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Restricted by the design of satellite sensors, the existing satellite-based Normalized Difference Vegetation Index (NDVI) cannot simultaneously have a high temporal resolution and spatial resolution, which substantially limits its applications. In recent years, several spatiotemporal fusion models have been developed to produce vegetation index datasets with both high spatial and temporal resolutions, but large uncertainties remain. This study proposes a spatiotemporal fusion model (i.e., Integrating ENvironmental VarIable spatiotemporal fusion model, InENVI) based on a machine-learning method by incorporating environmental variables to reconstruct NDVI data. Over 14 study areas covering various vegetation types globally, the InENVI method was validated for reproducing spatiotemporal variations in NDVI. On average, the determining coefficients (R 2 ) of the reconstructed NDVI compared with satellite-based NDVI observations were above 0.90, reflecting the spatiotemporal variations over all study sites. In addition, we compared the performance of the InENVI model with seven other fusion models over two cropland areas with high vegetation heterogeneity. The results showed the newly developed InENVI method had the best performance, and the reconstruction error of the InENVI method decreased about 23.68-59.63% on average over two study areas compared to the other seven methods. Our analyses also highlighted that the integration of environmental variables into spatiotemporal fusion is necessary to improve reconstruction accuracy. The InENVI model provides an alternative approach for reconstructing NDVI datasets with both high spatial and temporal resolutions over large areas.

Incorporating environmental variables into spatiotemporal fusion model to reconstruct high-quality vegetation index data