干旱区生态环境知识资源中心(Arid): FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG

Arid

DOI	10.3390/rs14215591
	FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG
	Ding, Haonan; Zhao, Limin; Liu, Shanwei; Chen, Xingfeng; de Leeuw, Gerrit; Wang, Fu; Zheng, Fengjie; Zhang, Yuhuan; Liu, Jun; Li, Jiaguo; She, Lu; Si, Yidan; Gu, Xingfa
通讯作者	Chen, XF
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:21
英文摘要	The Advanced Geostationary Radiation Imager (AGRI) is one of the main imaging sensors on the Fengyun-4A (FY-4A) satellite. Due to the combination of high spatial and temporal resolution, the AGRI is suitable for continuously monitoring atmospheric aerosol. Existing studies only perform AOD retrieval on the dark target area of FY-4A/AGRI, and the full disk AOD retrieval is still under exploration. The Neural Network AEROsol Retrieval for Geostationary Satellite (NNAeroG) based on the Fully Connected Neural Network (FCNN) was used to retrieve FY-4A/AGRI full disk aerosol optical depth (AOD). The data from 111 ground-based Aerosol Robotic Network (AERONET) and Sun-Sky Radiometer Observation Network (SONET) sites were used to train the neural network, and the data from 28 other sites were used for independent validation. FY-4A/AGRI AOD data from 2017 to 2020 were validated over the full disk and three different surface types (vegetated areas, arid areas, and marine and coastal areas). For general validation, the AOD predicted by the application of NNAeroG to FY-4A/AGRI observations is consistent with the ground-based reference AOD data. The validation of the FY-4A/AGRI AOD versus the reference data set shows that the root-mean-square error (RMSE), mean absolute error (MAE), R squared (R-2), and percentage of data with errors within the expected error +/- (0.05 + 15%) (EE15) are 0.237, 0.145, 0.733, and 58.7%, respectively. The AOD retrieval accuracy over vegetated areas is high but there is potential for improvement of the results over arid areas and marine and coastal areas. AOD retrieval results of FY-4A/AGRI were compared under fine and coarse modes. The retrieved AOD has low accuracy in coarse mode but is better in coarse-fine mixed mode and fine mode. The current AOD products over the ocean of NNAeroG-FY4A/AGRI are not recommended. Further development of algorithms for marine areas is expected to improve the full disk AOD retrieval accuracy.
英文关键词	aerosol optical depth (AOD) FY-4A AGRI geostationary satellite neural network
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000885872100001
WOS关键词	SPECTRAL-RESOLUTION LIDAR ; SATELLITE ; PRODUCTS ; CLIMATOLOGY ; NETWORK ; AERONET
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394224
推荐引用方式 GB/T 7714	Ding, Haonan,Zhao, Limin,Liu, Shanwei,et al. FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG[J],2022,14(21).
APA	Ding, Haonan.,Zhao, Limin.,Liu, Shanwei.,Chen, Xingfeng.,de Leeuw, Gerrit.,...&Gu, Xingfa.(2022).FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG.REMOTE SENSING,14(21).
MLA	Ding, Haonan,et al."FY-4A/AGRI Aerosol Optical Depth Retrieval Capability Test and Validation Based on NNAeroG".REMOTE SENSING 14.21(2022).