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Seasonal deviation correction enhanced BGIM downscaling algorithm for remote sensing AOD products

  • role： First author第一作者
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email：LiaYu798@163.com
  • Introduction：梁玉，研究方向为大气气溶胶应用。E-mail： LiaYu798@163.com
LIANG Yu,  
  • role： Corresponding author通信作者
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

  • Email：210010@csu.edu.cn
  • Introduction：邹滨，研究方向为大气污染遥感制图、环境地理信息系统建模与服务。E-mail： 210010@csu.edu.cn
ZOU Bin*,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

FENG Huihui,  
  • Affiliation：

    School of Geosciences and Info-physics, Central South University, Changsha 410083, China

LIU Ning

resumen

Satellite MODIS Aerosol Optical Depth (AOD) products based on Dark Target (DT) retrieval algorithm at 3 km resolution have been widely used in the ground air pollution monitoring. However, due to the limitations of DT retrieval algorithm, these products missed a large number of pixels with low spatiotemporal coverage and limited accuracy. By contrast, MODIS 10 km DT_DB_Combined AOD products integrate products based on DT and Deep Blue (DB) retrieval algorithms. However, to some extent, DT_DB_Combined AOD products can make up for the coverage and accuracy shortcomings of MODIS 3 km DT AOD data products, the resolution of which is low. Moreover, affected by the seasonal variation of aerosol component sources and the seasonal error of surface reflectance estimation, the accuracy of MODIS AOD data products also exhibits seasonal variation. This study takes Beijing‐Tianjin‐Hebei region as the experimental area and uses MODIS 10 km DT_DB_Combined AOD products as the material. Geostatistical Inverse Model (GIM) downscaling method, which considers the spatial covariance function differences of AOD data products at different scales, is introduced. At the same time, to consider the seasonal variation characteristics of AOD, the seasonal bias correction model for the MODIS 10 km DT_DB_Combined AOD products using accurate AERONET monitoring data is developed. On this basis, the Bias-corrected GIM (BGIM) downscaling algorithm coupled with seasonal bias correction model is further proposed. The AERONET ground observation data and MODIS 3 km DT AOD products are employed as the absolute and relative evaluation reference for the BGIM downscaling results. Results show that the absolute evaluated accuracies of the downscaled MODIS 3 km DT_DB_Combined AOD data, 10 km DT_DB_Combined AOD, and 3 km DT AOD data products are relatively close; the corresponding R2 values are 0.79, 0.70, and 0.71, respectively. Compared with MODIS 3 km DT AOD products, the relative evaluated R result of the seasonal corrected MODIS 3 km DT_DB_Combined AOD data is higher than 0.93. In addition, the temporal coverage and spatial coverage are increased by 11.21% and 11.44%, respectively. The spatial coverage in spring and winter is relatively higher among four seasons. The results confirm that the BGIM downscaling algorithm can not only effectively improve the resolution and accuracy of MODIS 10 km DT_DB_Combined AOD products, but also promote the spatiotemporal coverage compared with MODIS 3 km AOD products.

palabra clave

remote sensing;AOD;downscaling;BGIM;spatial-temporal statistics;Beijing‐Tianjin‐Hebei

