatches between surface observations and subsurface controls, limited ground validation, uncertainty propagation, and restricted model transferability across regions. The review identifies multi-sensor data fusion and explainable machine learning as the most promising directions for advancing lithological discrimination and model reliability. Future progress depends on integrating remote sensing with process-based understanding, improving validation strategies, and standardizing uncertainty reporting. These developments are essential for enabling more robust, scalable, and operationally relevant landslide susceptibility assessments in complex terrains. Lastly, we describe the directions of research that focus on multi-sensor fusion, explainable machine learning, UAV (Unmanned Aerial Vehicle)-enabled validation, and standardized uncertainty reporting that can help articulate landslide susceptibility assessment, making them even more robust and operationally significant.

Keywords:

Shallow Landslides; Lithological Mapping; Remote Sensing; Susceptibility Modeling; Complex Terrain

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How to Cite

Wang, Q. (2026). Remote Sensing-Enhanced Lithological Mapping for Predicting Shallow Landslide Susceptibility in Complex Terrains. Journal of Environmental & Earth Sciences, 8(3), 251–265. https://doi.org/10.30564/jees.v8i3.13222

Issue

Vol. 8 , Iss. 3 (March 2026)

Article Type

Review

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Copyright © 2026 Qixing Wang

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This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

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