Articles | Volume 19, issue 7
https://doi.org/10.5194/amt-19-2555-2026
© Author(s) 2026. 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-19-2555-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Retrieval of global aerosol and surface properties from the Gaofen-5 Directional Polarimetric Camera measurements
Zhenyu Zhang
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Yueming Dong
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Chongzhao Zhang
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
Qiurui Li
Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University, 100871, Beijing, China
National Satellite Meteorological Center, China Meteorological Administration, 100081, Beijing, China
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Cited articles
Albrecht, B. A.: Aerosols, Cloud Microphysics, and Fractional Cloudiness, Science, 245, 1227–1230, https://doi.org/10.1126/science.245.4923.1227, 1989. a
Chen, C., Dubovik, O., Fuertes, D., Litvinov, P., Lapyonok, T., Lopatin, A., Ducos, F., Derimian, Y., Herman, M., Tanré, D., Remer, L. A., Lyapustin, A., Sayer, A. M., Levy, R. C., Hsu, N. C., Descloitres, J., Li, L., Torres, B., Karol, Y., Herrera, M., Herreras, M., Aspetsberger, M., Wanzenboeck, M., Bindreiter, L., Marth, D., Hangler, A., and Federspiel, C.: Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring, Earth Syst. Sci. Data, 12, 3573–3620, https://doi.org/10.5194/essd-12-3573-2020, 2020. a, b, c, d
Cummings, J. A. and Smedstad, O. M.: Variational Data Assimilation for the Global Ocean, Springer Berlin Heidelberg, 303–343, https://doi.org/10.1007/978-3-642-35088-7_13, ISBN 9783642350887, 2013. a
Dai, G., Wu, S., Long, W., Liu, J., Xie, Y., Sun, K., Meng, F., Song, X., Huang, Z., and Chen, W.: Aerosol and cloud data processing and optical property retrieval algorithms for the spaceborne ACDL/DQ-1, Atmos. Meas. Tech., 17, 1879–1890, https://doi.org/10.5194/amt-17-1879-2024, 2024. a
Diner, D. J., Beckert, J. C., Reilly, T. H., Bruegge, C. J., Conel, J. E., Kahn, R. A., Martonchik, J. V., Ackerman, T. P., Davies, R., and Gerstl, S. A.: Multi-angle Imaging SpectroRadiometer (MISR) instrument description and experiment overview, IEEE T. Geosci. Remote, 36, 1072–1087, 1998. a
Dong, Y., Li, J., Zhang, Z., Zheng, Y., Zhang, C., and Li, Z.: Machine Learning-Based Retrieval of Aerosol and Surface Properties Over Land From the Gaofen-5 Directional Polarimetric Camera Measurements, IEEE T. Geosci. Remote, 62, 1–15, https://doi.org/10.1109/tgrs.2024.3419169, 2024. a, b, c, d, e, f, g
Dubovik, O. and King, M. D.: A flexible inversion algorithm for retrieval of aerosol optical properties from Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 20673–20696, https://doi.org/10.1029/2000jd900282, 2000. a
Dubovik, O., Smirnov, A., Holben, B. N., King, M. D., Kaufman, Y. J., Eck, T. F., and Slutsker, I.: Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements, J. Geophys. Res.-Atmos., 105, 9791–9806, https://doi.org/10.1029/2000jd900040, 2000. a
Dubovik, O., Herman, M., Holdak, A., Lapyonok, T., Tanré, D., Deuzé, J. L., Ducos, F., Sinyuk, A., and Lopatin, A.: Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations, Atmos. Meas. Tech., 4, 975–1018, https://doi.org/10.5194/amt-4-975-2011, 2011. a, b
Dubovik, O., Li, Z., Mishchenko, M. I., Tanré, D., Karol, Y., Bojkov, B., Cairns, B., Diner, D. J., Espinosa, W. R., Goloub, P., Gu, X., Hasekamp, O., Hong, J., Hou, W., Knobelspiesse, K. D., Landgraf, J., Li, L., Litvinov, P., Liu, Y., Lopatin, A., Marbach, T., Maring, H., Martins, V., Meijer, Y., Milinevsky, G., Mukai, S., Parol, F., Qiao, Y., Remer, L., Rietjens, J., Sano, I., Stammes, P., Stamnes, S., Sun, X., Tabary, P., Travis, L. D., Waquet, F., Xu, F., Yan, C., and Yin, D.: Polarimetric remote sensing of atmospheric aerosols: Instruments, methodologies, results, and perspectives, J. Quant. Spectrosc. Ra., 224, 474–511, https://doi.org/10.1016/j.jqsrt.2018.11.024, 2019. a, b, c
