AOD data fusion with Geostationary Korea Multi-Purpose Satellite (Geo-KOMPSAT) instruments GEMS, AMI, and GOCI-II: Statistical and deep neural network methods





Citation of Original Publication

Kim, Minseok, Jhoon Kim, Hyunkwang Lim, Seoyoung Lee, Yeseul Cho, Yun-Gon Lee, Sujung Go, and Kyunghwa Lee. “AOD Data Fusion with Geostationary Korea Multi-Purpose Satellite (Geo-KOMPSAT) Instruments GEMS, AMI, and GOCI-II: Statistical and Deep Neural Network Methods.” Atmospheric Measurement Techniques Discussions, December 6, 2023, 1–34.


CC BY 4.0 DEED Attribution 4.0 International



Abstract. Aerosol optical depth (AOD) data fusion of aerosol datasets from the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT, GK) series was undertaken using both statistical and deep neural network (DNN)-based methods. The GK mission includes an Advanced Meteorological Imager (AMI) onboard GK-2A and a Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager-II onboard GK-2B. The statistical fusion method corrected the bias of each aerosol product by assuming a Gaussian error distribution. The Maximum Likelihood Estimation (MLE) fusion technique accounted for pixel-level uncertainties by weighting the root-mean-square error of each AOD product for every pixel. A DNN-based fusion model was trained to target Aerosol Robotic Network AOD values using fully connected hidden layers. The statistical and DNN-based fusion results generally outperformed individual GEMS and AMI AOD datasets in East Asia (R = 0.888; RMSE = −0.188; MBE = −0.076; 60.6 % within EE for MLE AOD; R = 0.905; RMSE = 0.161; MBE = −0.060; 65.6 % within EE for DNN AOD). The selection of AOD around Korean peninsula, which is incorporating all aerosol products including GOCI-II resulted in much better results (R = 0.911; RMSE = 0.113; MBE = −0.047; 73.3 % within EE for MLE AOD; R = 0.912; RMSE = 0.102; MBE = −0.028; 78.2 % within EE for DNN AOD). The DNN AOD effectively addressed the rapid increase in uncertainty at higher aerosol loadings. Overall, fusion AOD (particularly DNN AOD) most closely matched the performance of the Moderate Resolution Imaging Spectroradiometer Dark Target algorithm, with slightly less variance and a negative bias. Both fusion algorithms stabilized diurnal error variations and provided additional insights into hourly aerosol evolution. The application of aerosol fusion techniques to future geostationary satellite projects such as TEMPO, ABI, and GeoXO may facilitate the production of high-quality global aerosol data.