Highlights What are the main findings? Five vegetation types were identified (RBBL, SXG, SMDT, SDEBF, and SCBMF) using Sentinel-2 NDVI classification, showing strong correlations with soil fertility gradients and revealing the vegetation-soil feedbacks in the Nanxiong Basin. The ecological degradation mechanism follows a "weathering-transport-exposure" sequence, where soil nutrient depletion and acidification are driven by lithological fragility, hydroclimatic conditions, and human disturbances. What are the implications of the main finding? NDVI-based remote sensing provides an efficient and reliable tool for identifying stages of ecological degradation and monitoring vegetation-soil feedbacks, which is valuable for ecological restoration and land management strategies in semi-arid and monsoonal regions. Although NDVI demonstrated good performance, integrating additional indices such as Red-Edge NDVI and NDMI in future studies could improve vegetation health and moisture detection, further enhancing classification accuracy and extending the applicability of remote sensing methods.Highlights What are the main findings? Five vegetation types were identified (RBBL, SXG, SMDT, SDEBF, and SCBMF) using Sentinel-2 NDVI classification, showing strong correlations with soil fertility gradients and revealing the vegetation-soil feedbacks in the Nanxiong Basin. The ecological degradation mechanism follows a "weathering-transport-exposure" sequence, where soil nutrient depletion and acidification are driven by lithological fragility, hydroclimatic conditions, and human disturbances. What are the implications of the main finding? NDVI-based remote sensing provides an efficient and reliable tool for identifying stages of ecological degradation and monitoring vegetation-soil feedbacks, which is valuable for ecological restoration and land management strategies in semi-arid and monsoonal regions. Although NDVI demonstrated good performance, integrating additional indices such as Red-Edge NDVI and NDMI in future studies could improve vegetation health and moisture detection, further enhancing classification accuracy and extending the applicability of remote sensing methods.Abstract Red-bed desertification represents a critical form of land degradation in subtropical regions, yet the coupled soil-vegetation processes remain poorly understood. This study integrates Sentinel-2 vegetation indices with soil fertility gradients to assess vegetation-soil interactions in the Nanxiong Basin of South China. By combining Normalized Difference Vegetation Index (NDVI)-based vegetation classification with comprehensive soil property analyses, we aim to uncover the spatial patterns and driving mechanisms of degradation. The results revealed a clear gradient from intact forests to exposed red-bed bare land (RBBL). NDVI classification achieved an overall accuracy of 77.8% (kappa = 0.723), with mixed forests being identified most reliably (97.1%), while Red-Bed Bare Land (RBBL) exhibited the highest omission rate. Along this gradient, soil organic matter, available nitrogen, and phosphorus declined sharply, while pH shifted from near-neutral in forests to strongly acidic in bare lands. Principal component analysis (PCA) identified a dominant fertility axis (PC1, explaining 56.7% of the variance), which clustered forested sites in nutrient-rich zones and isolated RBBL as the most degraded state. The observed vegetation-soil pattern aligns with a "weathering-transport-exposure" sequence, whereby physical disintegration and selective erosion during monsoonal rainfall drive organic matter depletion, soil thinning, and acidification, with human disturbance further accelerating these processes. To our knowledge, this study is the first to directly couple PCA-derived soil fertility gradients with vegetation patterns in red-bed regions. By integrating vegetation indices with soil fertility gradients, this study establishes a process-based framework for interpreting red-bed desertification. These findings underscore the utility of remote sensing, especially NDVI classification, as a powerful tool for identifying degradation stages and linking vegetation patterns with soil processes, providing a scientific foundation for monitoring and managing land degradation in monsoonal and semi-arid regions.