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Spatiotemporal Data Fusion (STDF) can effectively integrate the advantages of high spatial and temporal resolutions in remotely sensed images. However, existing STDF methods still face two persistent challenges: a trade-off between accuracy and computational efficiency, and low data utilization due to the reliance on two fully cloud-free image pairs. To address these issues, this study proposes an enhanced ESTARFM by integrating an object-oriented classification strategy and a flexible object-driven pixel prediction method, namely OL-E2STARFM. OL-E2STARFM’s performance is assessed by Landsat-MODIS surface reflectance datasets, and compared with ESTARFM, OL-ESTARFM and other STDF methods. Results show that OL-E2STARFM is 147.96−162.74 times faster than ESTARFM and 0.52−0.65 times faster than OL-ESTARFM, while maintaining good prediction accuracy, as evidenced by its lowest average RMSE value of 0.0351, compared to 0.0357 for ESTARFM and 0.0360 for OL-ESTARFM. In addition, OL-E2STARFM outperforms existing OL-STDF methods by achieving a better trade-off between accuracy and efficiency. Furthermore, OL-E2STARFM significantly increases the utilization of cloud-covered image data, with an increase from 7.58% to 50.56% when the cloud cover ranges from 5% to 50% and is randomly distributed across two reference image pairs. The proposed OL-E2STARFM shows promise in monitoring long-term dynamic changes on the Earth’s surface.

international journal of digital earth

OL-E2STARFM: an enhanced spatiotemporal data fusion method integrating object-oriented classification strategy and flexible object-driven pixel prediction method