Abstract: This study explored the spatiotemporal variation characteristics of the vegetation cover index in Hubei Province from 2001 to 2023. The MODIS NDVI data were used. Trend analysis, the Hurst index, and the coefficient of variation were analyzed. Geographic detectors were employed to analyze the driving factors. The findings indicated that: (1) The overall vegetation cover index in Hubei Province was relatively high, with 66.49% of the province classified as high or relatively high vegetation cover. Spatially, the vegetation cover index exhibited a distribution pattern characterized by "low in the middle and high around, high-value areas primarily concentrated in the four ecological barrier regions of Hubei Province, while low-value areas were mainly found in Wuhan City and the surrounding urban agglomeration. (2) Temporally, the vegetation cover index in Hubei Province demonstrated a significant increasing trend from 2001 to 2023, with an annual growth rate of 0.3233. For the period of 2001 to 2007, the vegetation cover experienced a rapid growth (at an annual growth rate of 1.1028), followed by a steady increase after 2008 (at an annual growth rate of 0.2257). (3) Approximately 69.29% of the area in Hubei Province exhibited a significant increasing trend in the vegetation cover index, predominantly located in the western and eastern regions. About 25.93% of the area, mainly in the Jianghan Plain, showed no significant change. Only 4.78% of the area demonstrated a significant decrease, which was located primarily in Shayang County, Qianjiang City, and Hanchuan City. In terms of the degree of change, the areas with low and relatively low fluctuations accounted for 83.77%, which was mainly located in the western and eastern parts of Hubei, while areas with relatively high and high fluctuations constituted only 2.34%. (4) The characteristic of vegetation change in Hubei Province showed a slightly stronger tendency for same-direction changes compared to opposite-direction changes, although there were relatively few areas exhibiting strong characteristics in either direction. Notably, 32.69% of the areas were expected to continue an improvement, while 30.44% were projected to shift from an improvement to a degradation. (5) The analysis of driving forces revealed that land use type was the primary factor influencing the vegetation index, followed by temperature, digital elevation model (DEM), and population density, and precipitation was found to have the least impact. The correlation between vegetation type and the vegetation cover index was not significant. It was found that the influence of these dominant factors increased, and the explanatory power of the interactions among these factors had grown.