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

IF 10.1 1区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
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Abstract

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: ① A baseline deep neural network (DNN) with skip connections is developed using 10 min Advanced Himawari Imager (AHI) AODs as the target variable, and ② sunphotometer 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 found to be able to capture the detailed temporal evolution of the event. This work demonstrates the superiority of the transfer-learning technique in satellite AOD retrievals and the applicability of the retrieved AGRI AOD in monitoring extreme pollution events.

用于 FY4-AGRI 的深度学习与迁移学习混合气溶胶检索算法:开发与亚洲验证
先进地球同步辐射成像仪(AGRI)是风云系列卫星的关键任务仪器。AGRI 每 15 分钟获取一次全盘图像,每 5 分钟通过 14 个光谱波段观测东亚地区,从而能够探测高度可变的气溶胶光学深度(AOD)。迄今为止,气溶胶光学深度的定量检索一直具有挑战性,尤其是在陆地上。本研究提出了一种结合深度学习和迁移学习的 AOD 检索算法。该算法采用了暗目标(DT)和深蓝(DB)算法的核心概念,为机器学习(ML)算法选择特征,可在 550 nm 波长范围内对暗表面和亮表面进行 AOD 检索。该算法包括两个步骤:使用 10 分钟先进向日葵成像仪 AOD 作为目标变量,开发了一个具有跳接连接的基线深度神经网络(DNN);②使用 89 个地面站的太阳光度计 AOD 对 DNN 参数进行微调。站外验证表明,检索到的 AOD 具有很高的准确性,其特征是判定系数 (R2) 为 0.70,平均偏差误差 (MBE) 为 0.03,预期误差 (EE) 范围内的数据百分比为 70.7%。敏感性研究表明,650 和 470 纳米波长的大气顶部反射率以及 650 纳米波长的地表反射率是影响检索的两个最大的不确定性来源。在监测极端气溶胶事件的案例研究中,发现 AGRI AOD 能够捕捉到事件的详细时间演变过程。这项工作证明了转移学习技术在卫星 AOD 检索中的优越性,以及 AGRI AOD 在监测极端污染事件中的适用性。
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来源期刊
Engineering
Engineering Environmental Science-Environmental Engineering
自引率
1.60%
发文量
335
审稿时长
35 days
期刊介绍: Engineering, an international open-access journal initiated by the Chinese Academy of Engineering (CAE) in 2015, serves as a distinguished platform for disseminating cutting-edge advancements in engineering R&D, sharing major research outputs, and highlighting key achievements worldwide. The journal's objectives encompass reporting progress in engineering science, fostering discussions on hot topics, addressing areas of interest, challenges, and prospects in engineering development, while considering human and environmental well-being and ethics in engineering. It aims to inspire breakthroughs and innovations with profound economic and social significance, propelling them to advanced international standards and transforming them into a new productive force. Ultimately, this endeavor seeks to bring about positive changes globally, benefit humanity, and shape a new future.
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