Estimation of the Arctic Aerosol Optical Depth Based on the Synergistic Integration of Multi-Source Data

Han Yuli; Chang Liang; Chen Fanglin; Ding Xueyao

Article Contents

Article Navigation > Haiyang Xuebao > 2026 > Accepted Manuscript

Han Yuli，Chang Liang，Chen Fanglin, et al. Estimation of the Arctic Aerosol Optical Depth Based on the Synergistic Integration of Multi-Source Data[J]. Haiyang Xuebao，2026, 48(x)：1–17

Citation:

Han Yuli，Chang Liang，Chen Fanglin, et al. Estimation of the Arctic Aerosol Optical Depth Based on the Synergistic Integration of Multi-Source Data[J]. Haiyang Xuebao，2026, 48(x)：1–17

Citation:

Han Yuli，Chang Liang，Chen Fanglin, et al. Estimation of the Arctic Aerosol Optical Depth Based on the Synergistic Integration of Multi-Source Data[J]. Haiyang Xuebao，2026, 48(x)：1–17

PDF( 11494 KB)

Estimation of the Arctic Aerosol Optical Depth Based on the Synergistic Integration of Multi-Source Data

College of Oceanography and Ecological Science, Shanghai Ocean University, Shanghai 201306, China

Received Date: 2025-08-27
Rev Recd Date: 2026-02-02

Available Online: 2026-02-13

Abstract

Abstract

The Arctic is a climate-sensitive region where Arctic Amplification is influenced by aerosol radiative forcing. Aerosol Optical Depth (AOD) as key parameter characterizing the extinction properties of atmospheric aerosols, plays a critical role in understanding the influence of aerosols on environmental and climate systems. However, single-satellite AOD products exhibit large uncertainties and data gaps in the Arctic due to sensor limitations and complex surface conditions. The Bayesian Maximum Entropy (BME) method is commonly used for AOD data fusion, yet the traditional BME approach, which employs least squares to model covariance, struggles to effectively handle the complexity and non-stationarity of high-dimensional parameter spaces. Based on AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging Spectro Radiometer (MISR), this study introduces a Particle Swarm Optimization (PSO) algorithm with global search capability to improve the covariance modeling process, resulting in a PSO-BME fusion algorithm that enhances the stability and accuracy of data integration. The results demonstrate that the PSO-BME method effectively integrates MODIS and MISR AOD data and successfully fills data gaps. In regions covered by both sources, the fused AOD achieves an RMSE of 0.055, an EE of 78%, an MAE of 0.04, and a correlation coefficient of 0.7, while maintaining acceptable accuracy in unobserved areas. The annual spatial coverage increased from 15.45% (MODIS) and 1.45% (MISR) to 32.7%. Spatiotemporal distribution analysis shows that the fusion product significantly improves spatial continuity and more accurately reflects overall AOD variations. Furthermore, the spatiotemporal evolution patterns reveal that aerosol distribution in the Arctic is influenced by both local meteorological conditions and cross-border transport of pollutants from mid- and low-latitudes.
- Arctic,
- Aerosol Optical Depth,
- Bayesian Maximum Entropy,
- Data Fusion

FullText(HTML)

References(50)

