Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces

Abstract In this study, we evaluated the performance of the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products and compared them with the Collection 6 (C6) products over regions in China with multifarious underlying surfaces during 2001–2016. The AOD retrievals were validated against 20 AERONET (V3) sites, and the results show that the correlation coefficient (R) for dark target (DT) retrievals in C6.1 is 0.946, while the fraction within the expected error (EE) can be considered relatively low at only 54.03%. Deep blue (DB) retrievals in C6.1 have a slightly lower R value (0.931), but the other criteria are superior to DT. Comparing the results over urban and vegetation areas in C6.1, the overall quality of the DB retrievals is better than the DT retrievals in urban areas. The performance of DT is significantly superior to DB in the low elevation vegetation (LEV) areas. For the high elevation vegetation (HEV) areas, DB performs integrally better than DT. In the spatial distribution aspect in C6.1, most of the DB AOD values are less than those of DT, and the relationship between DT and DB varies with the land cover types and surface reflectance. For the AOD coverage in C6.1, DT retrievals with high coverage mainly distribute in east-central China. However, the effects of high surface reflectance lead to low AOD coverage in the southwest. In contrast, the AOD coverage of DB tends to be high in areas where the main land cover type is arid or semiarid and tends to be low in areas affected by snow. In terms of the comparison between C6.1 and C6, the overestimation of DT over urban areas in C6 is effectively mitigated in C6.1. However, a nearly systematic decline in DT is discovered in C6.1 as well. With respect to DB, consistent AOD coverage distribution is observed, with only subtle distinction. The AOD coverage of DB in C6.1 appears higher than that in C6 in the middle, south, and northeast of China. The quality of the DB retrievals in C6.1 increases slightly compared to C6, and the major improvements are observed for the coarse aerosol particles and for high elevation areas at surface reflectance from 0.11 to 0.2, respectively.

[1]  Aiwen Lin,et al.  What drives changes in aerosol properties over the Yangtze River Basin in past four decades? , 2018, Atmospheric Environment.

[2]  B. Holben,et al.  A spatio‐temporal approach for global validation and analysis of MODIS aerosol products , 2002 .

[3]  Liangfu Chen,et al.  Comparison and evaluation of the MODIS Collection 6 aerosol data in China , 2015 .

[4]  Xiaoxiong Xiong,et al.  Status of terra MODIS and aqua modis , 2003 .

[5]  Ming Zhang,et al.  Performance of the NPP-VIIRS and aqua-MODIS Aerosol Optical Depth Products over the Yangtze River Basin , 2018, Remote. Sens..

[6]  O. Boucher,et al.  Global estimate of aerosol direct radiative forcing from satellite measurements , 2005, Nature.

[7]  T. Eck,et al.  An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET , 2001 .

[8]  Muhammad Bilal,et al.  Validation of MODIS 3 km Resolution Aerosol Optical Depth Retrievals Over Asia , 2016, Remote. Sens..

[9]  Huanfeng Shen,et al.  The Relationships between PM2.5 and Meteorological Factors in China: Seasonal and Regional Variations , 2017, International journal of environmental research and public health.

[10]  Sha Xu,et al.  Effect of Land Use and Cover Change on Air Quality in Urban Sprawl , 2016 .

[11]  Lorraine Remer,et al.  The MODIS 2.1-μm channel-correlation with visible reflectance for use in remote sensing of aerosol , 1997, IEEE Trans. Geosci. Remote. Sens..

[12]  C. Small Multitemporal analysis of urban reflectance , 2002 .

[13]  Bo Huang,et al.  MODIS 3 km and 10 km aerosol optical depth for China: Evaluation and comparison , 2017 .

[14]  Muhammad Bilal,et al.  Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms , 2014 .

[15]  Ying Liu,et al.  Characteristic and Driving Factors of Aerosol Optical Depth over Mainland China during 1980-2017 , 2018, Remote. Sens..

[16]  Liang-pei Zhang,et al.  Point-surface fusion of station measurements and satellite observations for mapping PM 2.5 distribution in China: Methods and assessment , 2016, 1607.02976.

[17]  Lorraine A. Remer,et al.  A surface reflectance scheme for retrieving aerosol optical depth over urbansurfaces in MODIS Dark Target retrieval algorithm , 2016 .

[18]  Wei Gong,et al.  Spatial‐temporal characteristics of aerosol loading over the Yangtze River Basin during 2001–2015 , 2018 .

[19]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[20]  Bo Huang,et al.  Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002 to 2015 , 2016 .

[21]  M. Molina,et al.  Secondary organic aerosol formation from anthropogenic air pollution: Rapid and higher than expected , 2006 .

