A Deep-Learning and Transfer-Learning Hybrid Aerosol Retrieval Algorithm for FY4-AGRI: Development and Verification over Asia

作者：Disong Fu, Hongrong Shi, Christian A. Gueymard, Dazhi Yang, Yu Zheng, Huizheng Che, Xuehua Fan, Xinlei Han, Lin Gao, Jianchun Bian, Minzheng Duan, Xiangao Xia · 发表于：Engineering · 年份：2024 · DOI：10.1016/j.eng.2023.09.023 · 被引用次数：23 · 研究领域：Atmospheric aerosols and clouds、Atmospheric Ozone and Climate、Atmospheric chemistry and aerosols

The Advanced Geosynchronous Radiation Imager (AGRI) is a mission-critical instrument for the Fengyun series of satellites. AGRI acquires full-disk images every 15 min and views East Asia every 5 min through 14 spectral bands, enabling the detection of highly variable aerosol optical depth (AOD). Quantitative retrieval of AOD has hitherto been challenging, especially over land. In this study, an AOD retrieval algorithm is proposed that combines deep learning and transfer learning. The algorithm uses core concepts from both the Dark Target (DT) and Deep Blue (DB) algorithms to select features for the machine-learning (ML) algorithm, allowing for AOD retrieval at 550 nm over both dark and bright surfaces. The algorithm consists of two steps: ①A baseline deep neural network (DNN) with skip connections is developed using 10 min Advanced Himawari Imager AODs as the target variable, and ②Sunphotometer AODs from 89 ground-based stations are used to fine-tune the DNN parameters. Out-of-station validation shows that the retrieved AOD attains high accuracy, characterized by a coefficient of determination (R2) of 0.70, a mean bias error (MBE) of 0.03, and a percentage of data within the expected error (EE) of 70.7%. A sensitivity study reveals that the top-of-atmosphere reflectance at 650 and 470 nm, as well as the surface reflectance at 650 nm, are the two largest sources of uncertainty impacting the retrieval. In a case study of monitoring an extreme aerosol event, the AGRI AOD is foun...