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Retrieval of aerosol optical depth over urban area by coupling the characteristics of surface directional reflection

  • role： First author第一作者
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

  • Email：xptian@yic.ac.cn
  • Introduction：田信鹏，研究方向为大气环境遥感。E-mail： xptian@yic.ac.cn
TIAN Xinpeng12,  
  • role： Corresponding author通信作者
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

  • Email：zqgao@yic.ac.cn
  • Introduction：高志强，研究方向为定量遥感、海岸带遥感应用等。E-mail： zqgao@yic.ac.cn
GAO Zhiqiang12*,  
  • Affiliation：

    Peng Cheng Laboratory, Shenzhen 518055, China

LIU Qiang3,  
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

WANG De12,  
  • Affiliation：

    CAS Key Laboratory of Coastal Environmental Processes and Ecological Remediation, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

    Shandong Key Laboratory of Coastal Environmental Processes, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China

WANG Yueqi12

Resümee

Aerosols play an important role in determining the Earth's radiation budget and its impact on climate change. Aerosol optical depth (AOD) is a crucial fundamental parameter for meteorological observation and a basic optical property of aerosol derived from satellites. Over land, the aerosol contribution in satellite signals is small compared with the surface, making it difficult to separate the aerosol path radiance from satellite measurements, particularly over the urban area. In the past several decades, numerous different AOD retrieval algorithms have been proposed by using different satellite sensors, but most of them do not consider surface anisotropy.The main purpose of this work is to improve the accuracy of aerosol retrievals and reduce the uncertainty of the operational MODIS AOD products over mixed surfaces. On this basis, a new generic high-performance aerosol retrieval algorithm is presented and explained. The new method is developed by coupling the non-Lambertian atmospheric radiative transfer model and semiempirical linear kernel-driven BRDF model. First, an a priori surface BRDF shape parameter database is constructed using the daily MODIS BRDF/Albedo product by using penalized least square regression based on a 3D discrete cosine transform (DCT-PLS) method. Then, the estimation of surface reflectance, including bidirectional reflectance, directional to hemispheric reflectance, hemispheric to directional reflectance, and bi-hemispheric reflectance (also called white-sky albedo, WSA), is based on this database and kernel-driven BRDF model. The presented method is tested on the Landsat 8 OLI images around the Beijing area, which features highly heterogeneous surfaces and severe air pollution problems. AOD retrievals with 500 m resolution can be successfully obtained over dark and bright surfaces.An accuracy assessment of the new algorithm, WSA-derived and HARLS AOD retrievals against AERONET AOD, from the four selected stations indicated the superiority of new algorithm, which is reflected in the high PWE and low RMSE. The comparison results show that the new algorithm is in good agreement with ground-based AOD (R=0.911) compared with the WSA-derived and HARLS AOD retrievals. Furthermore, the new algorithm and MODIS aerosol algorithms have similar spatial patterns of AOD. The new algorithm significantly improves the accuracy of aerosol retrievals, which is verified by AERONET AOD data, especially over brighter surfaces, because surface anisotropy is considered in this algorithm. The new algorithm can provide a detailed AOD spatial distribution over mixed surfaces and shows high ability in capturing fine-scale features. The new algorithm and MAIAC AOD retrievals have a similar spread of uncertainty envelopes. However, the new algorithm AOD retrievals have a higher correlation and smaller RMSE than the MAIAC retrievals, and the number of collections with AERONET for the new algorithm is almost 1.5 times those for MAIAC.This new AOD retrieval algorithm can provide a possibility for high-precision urban aerosol remote sensing monitoring and solve other pressing issues, such as long-term trend analysis of urban aerosols and air quality conditions, especially in heavily polluted areas. Based on the collocated observations, the new algorithm achieved satisfactory retrieval accuracy. However, several issues remain to be solved in the future. First, the retrieval errors of the MODIS BRDF kernel parameters are also a major source of uncertainty. Second, more analyses of the aerosol models and model selection are required. Third, the application in other regions and sensors is required in further work to evaluate the applicability of new algorithm.

Schlüsselwort

aerosol optical depth;surface anisotropy;kernel-driven BRDF model;MODIS;Landsat 8 OLI

