Temporal Resolution Enhancement of COMS Satellite Using Geo-Kompsat-2A Satellite Through Data-to-Data Translation

This study introduces a data-to-data (D2D) translation approach that utilizes a conditional adversarial learning framework to generate hypothetical data at the meteorological imager (MI) sensor on the communication, ocean, and meteorological satellite (COMS). The proposed D2D model produces virtual 10-min data from actual 30-min data by exploiting the 10-min temporal resolution (TR) of the advanced MI (AMI) on GEO-KOMPSAT-2A during the overlapping observation period of the two satellites. Specifically, the D2D model uses one visible (VIS) at 0.64 μm channel and four infrared channels (3.8, 6.9, 10.8, and 12.3 μm) from AMI of the actual 30-min data from April 2020 to April 2022 to train and test the model. Subsequently, the D2D model is applied to simulate hypothetical 10-min-TR COMS data using the 30-min COMS observation data from September 2019 to March 2020 during the coexistence period of the two satellites. Regression-calibrated COMS data alleviated the spectral response function differences between MI and AMI sensors. The proposed D2D method exhibits excellent statistical performance, with an average root-mean-square error of 0.056 for the VIS channel and 3.237 K, 1.005 K, 3.251 K, and 3.184 K for 3.8 μm, 6.9 μm, 10.8 μm, and 12.2 μm channels, respectively. The findings of this study are expected to facilitate various types of remote sensing research and applications using long-term data with AMI and AMI-like past MI data.