References

  1. 1.
    Byrd R H, Hribar M E and Nocedal J. 1999. An interior point algorithm for large-scale nonlinear programming. SIAM Journal on Optimization, 9(4): 877-900
  2. 2.
    Che H Z, Yang L K, Liu C, Xia X G, Wang Y Q, Wang H, Wang H, Lu X F and Zhang X Y. 2019. Long-term validation of MODIS C6 and C6.1 Dark Target aerosol products over China using CARSNET and AERONET. Chemosphere, 236: 124268
  3. 3.
    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): MOD2-1—MOD2-4
  4. 4.
    De Leeuw G, Sogacheva L, Rodriguez E, Kourtidis K, Georgoulias A K, Alexandri G, Amiridis V, Proestakis E, Marinou E, Xue Y and Van Der Ronald A. 2018. Two decades of satellite observations of AOD over mainland China using ATSR-2, AATSR and MODIS/Terra: data set evaluation and large-scale patterns. Atmospheric Chemistry and Physics, 18(3): 1573-1592
  5. 5.
    Duan L M, Fan K K, Li W and Liu T X. 2019. Spatial downscaling algorithm of TRMM precipitation based on multiple high-resolution satellite data for Inner Mongolia, China. Theoretical and Applied Climatology, 135(1/2): 45-59
  6. 6.
    Guang J, Xue Y, Li X W, Bai L Y, Wang Y, Li Y J, Wan W and Guo J P. 2008. Multi-spatial scale aerosol optical thickness mapping for Beijing using remote sensing technique. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B8): 485-490
  7. 7.
    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
  8. 8.
    Huth R. 2002. Statistical downscaling of daily temperature in central Europe. Journal of Climate, 15(13): 1731-1742
  9. 9.
    Ichoku C, Chu D A, Mattoo S, Kaufman Y J, Remer L A, Tanré D, Slutsker I and Holben B N. 2002. A spatio-temporal approach for global validation and analysis of MODIS aerosol products. Geophysical Research Letters, 29(12): MOD1-1—MOD1-4
  10. 10.
    Jing Y, Sun Y L, Fu H C, Ma H, Teng Q Y and Cui Y M. 2018. Temporal and spatial variation of aerosol optical depth and analysis of influencing factors in Beijing‐Tianjin‐Hebei region from 2010 to 2016. Environmental Science & Technology, 41(8) :104-113
  11. 11.
    Kaufman Y J, Tanré D, Remer L A, Vermote E F, Chu A and Holben B N. 1997. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. Journal of Geophysical Research: Atmospheres, 102(D14): 17051-17067
  12. 12.
    Kitanidis P K. 1995. Quasi-linear geostatistical theory for inversing. Water Resources Research, 31(10): 2411-2419
  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.
    Li B G, Yuan H S, Feng N and Tao S. 2009. Comparing MODIS and AERONET aerosol optical depth over China. International Journal of Remote Sensing, 30(24): 6519-6529
  15. 15.
    Li S S, Chen L F, Xiong X Z, Tao J H, Su L, Han D and Liu Y. 2013. Retrieval of the haze optical thickness in North China Plain using MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 51(5): 2528-2540
  16. 16.
    Li Z B, Wang N, Zhang Z L, Wang T T, Tao J H, Wang P, Ma S L, Xu B B and Fan M. 2020. Validation and analyzation of MODIS aerosol optical depth products over China. China Environmental Science, 40(10):4190-4204
  17. 17.
    Liang L W, Wang Z B, Fang C L and Sun Z. 2019. Spatiotemporal differentiation and coordinated development pattern of urbanization and the ecological environment of the Beijing-Tianjin-Hebei urban agglomeration. Acta Ecologica Sinica, 39(4): 1212-1225
  18. 18.
    Liang Y Z, Ge Y and Wang J H. 2015. Review of geostatistical-based downscaling. Remote Sensing Technology and Application, 30(1): 1-7
  19. 19.
    Liu H, Gao X M, Xie Z Y, Li T T and Zhang W J. 2015. Spatio-temporal characteristics of aerosol optical depth over Beijing-Tianjin-Hebei-Shanxi-Shandong region during 2000-2013. Acta Scientiae Circumstantiae, 35(5): 1506-1511
  20. 20.
    Liu N, Zou B, Feng H H, Wang W, Tang Y Q and Liang Y. 2019. Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China. Atmospheric Chemistry and Physics, 19(12): 8243-8268
  21. 21.
    Lyapustin A, Wang Y J, Korkin S and Huang D. 2018. MODIS Collection 6 MAIAC algorithm. Atmospheric Measurement Techniques, 11(10): 5741-5765
  22. 22.