References

  1. 1.
    Chen J, Ma L, Chen X H and Rao Y H. 2016. Research progress of spectral mixture analysis. Journal of Remote Sensing, 20(5):1102-1109
  2. 2.
    Chu D A, Kaufman Y J, Ichoku C, Remer L A, Tanré D and Holben B N. 2002. Validation of MODIS aerosol optical depth retrieval over land. Geophysical Research Letters, 29(12): 1617-1620
  3. 3.
    Diner D J, Martonchik J V, Kahn R A, Pinty B, Gobron N, Nelson D L and Holben B N. 2005. Using angular and spectral shape similarity constraints to improve MISR aerosol and surface retrievals over land. Remote Sensing of Environment, 94(2): 155-171
  4. 4.
    Garcia D. 2010. Robust smoothing of gridded data in one and higher dimensions with missing values. Computational Statistics and Data Analysis, 54(4): 1167-1178
  5. 5.
    Gatebe C K and King M D. 2016. Airborne spectral BRDF of various surface types (ocean, vegetation, snow, desert, wetlands, cloud decks, smoke layers) for remote sensing applications. Remote Sensing of Environment, 179, 131-148
  6. 6.
    Ge B Y, Yang L K, Chen X F, Li Z Q, Mei X D and Liu L. 2018. Study on aerosol optical depth retrieval over land from Himawari-8 data based on dark target method. Journal of Remote Sensing, 22(1): 38-50
  7. 7.
    Giles D M, Sinyuk A, Sorokin M G, Schafer J S, Smirnov A, Slutsker I, Eck T F, Holben B N, Lewis J R, Campbell J R, Welton E J, Korkin S V and Lyapustin A I. 2019. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database - automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. Atmospheric Measurement Techniques, 12(1): 169-209
  8. 8.
    Holben B N, Eck T F, Slutsker I, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I and Smirnov A. 1998. AERONET-A federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1-16
  9. 9.
    Hsu N C, Jeong M J, Bettenhausen C, Sayer A M, Hansell R, Seftor C S, Huang J and Tsay S C. 2013. Enhanced Deep Blue aerosol retrieval algorithm: the second generation. Journal of Geophysical Research: Atmospheres, 118(16): 9296-9315
  10. 10.
    Hsu N C, Tsay S C, King M D and Herman J R. 2004. Aerosol properties over bright-reflecting source regions. IEEE Transactions on Geoscience and Remote Sensing, 42(3): 557-569
  11. 11.
    Jia C, Sun L, Chen Y F, Zhang X K, Wang W Y and Wang Y J. 2020. Inversion of aerosol optical depth for Landsat 8 OLI data using deep belief network. Journal of Remote Sensing, 24(10): 1180-1192
  12. 12.
    Kaufman Y J, Wald A E, Remer L A, Gao B C, Li R R and Flynn L. 1997. The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol. IEEE Transactions on Geoscience and Remote Sensing, 35(5): 1286-1298
  13. 13.
    Levy R C, Mattoo S, Munchak L A, Remer L A, Sayer A M, Patadia F and Hsu N C. 2013. The Collection 6 MODIS aerosol products over land and ocean. Atmospheric Measurement Techniques, 6(11): 2989-3034
  14. 14.
    Levy R C, Remer L A, Kleidman R G, Mattoo S, Ichoku C, Kahn R and Eck T F. 2010. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmospheric Chemistry and Physics, 10(21): 10399-10420
  15. 15.
    Li Z Q, Xie Y S, Zhang Y, Li L, Xu H, Li K T and Li D H. 2019. Advance in the remote sensing of atmospheric aerosol composition. Journal of Remote Sensing, 23(3): 359-373
  16. 16.
    Liang S L, Li X W and Wang J D. 2013. Quantitative remote sensing: Idea and algorithm. Beijing: Science Press
  17. 17.
    Liang S L, Fang H L, Chen M Z, Shuey C J, Walthall C, Daughtry C, Morisette J, Schaaf C and Strahler A. 2002. Validating MODIS land surface reflectance and albedo products: methods and preliminary results. Remote Sensing of Environment, 83(1/2): 149-162
  18. 18.
    Lucht W, Schaaf C B and Strahler A H. 2000. An algorithm for the retrieval of albedo from space using semiempirical BRDF models. IEEE Transactions on Geoscience and Remote Sensing, 38(2): 977-998
  19. 19.
    Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765
  20. 20.
    Omar A H, Won J G, Winker D M, Yoon S C, Dubovik O and McCormick M P. 2005. Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements. Journal of Geophysical Research: Atmospheres, 110(D10): D10S14
  21. 21.
    Pokrovsky O and Roujean J L. 2003. Land surface albedo retrieval via kernel-based BRDF modeling: I. Statistical inversion method and model comparison. Remote Sensing of Environment, 84(1): 100-119
  22. 22.
    Qin W H, Herman J R and Ahmad Z. 2001. A fast, accurate algorithm to account for non-Lambertian surface effects on TOA radiance. Journal of Geophysical Research: Atmospheres, 106(D19): 22671-22684
  23. 23.
    Sayer A M, Munchak L A, Hsu N C, Levy R C, Bettenhausen C and Jeong M J. 2014. MODIS Collection 6 aerosol products: Comparison between Aqua’s e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations. Journal of Geophysical Research: Atmospheres, 119(24): 13965-13989
