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NDVI data are crucial for agricultural and environmental research. The Fengyun-3 (FY-3) series satellites are recognized as primary sources for retrieving NDVI products on a global scale. To apply FY-3 NDVI data for long-term studies, such as climate change, this study conducted a thorough evaluation to detect the potentials of the FY-3B and FY-3D satellites for generating a long time series NDVI dataset. For this purpose, the spatiotemporal consistency between the FY-3B and FY-3D satellites was evaluated, and their performances were compared. Then, a grey relational analysis (GRA) method was applied to detect the factors influencing the consistency among the different satellites, and a gradient boosting regression (GBR) model was constructed to create a long-term FY-3 NDVI product. The results indicate an overall high consistency between the FY-3B and FY-3D NDVIs, suggesting that they could be used as complementary datasets for generating a long-term NDVI dataset. The correlations between the FY-3D NDVI and the MODIS NDVI, as well as the leaf area index (LAI) measurements, were both higher than those of FY-3B, which indicates a better performance of FY-3D in retrieving NDVI data. The grey correlation degrees between the NDVI differences and four parameters, which were land cover (LC), DEM, latitude (LAT) and longitude (LON), were calculated, revealing that the LC was the most related to the NDVI differences. Finally, a GBR model with FY-3B NDVI, LC, DEM, LAT and LON as the input variables and FY-3D NDVI as the target variable was established and achieved a robust performance. The R values between the GBR-estimated NDVI and FY-3D NDVI reached 0.947, 0.867 and 0.829 in the training, testing and validation datasets, respectively, indicating the feasibility of the established model for generating long time series NDVI data by combining data from the FY-3B and FY-3D satellites. [ABSTRACT FROM AUTHOR]