Spectral Band Adjustment and Data Fusion of Multiple Third-Generation Geostationary Satellites: Toward Hyper-Temporal Monitoring of the Biosphere

Satellite remote sensing with the third-generation geostationary satellites has recently gained significant attention. These satellites provide hyper-temporal datasets, enabling the mitigation of cloud contamination effects and the observation of diurnal changes in targets, in contrast to polar-orbiting satellites. Currently, several third-generation geostationary satellites, such as the Japanese Himawari series, the US GOES series, the Korean GK2A, the Chinese FY4 series, and the European MTG1, have been under operation, and their datasets are available. In contrast with polar-orbiting satellites, geostationary satellites cover the limited areas of the Earth due to orbital constraints. Consequently, collaboration among multiple geostationary satellites is required to cover the Earth comprehensively. For this collaboration involving satellites with different sensors, spectral band adjustments among sensors and subsequent data fusion based on these adjustments are indispensable. In this study, spectral band adjustments and data fusion were performed using band simulations with hyperspectral data from satellites, in situ observations, and a 3-D radiative transfer model. The spectral band adjustments and data fusion focused on the visible and near-infrared regions, which are critical for terrestrial ecosystem monitoring, including vegetation monitoring. Our simulations revealed linear relationships in the visible and near-infrared regions among the bands of each sensor after specific mathematical processes. Additionally, experimental data fusion using actual geostationary satellite datasets demonstrated the success of our spectral band adjustment approach. These results suggest that the proposed method can significantly contribute to environmental monitoring with third-generation geostationary satellites, mainly observation of the terrestrial ecosystem. Further research, such as applications for terrestrial vegetation monitoring, is anticipated.