Establishment of a High Spatiotemporal Resolution Data Production Methodology Through Combined Observation of Geostationary and Polar-Orbiting Satellites

Conventional satellite observation faces a trade-off between temporal frequency and spatial resolution. This study establishes a methodology that combines the high-frequency observations of the geostationary Himawari-8 Advanced Himawari Imager (AHI; 10 min intervals, 0.5–1 km) with the polar-orbiting GCOM-C Second-generation Global Imager (SGLI; 250 m) to produce a daily, 250 m surface reflectance product for Japan in 2019. The methodology comprises data pre-processing, AHI–SGLI sensitivity correction via linear regression, and SGLI-guided mean-preserving disaggregation of AHI reflectance. Unlike deep learning super-resolution or statistical fusion (e.g., STARFM/ESTARFM), the product is derived entirely from actual observations. Comparison with Sentinel-2/MSI aggregated to 250 m yielded Pearson correlations of r = 0.71, 0.68, 0.71, and 0.57 for the blue, green, red, and near-infrared bands, respectively. The fused product increased the effective observation frequency 1.7-fold over the Kanto region relative to SGLI alone, captured phenological differences between Quercus- and Zelkova-dominated deciduous stands, and distinguished salt-damaged from non-damaged evergreen broadleaf forest within a single AHI 1 km pixel after Typhoon Faxai (September 2019), which was followed by Typhoon Hagi-bis (October 2019). These results demonstrate that observation-based sensor fusion can mitigate the spatiotemporal trade-off, with applications in climate change monitoring, disaster response, and forest management.