    Malakar N K, Lary D J, Moore A, Gencaga D, Roscoe B, Albayrak A and Wei J. 2012. Estimation and bias correction of aerosol abundance using data-driven machine learning and remote sensing//2012 Conference on Intelligent Data Understanding. Boulder, CO, USA: IEEE: 24-30
  23. 23.
    Michalak A M, Bruhwiler L and Tans P P. 2004. A geostatistical approach to surface flux estimation of atmospheric trace gases. Journal of Geophysical Research: Atmospheres, 109(D14): D14109
  24. 24.
    Munchak L A, Levy R C, Mattoo S, Remer L A, Holben B N, Schafer J S, Hostetler C A and Ferrare R A. 2013. MODIS 3 km aerosol product: applications over land in an urban/suburban region. Atmospheric Measurement Techniques, 6(7): 1747-1759
  25. 25.
    Niu F, Li C C and Mao J T. 2006. A synergic application of TERRA and AQUA on retrieving aerosol optical depth over Land. Journal of Remote Sensing, 10(4): 456-462
  26. 26.
    Schaap M, Timmermans R M A, Koelemeijer R B A, De Leeuw G and Builtjes P J H. 2008. Evaluation of MODIS aerosol optical thickness over Europe using sun photometer observations. Atmospheric Environment, 42(9): 2187-2197
  27. 27.
    Stathopoulou M and Cartalis C. 2009. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sensing of Environment, 113(12): 2592-2605
  28. 28.
    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
  29. 29.
    Wang H L, Liu Q, Chen Y H, Sun R, Li X, Zhang H, Wei G, Hu J and Liu T Q. 2019. Applicability of MODIS C006 aerosol products in a typical environmental area of the Beijing-Tianjin-Hebei region. Environmental Science, 40(1): 44-54
  30. 30.
    Wang J, Brown D G and Hammerling D. 2013. Geostatistical inverse modeling for super-resolution mapping of continuous spatial processes. Remote Sensing of Environment, 139: 205-215
  31. 31.
    Wang K, Sun X J, Zhou Y B and Zhang C L. 2017. Validation of MODIS-Aqua aerosol products C051 and C006 over the Beijing-Tianjin-Hebei region. Atmosphere, 8(9): 172
  32. 32.
    Wang X Y, Xin J Y, Wang L L and Wang Y S. 2012. Aerosol optical depth observed by Chinese sun hazemeter network and comparison with MODIS products in three typical cities in China. Climatic and Environmental Research, 17(1): 37-45
  33. 33.
    Wei J, Peng Y R, Guo J P and Sun L. 2019. Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land. Atmospheric Environment, 206: 30-44
  34. 34.
    Xie Y Y, Wang Y X, Zhang K, Dong W H, Lv B L and Bai Y Q. 2015. Daily estimation of ground-level PM2.5 concentrations over Beijing using 3 km resolution MODIS AOD. Environmental Science and Technology, 49(20): 12280-12288
  35. 35.
    Yan L B, Liu X D. 2009. Seasonal variation of atmospheric aerosol and its relation to cloud faction over Beijing-Tianjin-Hebei region. Research of Environmental Sciences, 22(8): 924-931
  36. 36.
    Yang Y K, Sun L, Wei J, Tian X P and Jia C. 2016. Verification of MODIS C5 and C6 and their regional adaptability evaluation. Journal of Shandong University of Science and Technology (Natural Science), 35(5): 17-27
  37. 37.
    Yi N N, Zhang L, Liu W P, Tian P F and Dong Z L. 2017. Optical characteristics and radiative effects of atmospheric aerosol over Northwest China. Chinese Journal of Atmospheric Sciences, 41(2): 409-420
  38. 38.
    Zhang J. 2018. Assessment, mergence of satellite aerosol products, and their applications in estimating regional PM2.5 concentrations. Lanzhou: Lanzhou University
  39. 39.
    Zhang J, Liu H Y, Xin J Y, Zhang W Y, Xiao G J, Feng X Y and Wang L L. 2016. The comparison of MODIS and MERSI aerosol products in Shenyang. Journal of Remote Sensing, 20(4): 549-560
  40. 40.
    Zhao C, Liu Z R, Wang Q, Ban J, Chen N X and Li T T. 2019. High-resolution daily AOD estimated to full coverage using the random forest model approach in the Beijing-Tianjin-Hebei region. Atmospheric Environment, 203: 70-78
  41. 41.
    Zheng M, Yan C Q, Li X Y, Wang X S and Zhang Y H. 2014. A review of methods for quantifying secondary organic aerosol. China Environmental Science, 34(3): 555-564
  42. 42.
    Zou B, Chen J W, Zhai L, Fang X and Zheng Z. 2017. Satellite based mapping of ground PM2.5 concentration using generalized additive modeling. Remote Sensing, 9(1): 1

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