[Objective] The spatial and temporal patterns of vegetation cover and its driving forces in the Tingjiang River basin of the southern red soil erosion area， were scientifically evaluated to reveal the strength of the role of each driving factor in the spatial differentiation of vegetation cover and the mechanism of interaction in order to provide a scientific basis for the restoration of ecosystems and comprehensive control of soil erosion. [Methods] Based on the monthly scale MOD13Q1 (250 m) dataset for 20 years from 2000 to 2020, we used one-way linear regression and Pearson’s correlation analysis to explore the relationship between normalised difference vegetation index (NDVI) and time and incorporated natural and anthropogenic factors such as temperature, precipitation, and elevation, as well as the spatial and temporal dynamics of vegetation NDVI in the watershed using the geoprobe model. A geoprobe model was used to analyse the temporal and spatial changes in vegetation NDVI in the watershed. [Results] ① Temporally, the vegetation cover in the Tingjiang River basin showed a fluctuating upward trend from 2000 to 2020, with a growth rate of 7.11% and an average rate of increase of 0.002 2/year, indicating that the ecological environment of the region was stable and continuously improving. ② Spatially, the overall medium-high and high coverage was dominant, showing a spatial distribution pattern that was lower than the surrounding area in the middle of each district and county, with significant geographical differences; the NDVI improved area was 86.33%, which was much larger than the degraded area region. ③ The driving factor detection results were: precipitation ＞ elevation ＞ temperature ＞ GDP＞ population density ＞ land use type ＞ vegetation type ＞ slope ＞ soil type. [Conclusion] Temporal and spatial variations in vegetation cover within the Tingjiang River Basin were affected by both natural and anthropogenic factors. The explanatory power of the precipitation factor was 0.705, which was the main driving factor affecting the changes in vegetation cover in the study area. Elevation, temperature, and GDP were the secondary driving factors, with explanatory powers of 0.58 or more. The interactions (q) between the factors showed higher explanatory power than the single factors, mainly in the form of a complex relationship between the enhancement and nonlinear enhancement effects of the two factors.