Articles | Volume 17, issue 14
https://doi.org/10.5194/amt-17-4411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-17-4411-2024
© Author(s) 2024. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-angle aerosol optical depth retrieval method based on improved surface reflectance
Lijuan Chen
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Ren Wang
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
Ying Fei
Key Laboratory of Virtual Geographic Environment of Ministry of Education, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
Peng Fang
Key Laboratory of Virtual Geographic Environment of Ministry of Education, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
Yong Zha
Key Laboratory of Virtual Geographic Environment of Ministry of Education, Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, College of Geographic Science, Nanjing Normal University, Nanjing 210023, China
Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Joint International Research Laboratory of Climate and Environment Change (ILCEC)/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD), Nanjing University of Information Science and Technology, Nanjing 210044, China
School of Atmospheric Science, Nanjing University of Information Science and Technology, Nanjing 210044, China
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Cited articles
Abdou, W. A., Diner, D. J., Martonchik, J. V., Bruegge, C. J., Kahn, R. A., Gaitley, B. J., Crean, K. A., Remer, L. A., and Holben, B.: 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. Geophys. Res., 110, D10S07, https://doi.org/10.1029/2004JD004693, 2005.
AERONET (Aerosol Robotic Network): https://aeronet.gsfc.nasa.gov/new_web/index.html, last access: 10 August 2023.
Berhane, S. A. and Bu, L.: Aerosol-Cloud Interaction with Summer Precipitation over Major Cities in Eritrea, Remote Sens.-Basel, 13, 21, https://doi.org/10.3390/rs13040677, 2021.
Chen, L., Wang, R., and Han, J.: Influence of observation angle change on satellite retrieval of aerosol optical depth, Tellus B, 73, 1–14, https://doi.org/10.1080/16000889.2021.1940758, 2021a.
Chen, L., Fei, Y., and Wang, R.: Retrieval of high temporal resolution aerosol optical depth using the GOCI remote sensing data, Remote Sens.-Basel, 13, 2376, https://doi.org/10.3390/rs13122376, 2021b.
Chen, L., Wang, R., and Wei, G.: A surface reflectance correction model to improve the retrieval of MISR aerosol optical depth supported by MODIS data, Adv. Space. Res., 67, 858–867, https://doi.org/10.1016/j.asr.2020.10.033, 2021c.
Daniel, R., Steven, S., Robert, W., and Leo, D.: Climate Effects of Aerosol-Cloud Interactions, Science, 343, 379–380, https://doi.org/10.1126/science.1247490, 2014.
Dao, Y. and Gong, W.: Observed holiday aerosol reduction and temperature cooling over East Asia, J. Geophys. Res.-Atmos., 11, 6306–6324, https://doi.org/10.1002/2014JD021464, 2014.
Deuzé, J. L., Bréon, F. M., Devaux, C., Goloub, P., Herman, M., Lafrance, B., Maignan, F., Marchand, A., Nadal, F., Perry, G., and Tanré, D.: Remote sensing of aerosols over land surfaces from POLDER-ADEOS-1 polarized measurements, J. Geophys. Res.-Atmos., 106, 4913–4926, https://doi.org/10.1029/2000JD900364, 2001.
Dong, W., Tao, M., Xu, X., Wang, J., Wang, Y., Wang, L., Song, Y., Fan, M., and Chen, L.: Satellite Aerosol Retrieval From Multiangle Polarimetric Measurements: Information Content and Uncertainty Analysis, IEEE T. Geosci. Remote, 61, 1–13, https://doi.org/10.1109/TGRS.2023.3264554, 2023,.
Dubovik, O., Li, Z., and Mishchenko, M. I.: Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives, Pergamon, 224, 474–511, https://doi.org/10.1016/j.jqsrt.2018.11.024, 2019.
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.: 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, Atmos. Meas. Tech., 12, 169–209, https://doi.org/10.5194/amt-12-169-2019, 2019.
Gupta, P., Levy, R. C., Mattoo, S., Remer, L. A., and Munchak, L. A.: A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm, Atmos. Meas. Tech., 9, 3293–3308, https://doi.org/10.5194/amt-9-3293-2016, 2016.
Hatzianastassiou, N.: The direct effect of aerosols on the radiation budget and climate of the Earth-atmosphere system: its variability in space and time, EGU General Assembly Conference Abstracts EGU General Assembly Conference Abstracts, 11, EGU2009-10109, 2009.
He, J., Zha, Y., Zhang, J., and Gao, J.: Aerosol Indices Derived from MODIS Data for Indicating Aerosol-Induced Air Pollution, Remote Sens., 6, 1587–1604, https://doi.org/10.3390/rs6021587, 2014.
Holben, B. N., Tanré, D., and Smirnov, A.: An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res., 106, 12067–12097, https://doi.org/10.1029/2001JD900014, 2001.
Hsu, N. C., Tsay, S. C., and King, M. D.: Aerosol properties over bright-reflecting source regions, IEEE T. Geosci. Remote, 42, 557–569, https://doi.org/10.1109/TGRS.2004.824067, 2004.
Huang, X. and Ding, A.: Aerosol as a critical factor causing forecast biases of air temperature in global numerical weather prediction models, Sci. Bull., 18, 1917–1924, https://doi.org/10.1016/j.scib.2021.05.009, 2021.
Kaufman, Y. J., Wald, A. E., Remer, L. A., Gao, B. C., Li, R., and Flynn, L.: The MODIS 2.1-um channel-correlation with visible reflectance for use in remote sensing of aerosol, IEEE T. Geosci. Remote, 35, 1286–1298, https://doi.org/10.1109/36.628795, 1997.