References

[1]	余光明, 徐建中, 任贾文. 青藏高原大气气溶胶研究进展[J]. 冰川冻土, 2012, 34(3): 609−617. Yu Guangming, Xu Jianzhong, Ren Jiawen. The progress of aerosol research in the Tibetan Plateau[J]. Journal of Glaciology and Geocryology, 2012, 34(3): 609−617.
[2]	Rantanen M, Karpechko A Y, Lipponen A, et al. The Arctic has warmed nearly four times faster than the globe since 1979[J]. Communications Earth & Environment, 2022, 3(1): 168. doi: 10.1038/s43247-022-00498-3
[3]	Perovich D K, Polashenski C. Albedo evolution of seasonal Arctic sea ice[J]. Geophysical Research Letters, 2012, 39(8): L08501. doi: 10.1029/2012gl051432
[4]	Boisvert L N, Petty A A, Stroeve J C. The impact of the extreme winter 2015/16 arctic cyclone on the Barents–Kara Seas[J]. Monthly Weather Review, 2016, 144(11): 4279−4287. doi: 10.1175/MWR-D-16-0234.1
[5]	Nummelin A, Li C, Hezel P J. Connecting ocean heat transport changes from the midlatitudes to the Arctic Ocean[J]. Geophysical Research Letters, 2017, 44(4): 1899−1908. doi: 10.1002/2016GL071333
[6]	Middlemas E A, Kay J E, Medeiros B M, et al. Quantifying the influence of cloud radiative feedbacks on arctic surface warming using cloud locking in an earth system model[J]. Geophysical Research Letters, 2020, 47(15): e2020GL089207. doi: 10.1029/2020GL089207
[7]	Pithan F, Mauritsen T. Arctic amplification dominated by temperature feedbacks in contemporary climate models[J]. Nature Geoscience, 2014, 7(3): 181−184. doi: 10.1038/ngeo2071
[8]	Park J Y, Kug J S, Bader J, et al. Amplified Arctic warming by phytoplankton under greenhouse warming[J]. Proceedings of the National Academy of Sciences of the United States of America, 2015, 112(19): 5921−5926.
[9]	Campen H I, Arévalo-Martínez D L, Artioli Y, et al. The role of a changing Arctic Ocean and climate for the biogeochemical cycling of dimethyl sulphide and carbon monoxide[J]. Ambio, 2022, 51(2): 411−422. doi: 10.1007/s13280-021-01612-z
[10]	Shindell D, Faluvegi G. Climate response to regional radiative forcing during the twentieth century[J]. Nature Geoscience, 2009, 2(4): 294−300. doi: 10.1038/ngeo473
[11]	Bond T C, Doherty S J, Fahey D W, et al. Bounding the role of black carbon in the climate system: a scientific assessment[J]. Journal of Geophysical Research: Atmospheres, 2013, 118(11): 5380−5552. doi: 10.1002/jgrd.50171
[12]	Twomey S. The influence of pollution on the shortwave albedo of clouds[J]. Journal of the Atmospheric Sciences, 1977, 34(7): 1149−1152. doi: 10.1175/1520-0469(1977)034<1149:tiopot>2.0.co;2
[13]	Kaufman Y J, Tanré D, Boucher O. A satellite view of aerosols in the climate system[J]. Nature, 2002, 419(6903): 215−223. doi: 10.1038/nature01091
[14]	Popp T, De Leeuw G, Bingen C, et al. Development, production and evaluation of aerosol climate data records from European satellite observations (Aerosol_cci)[J]. Remote Sensing, 2016, 8(5): 421. doi: 10.3390/rs8050421
[15]	唐健雄. 基于GF-5 DPC数据的PM_2.5估算方法研究[D]. 廊坊: 北华航天工业学院, 2023. Tang Jianxiong. Research on PM_2.5 estimation method based on GF-5 DPC data[D]. Langfang: North China Institute of Aerospace Engineering, 2023. (查阅网上资料, 未找到对应的英文翻译, 请确认)
[16]	杨军, 咸迪, 唐世浩. 风云系列气象卫星最新进展及应用[J]. 卫星应用, 2018(11): 8−14. Yang Jun, Xian Di, Tang Shihao. Latest developments and applications of the Fengyun series meteorological satellites[J]. Satellite Application, 2018(11): 8−14. (查阅网上资料, 未找到对应的英文翻译, 请确认)
[17]	顾俊杰, 戴一枫, 金林林. 卫星遥感气溶胶光学厚度的相关研究综述[J]. 科技资讯, 2017, 15(19): 219−220, 222. Gu Junjie, Dai Yifeng, Jin Linlin. A review on the research on satellite remote sensing aerosol optical thickness[J]. Science & Technology Information, 2017, 15(19): 219−220, 222. (查阅网上资料, 未找到对应的英文翻译, 请确认)
