A Novel Machine Learning-Based Algorithm for Retrieving Remote-Sensing Reflectance from Multispectral Observations of the Geostationary Meteorological Satellite Himawari-8

Ocean color (OC) data, particularly remote-sensing reflectance ( R rs ), are crucial for estimating marine optical property parameters. Although some polar-orbiting satellites provide reliable and operational R rs through atmospheric correction (AC), achieving comparable accuracy and wide-area synchronous observations using Himawari-8 (H8) over the East Asia–Pacific region with a high-temporal resolution remains a marked challenge. Since H8 is not a dedicated OC satellite, its ability to capture rapid minute-level OC changes is restricted. The primary challenge involves further enhancing the R rs retrieval accuracy beyond that of hourly products to compensate for the sensor’s signal-to-noise ratio (SNR) limitations. To address these issues, we develop a novel algorithm for retrieving R rs from H8. This algorithm innovatively integrates the Moderate Resolution Imaging Spectroradiometer (MODIS) with H8 at a 10-min resolution using machine learning. This ability to retrieve high-frequency R rs from H8 represents an unprecedented advance. After applying a classic AC framework for gas absorption and Rayleigh scattering corrections, an advanced transformer-based algorithm is employed for R rs retrievals. The results demonstrate high spatiotemporal consistency with MODIS OC products. A validation with AERONET-OC confirms that the new algorithm substantially improves upon the accuracy of the H8 R rs products, with RMSE reductions of approximately 34%, 26%, and 12% at 470, 510, and 640 nm, respectively. The algorithm effectively mitigates the underestimation of R rs values from H8 products at 470/510 nm in turbid waters, and the overall overestimation at 640 nm in clear water. This work pioneers OC variation analyses at 10-min intervals using H8.