Kokhanovsky, A. A., Curier, R. L., Leeuw, G. D., and Grey, W. M. F.: The intercomparison of AATSR dual-view aerosol optical thickness retrievals with results from various algorithms and instruments, Int. J. Remote Sens., 30, 4525–4537, https://doi.org/10.1080/01431160802578012, 2009.
Lee, S. S., Donner, L. J., and Penner, J. E.: Thunderstorm and stratocumulus: how does their contrasting morphology affect their interactions with aerosols?, Atmos. Chem. Phys., 10, 6819–6837, https://doi.org/10.5194/acp-10-6819-2010, 2010.
Li, E., Zhang, Z., and Tan, Y.: A Novel Cloud Detection Algorithm Based on Simplified Radiative Transfer Model for Aerosol Retrievals: Preliminary Result on Himawari-8 Over Eastern China, IEEE T. Geosci. Remote, 59, 1–12, https://doi.org/10.1109/TGRS.2020.3004719, 2020.
Li, Y., Xue, Y., and Guang, J.: Ground-Level PM2.5 Concentration Estimation from Satellite Data in the Beijing Area Using a Specific Particle Swarm Extinction Mass Conversion Algorithm, Remote Sens.-Basel, 10, 1906, https://doi.org/10.3390/rs10121906, 2018.
Lu, S., Xue, Y., Yang, X. H., Leys, J., Guang, J., Che, Y. H., Fan, C., Xie, Y. Q., and Li, Y.: Joint Retrieval of Aerosol Optical Depth and Surface Reflflectance Over Land Using Geostationary Satellite Data, IEEE T. Geosci. Remote, 57, 1489–1501, https://doi.org/10.1109/TGRS.2018.2867000, 2019.
Martonchik, J. V.: Determination of aerosol optical depth and land surface directional reflectances using multiangle imagery, J. Geophys. Res., 102, 17015–17022, https://doi.org/10.1029/96JD02444, 1997.
Morozova, A. L. and Mironova, I. A.: Aerosols over continental Portugal (1978–1993): their sources and an impact on the regional climate, Atmos. Chem. Phys., 15, 6407–6418, https://doi.org/10.5194/acp-15-6407-2015, 2015.
NASA: MI1B2T, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/Terra/MISR/MI1B2T_L1.003, 2007.
NASA: MI1B2GEOP, NASA Langley Atmospheric Science Data Center DAAC [data set], https://doi.org/10.5067/Terra/MISR/MIB2GEOP_L1.002, 2008.
NASA Goddard Space Flight Center: AERONET data, available at: https://aeronet.gsfc.nasa.gov/new_web/aerosols.html, last access: 20 October 2023.
NASA LAADS DAAC: MODIS L1B data and MODIS BRDF data, available at: https://ladsweb.modaps.eosdis.nasa.gov/search/, last access: 10 August 2023.
Remer, L. A., Tanré, D., and Kaufman, Y. J.: Algorithm for remote sensing of tropospheric aerosol from MODIS: Collection 005, NASA's Earth Observing System, https://eospso.gsfc.nasa.gov/atbd/algorithm-remote-sensing-tropospheric-aerosol-modis (last access: 2 September 2023), 2009.
Samset, B. H., Sand, M., and Smith, C. J.: Climate Impacts From a Removal of Anthropogenic Aerosol Emissions, Geophys. Res. Lett., 45, 1020–1029, https://doi.org/10.1002/2017GL076079, 2018.
Schaaf, C. B., Strahler, A. H., and Gao, F.: MODIS BRDF Albedo Product ATBD V 5.0, Eospso.nasa.gov, https://modis.gsfc.nasa.gov/data/atbd/ (last access: 10 May 2024), 1999.
Sun, E., Fu, C., and Yu, W.: Variation and Driving Factor of Aerosol Optical Depth over the South China Sea from 1980 to 2020, Atmosphere, 13, 372, https://doi.org/10.3390/atmos13030372, 2022.
Sundstrom, A. M., Kolmonen, P., Sogacheva, L., and Leeuw, G. D.: Aerosol retrieval over China with the AATSR dual view algorithm, Remote Sens. Environ., 116, 189–198, https://doi.org/10.1016/j.rse.2011.04.041, 2012.
Xie, Y., Xue, Y., and Jie, G.: Deriving a Global and Hourly Data Set of Aerosol Optical Depth Over Land Using Data From Four Geostationary Satellites: GOES-16, MSG-1, MSG-4, and Himawari-8, IEEE T. Geosci. Remote, 99, 1–12, https://doi.org/10.1109/TGRS.2019.2944949, 2019.
Zhang, Y., Li, Z., Liu, Z., Wang, Y., Qie, L., Xie, Y., Hou, W., and Leng, L.: Retrieval of aerosol fine-mode fraction over China from satellite multiangle polarized observations: validation and comparison, Atmos. Meas. Tech., 14, 1655–1672, https://doi.org/10.5194/amt-14-1655-2021, 2021.
Short summary
This study explores the problems of surface reflectance estimation from previous MISR satellite remote sensing images and develops an error correction model to obtain a higher-precision aerosol optical depth (AOD) product. High-accuracy AOD is important not only for the daily monitoring of air pollution but also for the study of energy exchange between land and atmosphere. This will help further improve the retrieval accuracy of multi-angle AOD on large spatial scales and for long time series.
This study explores the problems of surface reflectance estimation from previous MISR satellite...