资源开采引发的土壤结构破坏、养分流失及重金属污染等多重问题，已成为矿区生态退化的关键驱动因素，严重制约了植被恢复与生态系统功能重建。近年来，可见-短波红外（VNIR-SWIR）高光谱遥感技术因具备无损、高效和大范围连续监测等优势，在矿区土壤理化特性监测及问题诊断中展现出广阔前景。本文系统阐述了：1）矿区土壤的形成演变过程中及物理和化学特征及其光谱响应规律；2）梳理了目前在光谱建模及定量估算中常用的经验模型、物理模型及机器学习模型的基本原理和适用性；3）重点讨论了国内外利用实验室光谱测量、无人机成像光谱以及卫星光谱技术在矿区土壤有机质、全氮等养分特性及重金属元素等污染特性监测中的应用。研究表明，VNIR-SWIR高光谱技术结合定量估算模型已在提升矿区土壤关键理化特性估算精度方面取得显著成效，但仍面临精度不稳定和区域适应性差及动态监测能力不足等挑战。未来应加强对“土壤-光谱”响应机制的机理探讨，推动多源、多平台/尺度、多时相数据融合与动态建模体系的构建，发展具备时空迁移能力的通用化遥感估算框架，为矿区土地精准复垦与生态系统可持续修复提供坚实的技术支撑。

Resource extraction activities fundamentally reshape soil systems through a series of processes such as surface stripping, underground excavation, waste dumping, and mineral processing. These operations disrupt natural soil by removing topsoil horizons, mixing soils with fragmented rock materials, and introducing exogenous substances associated with tailings and beneficiation residues. As a result, mining-affected soils commonly exhibit pronounced alterations in physical structure, chemical composition, and surface morphology, including increased coarse fragment content, reduced soil nutrients, and elevated concentrations of potentially toxic elements. Such changes often lead to a series of environmental problems, such as soil degradation, contamination, and desertification-like processes, ultimately impairing soil fertility and ecosystem functioning. Effective monitoring and assessment of these soil disturbances therefore constitute a critical prerequisite for guiding ecological restoration, evaluating reclamation effectiveness, and supporting sustainable management in mining areas. In recent years, visible-shortwave infrared (VNIR-SWIR) hyperspectral remote sensing technology has demonstrated significant potential in soil physicochemical properties estimation and environmental diagnosis in mining regions, owing to its non-destructive nature, high efficiency, and capability for large-scale, continuous monitoring. This potential arises from the sensitivity of VNIR-SWIR hyperspectral technology to diagnostic absorption features, reflectance magnitude, and spectral shape variations controlled by soil mineral composition, organic matter content, and moisture conditions. This capability is particularly valuable in mining landscapes, where soil properties exhibit pronounced spatial heterogeneity and rapid temporal changes driven by anthropogenic disturbance and reclamation interventions. Based on a comprehensive review of the literature published over the past three decades, this review systematically: (1) examines the formation and evolution processes of mining-affected soils, along with their key physical and chemical characteristics and corresponding spectral responses in the VNIR-SWIR domain; (2) summarizes the theoretical foundations and applicability of hyperspectral remote sensing estimation models, including empirical models, physical models, and machine learning approaches; and (3) highlights recent advances in the application across a wide range of mining environments, including open-pit and underground operations in both metal and coal mining areas, with representative case studies reported from major mining regions worldwide—such as China, Australia, and Europe—using laboratory spectroscopy, UAV-based imaging, and satellite hyperspectral techniques to estimate key soil quality indicators—such as organic matter, total nitrogen, and heavy metal concentrations. Research findings indicate that VNIR-SWIR hyperspectral technology, when integrated with appropriate estimation models, has notably improved the accuracy of estimating key soil physicochemical properties in mining areas. Nevertheless, significant challenges remain in terms of model stability, regional adaptability, and dynamic monitoring capabilities. Spectral interference caused by heterogeneous surface materials, bidirectional reflectance effects, scale mismatches among platforms, and uncertainties associated with model transfer across different mining regions continue to limit operational applications. Future research should focus on deepening the mechanistic understanding of soil–spectral interactions, promoting the fusion of multi-source, multi-scale, and multi-temporal data, and advancing the development of generalizable and transferable modeling frameworks. Moreover, multi-scale observations—from proximal sensing and UAV platforms to spaceborne hyperspectral missions—have expanded the spatial and temporal applicability of soil monitoring in complex mining settings. Collectively, these advances provide new opportunities for bridging the gap between experimental studies and regional-scale applications. Such efforts are essential for enabling robust, long-term monitoring of soil quality, supporting evidence-based land reclamation planning, and ultimately facilitating sustainable ecological restoration in mining landscapes.