  24. 24.
    She L, Xue Y, Yang X H, Leys J, Guang J, Che Y H, Fan C, Xie Y Q and Li Y. 2019. Joint retrieval of aerosol optical depth and surface reflectance over land using geostationary satellite data. IEEE Transactions on Geoscience and Remote Sensing, 57(3): 1489-1501
  25. 25.
    Shi S Y, Cheng T H, Gu X F, Chen H, Guo H, Wang Y, Bao F W, Xu B R, Wang W N, Zuo X, Meng C and Zhang X C. 2017. Synergy of MODIS and AATSR for better retrieval of aerosol optical depth and land surface directional reflectance. Remote Sensing of Environment, 195: 130-141
  26. 26.
    Smirnov A, Holben B N, Eck T F, Dubovik O and Slutsker I. 2000. Cloud-screening and quality control algorithms for the AERONET database. Remote Sensing of Environment, 73(3): 337-349
  27. 27.
    Strahler A H, Lucht W, Schaaf C B, Tsang T, Gao F, Li X W, Muller J P, Lewis P and Barnsley M. 1999. MODIS BRDF/Albedo Product: Algorithm Theoretical Basis Document Version 5.0. MODIS Documentation
  28. 28.
    Su T N, Laszlo I, Li Z Q, Wei J and Kalluri S. 2020. Refining aerosol optical depth retrievals over land by constructing the relationship of spectral surface reflectances through deep learning: Application to Himawari-8. Remote Sensing of Environment, 251: 112093-112107
  29. 29.
    Sun L, Wei J, Bilal M, Tian X P, Jia C, Guo Y M and Mi X T. 2016. Aerosol optical depth retrieval over bright areas using Landsat 8 OLI images. Remote Sensing, 8(1): 23-36
  30. 30.
    Sun L, Yu H Y, Fu Q Y, Wang J, Tian X P and Mi X T. 2016. Aerosol optical depth retrieval and atmospheric correction application for GF-1 PMS supported by land surface reflectance data. Journal of Remote Sensing, 20(2): 216-228
  31. 31.
    Tang J K, Xue Y, Yu T and Guan Y N. 2005. Aerosol optical thickness determination by exploiting the synergy of TERRA and AQUA MODIS. Remote Sensing of Environment, 94(3): 327-334
  32. 32.
    Tian X P, Liu Q, Gao Z Q, Wang Y Q, Li X H and Wei J. 2021a. Improving MODIS aerosol estimates over land with the surface BRDF reflectances using the 3-D discrete cosine transform and RossThick-LiSparse models. IEEE Transactions on Geoscience and Remote Sensing, 59(12): 9851-9860
  33. 33.
    Tian X P, Gao Z Q, Liu Q., Wang Y Q and Li X H. 2021b. Estimation of BRDF model kernel weights under an a priori knowledge-aided constraint. Remote Sensing Letters, 12(2): 146-155
  34. 34.
    Tian X P, Sun L, Liu Q and Li X H. 2018. Retrieval of high-resolution aerosol optical depth using Landsat 8 OLI data over Beijing. Journal of Remote Sensing, 22(1): 51-63
  35. 35.
    Tian X P, Liu Q, Song Z W, Dou B C and Li X H. 2018. Aerosol optical depth retrieval from Landsat 8 OLI images over urban areas supported by MODIS BRDF/Albedo Data. IEEE Geoscience and Remote Sensing Letters, 15(7): 976-980
  36. 36.
    Vermote E F, Roger J C, Sinyuk A, Saleous N and Dubovik O. 2007. Fusion of MODIS-MISR aerosol inversion for estimation of aerosol absorption. Remote Sensing of Environment, 107(1/2): 81-89
  37. 37.
    Wei J, Li Z Q, Sun L, Yang Y K, Zhao C F and Cai Z X. 2019. Enhanced aerosol estimations from Suomi-NPP VIIRS images over heterogeneous surfaces. IEEE Transactions on Geoscience and Remote Sensing, 57(12): 9534-9543
  38. 38.
    Xiao Z Q, Liang S L, Wang T T and Liu Q. 2015. Reconstruction of satellite-retrieved land-surface reflectance based on temporally-continuous vegetation indices. Remote Sensing, 7(8): 9844-9864
  39. 39.
    Xue Y, Guo J P and Zhang X Y. 2009. Aerosol optical thickness retrieval over non-Lambertian land surface with synergistic use of AATSR radiance measurements and MODIS derived Albedo Model Parameters. Atmospheric Research, 93(4): 736-746
  40. 40.
    Yang L K, Hu X Q, Wang H, He X W, Liu P, Xu N, Yang Z D and Zhang P. 2022. Preliminary test of quantitative capability in aerosol retrieval over land from MERSI-II onboard FY-3D. National Remote Sensing Bulletin, 26(5): 923-940
  41. 41.
    Yang L K, Xue Y, Guang J, Kazemian H, Zhang J H and Li C. 2014. Improved aerosol optical depth and Ångstrom exponent retrieval over land from MODIS based on the non-lambertian forward model. IEEE Geoscience and Remote Sensing Letters, 11(9): 1629-1633
  42. 42.
    Zhang H, Jiao Z T, Dong Y D, Li J Y and Li X W. 2015. Albedo retrieved from BRDF archetype and surface directional reflectance. Journal of Remote Sensing, 19(3): 355-367.
  43. 43.
    Zhu Z, Wang S X and Woodcock C E. 2015. Improvement and expansion of the Fmask algorithm: cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images. Remote Sensing of Environment, 159: 269-277

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