Aerosol optical depth data fusion with Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2) instruments GEMS, AMI, and GOCI-II: statistical and deep neural network methods

• Published in: Atmospheric Measurement Techniques
• Main Authors: M. Kim, J. Kim, H. Lim, S. Lee, Y. Cho, Y.-G. Lee, S. Go, K. Lee
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2024
• Subjects: Environmental engineering; TA170-171; Earthwork. Foundations; TA715-787; Aerosol Robotic Network
• Online Access: https://doi.org/10.5194/amt-17-4317-2024; https://doaj.org/article/975944d9153342939786bfaec048f9e5

Data fusion of aerosol optical depth (AOD) datasets from the second generation of the Geostationary Korea Multi-Purpose Satellite (GEO-KOMPSAT-2, GK-2) series was undertaken using both statistical and deep neural network (DNN)-based methods. The GK-2 mission includes an Advanced Meteorological Imager (AMI) aboard GK-2A and a Geostationary Environment Monitoring Spectrometer (GEMS) and Geostationary Ocean Color Imager II (GOCI-II) aboard GK-2B. The statistical fusion method, maximum likelihood estimation (MLE), corrected the bias of each aerosol product by assuming a Gaussian error distribution and 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 (AERONET) AOD values using fully connected hidden layers. The MLE and DNN AOD 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 the Korean Peninsula, which incorporates 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) showed improvements with 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 Tropospheric Emissions: Monitoring of Pollution (TEMPO), Sentinel-4, and Geostationary Extended Observations (GeoXO) may facilitate the production of high-quality global aerosol data.