Understanding the spatial and temporal variation in vegetation coverage in the Haihe River Basin and its influencing factors can provide a theoretical reference for its ecological protection and construction. Based on the datasets of the normalized difference vegetation index （NDVI）, natural factors, and socioeconomic factors, the spatial and temporal variation characteristics of the NDVI in the Haihe River Basin from 2000 to 2020 were analyzed by using the maximum value synthesis method, Theil-Sen Median trend analysis, the Mann-Kendall significance test, the Hurst index, and a geographical detector. The factors influencing the spatial differentiation of the NDVI were discussed. The results showed that： ① The NDVI in the Haihe River Basin showed a fluctuating upward trend from 2000 to 2020, with a growth rate of 0.003 2 a-1. High vegetation coverage and medium-high vegetation coverage accounted for 81.79% of the total area, and vegetation coverage was generally at a high level. The spatial distribution of the NDVI showed obvious differences, which were high in the west and low in Bohai Bay and urban areas. The area of vegetation improvement in the future was 49.68%. ② The q values of the land use type and soil type were 0.254 and 0.222, respectively, which were the dominant factors affecting the spatial variation in the NDVI. Rainfall and temperature were secondary factors, and the q values were 0.164 and 0.147, respectively. The q value of interaction land use type ∩ soil type was the highest （0.442）, followed by the q value of land use type ∩ annual precipitation （0.428）. Vegetation change is the result of the combined effect of various factors. It is necessary to formulate different strategies and managements according to local conditions. The research results can provide a theoretical basis for the eco-environmental construction as well as the sustainable development of the Haihe River Basin.
Keywords: Haihe River Basin; geographical detector; impact factor; normalized difference vegetation index （NDVI）; spatial distribution pattern.