[22]  Michael D. King,et al.  Aerosol properties over bright-reflecting source regions , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Lin Sun,et al.  Verification, improvement and application of aerosol optical depths in China Part 1: Inter-comparison of NPP-VIIRS and Aqua-MODIS , 2018 .

[24]  L. Remer,et al.  The Collection 6 MODIS aerosol products over land and ocean , 2013 .

[25]  Alan H. Strahler,et al.  The Moderate Resolution Imaging Spectroradiometer (MODIS): land remote sensing for global change research , 1998, IEEE Trans. Geosci. Remote. Sens..

[26]  Robert C. Levy,et al.  MODIS Collection 6 aerosol products: Comparison between Aqua's e‐Deep Blue, Dark Target, and “merged” data sets, and usage recommendations , 2014 .

[27]  LI Chengcai,et al.  Validation of MODIS derived aerosol optical depth over the Yangtze River Delta in China , 2010 .

[28]  Jietai Mao,et al.  Characteristics of distribution and seasonal variation of aerosol optical depth in eastern China with MODIS products , 2003, Science Bulletin.

[29]  Liangpei Zhang,et al.  Estimating Ground‐Level PM2.5 by Fusing Satellite and Station Observations: A Geo‐Intelligent Deep Learning Approach , 2017, 1707.03558.

[30]  M. Brauer,et al.  Global Estimates of Ambient Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth: Development and Application , 2010, Environmental health perspectives.

[31]  Ming Zhang,et al.  Improving the Estimation of Daily Aerosol Optical Depth and Aerosol Radiative Effect Using an Optimized Artificial Neural Network , 2018, Remote. Sens..

[32]  Alexander Smirnov,et al.  Multiangle Imaging SpectroRadiometer global aerosol product assessment by comparison with the Aerosol Robotic Network , 2010 .

[33]  Majid Ezzati,et al.  Fine-particulate air pollution and life expectancy in the United States. , 2009, The New England journal of medicine.

[34]  E. Vermote,et al.  Second‐generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance , 2007 .

[35]  Damien Sulla-Menashe,et al.  MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets , 2010 .

[36]  Yoram J. Kaufman,et al.  Dust transport and deposition observed from the Terra‐Moderate Resolution Imaging Spectroradiometer (MODIS) spacecraft over the Atlantic Ocean , 2005 .

[37]  Muhammad Bilal,et al.  Evaluation of the NDVI-Based Pixel Selection Criteria of the MODIS C6 Dark Target and Deep Blue Combined Aerosol Product , 2017, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[38]  Xiaoxiong Xiong,et al.  Validation of MODIS aerosol optical depth product over China using CARSNET measurements , 2011 .

[39]  A. Chédin,et al.  Infrared dust aerosol optical depth retrieved daily from IASI and comparison with AERONET over the period 2007–2016 , 2018 .

[40]  O. Boucher,et al.  A satellite view of aerosols in the climate system , 2002, Nature.

[41]  Jin Huang,et al.  Enhanced Deep Blue aerosol retrieval algorithm: The second generation , 2013 .

[42]  David M. Broday,et al.  Evaluation of MODIS Collection 6 aerosol retrieval algorithms over Indo-Gangetic Plain: Implications of aerosols types and mass loading , 2017 .

[43]  Muhammad Bilal,et al.  Validation of Aqua-MODIS C051 and C006 Operational Aerosol Products Using AERONET Measurements Over Pakistan , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[44]  Yong Xue,et al.  Retrieval of aerosol optical depth over land surfaces from AVHRR data , 2013 .

[45]  Gautam Bisht,et al.  Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear sky days , 2005 .

[46]  W. Paul Menzel,et al.  Remote sensing of cloud, aerosol, and water vapor properties from the moderate resolution imaging spectrometer (MODIS) , 1992, IEEE Trans. Geosci. Remote. Sens..

[47]  Ashish Kumar,et al.  Evaluation and utilization of MODIS and CALIPSO aerosol retrievals over a complex terrain in Himalaya , 2018 .

[48]  Muhammad Bilal,et al.  New customized methods for improvement of the MODIS C6 Dark Target and Deep Blue merged aerosol product , 2017 .

[49]  Joseph Frostad,et al.  Ambient Air Pollution Exposure Estimation for the Global Burden of Disease 2013. , 2016, Environmental science & technology.

[50]  Zhanqing Li,et al.  Assessment and comparison of three years of Terra and Aqua MODIS Aerosol Optical Depth Retrieval (C005) in Chinese terrestrial regions , 2010 .