[18]	晏阳天, 白学志. 北极气溶胶时空变化特征及其对向下短波辐射的影响[J/OL]. 海洋科学, 1−19. https://link.cnki.net/urlid/37.1151.P.20250513.1627.002, 2025-08-23. Yan Yangtian, Bai Xuezhi. Temporal and spatial variation characteristics of Arctic aerosol and its influence on downward short-wave radiation[J/OL]. Marine Sciences, 1−19. https://link.cnki.net/urlid/37.1151.P.20250513.1627.002, 2025-08-23.
[19]	Swain B, Vountas M, Deroubaix A, et al. Retrieval of aerosol optical depth over the Arctic cryosphere during spring and summer using satellite observations[J]. Atmospheric Measurement Techniques, 2024, 17(1): 359−375. doi: 10.5194/amt-17-359-2024
[20]	杨艳丽, 常亮. MODIS北极气溶胶光学厚度的精度验证与分布特征研究[J]. 极地研究, 2022, 34(1): 62−71. doi: 10.13679/j.jdyj.20210028 Yang Yanli, Chang Liang. Accuracy validation and distribution characteristics of Arctic MODIS aerosol optical depth[J]. Chinese Journal of Polar Research, 2022, 34(1): 62−71. doi: 10.13679/j.jdyj.20210028
[21]	Tomasi C, Kokhanovsky A A, Lupi A, et al. Aerosol remote sensing in polar regions[J]. Earth-Science Reviews, 2015, 140: 108−157. doi: 10.1016/j.earscirev.2014.11.001
[22]	Schmale J, Sharma S, Decesari S, et al. Pan-Arctic seasonal cycles and long-term trends of aerosol properties from 10 observatories[J]. Atmospheric Chemistry and Physics, 2022, 22(5): 3067−3096. doi: 10.5194/acp-22-3067-2022
[23]	Li Deren. Remote sensing image fusion: a practical guide[J]. Geo-Spatial Information Science, 2017, 20(1): 56.
[24]	贾永红, 李德仁. 多源遥感影像像素级融合分类与决策级分类融合法的研究[J]. 武汉大学学报(信息科学版), 2001, 26(5): 430−434. Jia Yonghong, Li Deren. An approach of classification based on pixel level and decision level fusion of Multi-source images in remote sensing[J]. Geomatics and Information Science of Wuhan University, 2001, 26(5): 430−434.
[25]	Nguyen H, Cressie N, Braverman A. Spatial statistical data fusion for remote sensing applications[J]. Journal of the American Statistical Association, 2012, 107(499): 1004−1018. doi: 10.1080/01621459.2012.694717
[26]	Yang Jing, Hu Maogui. Filling the missing data gaps of daily MODIS AOD using spatiotemporal interpolation[J]. Science of the Total Environment, 2018, 633: 677−683. doi: 10.1016/j.scitotenv.2018.03.202
[27]	Christakos G. A Bayesian/maximum-entropy view to the spatial estimation problem[J]. Mathematical Geology, 1990, 22(7): 763−777. doi: 10.1007/BF00890661
[28]	He Junyu, Christakos G, Wu Jiaping, et al. Spatiotemporal BME characterization and mapping of sea surface chlorophyll in Chesapeake Bay (USA) using auxiliary sea surface temperature data[J]. Science of the Total Environment, 2021, 794: 148670. doi: 10.1016/j.scitotenv.2021.148670
[29]	Tang Qingxin, Bo Yanchen, Zhu Yuxin. Spatiotemporal fusion of multiple‐satellite aerosol optical depth (AOD) products using Bayesian maximum entropy method[J]. Journal of Geophysical Research: Atmospheres, 2016, 121(8): 4034−4048. doi: 10.1002/2015JD024571
[30]	Yu H L, Wu Yuzhang, Cheung S Y. A data assimilation approach for groundwater parameter estimation under Bayesian maximum entropy framework[J]. Stochastic Environmental Research and Risk Assessment, 2020, 34(5): 709−721. doi: 10.1007/s00477-020-01795-z
[31]	Chen Li, Liang Shuang, Li Xiaoli, et al. A hybrid approach to estimating long-term and short-term exposure levels of ozone at the national scale in China using land use regression and Bayesian maximum entropy[J]. Science of the Total Environment, 2021, 752: 141780. doi: 10.1016/j.scitotenv.2020.141780
[32]	Li Aihua, Bo Yanchen, Zhu Yuxin, et al. Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method[J]. Remote Sensing of Environment, 2013, 135: 52−63. doi: 10.1016/j.rse.2013.03.021
