Evaluating Geostationary Satellite–Based Approaches for NDVI Gap Filling in Polar-Orbiting Satellite Observations

The Normalized Difference Vegetation Index (NDVI) derived from polar-orbiting satellites is widely used for vegetation monitoring; however, its temporal continuity is often limited by cloud contamination and fixed revisit cycles. This study investigates the feasibility of using geostationary satellite observations to support NDVI gap filling applications and continuous regional monitoring. Geostationary Ocean Color Imager II (GOCI-II) data were used as input, while Sentinel-2 Multispectral Instrument (MSI) NDVI served as the primary reference dataset. Landsat Operational Land Imager NDVI was additionally employed for independent cross-sensor comparison. A data-driven transformation framework was developed and applied to convert GOCI-II NDVI into MSI-equivalent NDVI while maintaining physically interpretable NDVI values. The transformed NDVI was evaluated through spatial comparisons and pixel-level statistical metrics, including correlation coefficient, mean absolute error, root mean square error, and structural similarity index measure. The results indicate that NDVI transformed from geostationary observations can capture broad spatial patterns and relative variability observed in MSI NDVI, particularly at the field scale. At the same time, reduced contrast and NDVI underestimation are observed, mainly due to spatial resolution differences and sub-pixel heterogeneity. This study emphasizes the potential role of geostationary satellite data as a complementary source for polar-orbiting NDVI products. The findings suggest that integrating geostationary and polar-orbiting satellite observations may contribute to improving NDVI continuity and supporting sustained vegetation monitoring over fixed regions where high temporal resolution is required.