[33]	Xia Xinghui, Zhao Bin, Zhang Tianhao, et al. Satellite-derived aerosol optical depth fusion combining active and passive remote sensing based on Bayesian maximum entropy[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 4100313. doi: 10.1109/tgrs.2021.3051799
[34]	Zhu Hao, Cheng Tianhai, Li Xiaoyang, et al. Fusion of multisource satellite AOD products via Bayesian maximum entropy with explicit a priori knowledge[J]. IEEE Transactions on Geoscience and Remote Sensing, 2023, 61: 4103112. doi: 10.1109/tgrs.2023.3285943/mm1
[35]	Aldabash M, Bektas Balcik F, Glantz P. Validation of MODIS C6.1 and MERRA-2 AOD using AERONET observations: a comparative study over turkey[J]. Atmosphere, 2020, 11(9): 905. doi: 10.3390/atmos11090905
[36]	Tao Minghui, Wang Jun, Li Rong, et al. Characterization of aerosol type over East Asia by 4.4 km MISR product: first insight and general performance[J]. Journal of Geophysical Research: Atmospheres, 2020, 125(13): e2019JD031909. doi: 10.1029/2019JD031909
[37]	Abdou W A, Diner D J, Martonchik J V, et al. Comparison of coincident Multiangle Imaging Spectroradiometer and Moderate Resolution Imaging Spectroradiometer aerosol optical depths over land and ocean scenes containing Aerosol Robotic Network sites[J]. Journal of Geophysical Research: Atmospheres, 2005, 110(D10): 2004JD004693. doi: 10.1029/2004JD004693
[38]	Kahn R A, Gaitley B J, Garay M J, et al. Multiangle Imaging SpectroRadiometer global aerosol product assessment by comparison with the Aerosol Robotic Network[J]. Journal of Geophysical Research: Atmospheres, 2010, 115(D23): D23209. doi: 10.1029/2010jd014601
[39]	Li Long, Shi Runhe, Zhang Lu, et al. The data fusion of aerosol optical thickness using universal kriging and stepwise regression in East China[C]//Proceedings of SPIE 9221, Remote Sensing and Modeling of Ecosystems for Sustainability XI. San Diego: SPIE, 2014: 922112.
[40]	Levy R C, Mattoo S, Munchak L A, et al. The Collection 6 MODIS aerosol products over land and ocean[J]. Atmospheric Measurement Techniques, 2013, 6(11): 2989−3034. doi: 10.5194/amt-6-2989-2013
[41]	Chung M. Gaussian Kernel Smoothing[M]. University of Wisconsin-Madison, 2020. (查阅网上资料, 未找到本条文献信息, 请确认并补充出版地信息)
[42]	Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning[M]. New York: Springer, 2001.
[43]	Kennedy J, Eberhart R. Particle swarm optimization[C]//Proceedings of International Conference on Neural Networks. Perth: IEEE, 1995: 1942-1948.
[44]	Tyagi N, Bhargava D, Ahlawat A. Implementation of particle swarm optimization algorithm inspired by the social behaviour of birds[C]//Proceedings of the 2024 4th International Conference on Technological Advancements in Computational Sciences. Tashkent: IEEE, 2024: 750-754.
[45]	Karaboga D. An idea based on honey bee swarm for numerical optimization[R]. Technical Report - TR06, Kayseri: Erciyes University, 2005.
[46]	翟盘茂, 任福民, 张强. 中国降水极值变化趋势检测[J]. 气象学报, 1999, 57(2): 81−89. Zhai Panmao, Ren Fumin, Zhang Qiang. Detection of trends in China's precipitation extremes[J]. Acta Meteorologica Sinica, 1999, 57(2): 81−89.
[47]	Remer L A, Tanré D, Kaufman Y J, et al. Validation of MODIS aerosol retrieval over ocean[J]. Geophysical Research Letters, 2002, 29(12): 1618.
[48]	Shaw G E. The arctic haze phenomenon[J]. Bulletin of the American Meteorological Society, 1995, 76(12): 2403−2414.
[49]	Damoah R, Spichtinger N, Forster C, et al. Around the world in 17 days – hemispheric-scale transport of forest ﬁre smoke from Russia in May 2003[J]. Atmospheric Chemistry and Physics, 2004, 4(5): 1311−1321. doi: 10.5194/acpd-4-1449-2004
[50]	Stohl A. Characteristics of atmospheric transport into the Arctic troposphere[J]. Journal of Geophysical Research: Atmospheres, 2006, 111(D11): D11306. doi: 10.1029/2005jd006888