Eck, T. F., Holben, B. N., Reid, J. S., Sinyuk, A., Giles, D. M., Arola, A., Slutsker, I., Schafer, J. S., Sorokin, M. G., Smirnov, A., LaRosa, A. D., Kraft, J., Reid, E. A., O’Neill, N. T., Welton, E., and Menendez, A. R.: The extreme forest fires in California/Oregon in 2020: Aerosol optical and physical properties and comparisons of aged versus fresh smoke, Atmos. Environ., 305, 119798, https://doi.org/10.1016/j.atmosenv.2023.119798, 2023. a
Engelstaedter, S., Tegen, I., and Washington, R.: North African dust emissions and transport, Earth-Sci. Rev., 79, 73–100, https://doi.org/10.1016/j.earscirev.2006.06.004, 2006. a
Fang, L., Hasekamp, O., Fu, G., Gong, W., Wang, S., Wang, W., Han, Q., and Tang, S.: Retrieval of Aerosol Optical Properties over Land Using an Optimized Retrieval Algorithm Based on the Directional Polarimetric Camera, Remote Sensing, 14, 4571, https://doi.org/10.3390/rs14184571, 2022. a, b, c
GCOS: The Global Observing System For Climate: Implementation Needs, GCOS-200 (GOOS-214), WMO, Geneva, 341 pp., https://library.wmo.int/records/item/55469 -the-global-observing-system-for-climate (last access: 13 April 2026), 2016. a
Ge, B., Li, Z., Chen, C., Hou, W., Xie, Y., Zhu, S., Qie, L., Zhang, Y., Li, K., and Xu, H.: An Improved Aerosol Optical Depth Retrieval Algorithm for Multiangle Directional Polarimetric Camera (DPC), Remote Sensing, 14, 4045, https://doi.org/10.3390/rs14164045, 2022. a, b
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. a
Ginoux, P., Prospero, J. M., Gill, T. E., Hsu, N. C., and Zhao, M.: Global‐scale attribution of anthropogenic and natural dust sources and their emission rates based on MODIS Deep Blue aerosol products, Rev. Geophys., 50, https://doi.org/10.1029/2012rg000388, 2012. a
Habib, A., Chen, B., Khalid, B., Tan, S., Che, H., Mahmood, T., Shi, G., and Butt, M. T.: Estimation and inter-comparison of dust aerosols based on MODIS, MISR and AERONET retrievals over Asian desert regions, J. Environ. Sci., 76, 154–166, https://doi.org/10.1016/j.jes.2018.04.019, 2019. a
Hasekamp, O. P., Litvinov, P., and Butz, A.: Aerosol properties over the ocean from PARASOL multiangle photopolarimetric measurements, J. Geophys. Res.-Atmos., 116, D14204, https://doi.org/10.1029/2010JD015469, 2011. a
Hasekamp, O., Litvinov, P., Fu, G., Chen, C., and Dubovik, O.: Algorithm evaluation for polarimetric remote sensing of atmospheric aerosols, Atmos. Meas. Tech., 17, 1497–1525, https://doi.org/10.5194/amt-17-1497-2024, 2024. a
Heald, C. L., Ridley, D. A., Kroll, J. H., Barrett, S. R. H., Cady-Pereira, K. E., Alvarado, M. J., and Holmes, C. D.: Contrasting the direct radiative effect and direct radiative forcing of aerosols, Atmos. Chem. Phys., 14, 5513–5527, https://doi.org/10.5194/acp-14-5513-2014, 2014. a
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz‐Sabater, J., Nicolas, J., Peubey, C., Radu, R., Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G., Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G., Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes, R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J., Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu, C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and Thépaut, J.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
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.: AERONET – A Federated Instrument Network and Data Archive for Aerosol Characterization, Remote Sens. Environ., 66, 1–16, https://doi.org/10.1016/S0034-4257(98)00031-5, 1998. a
Iglesias, V., Balch, J. K., and Travis, W. R.: U.S. fires became larger, more frequent, and more widespread in the 2000s, Science Advances, 8, https://doi.org/10.1126/sciadv.abc0020, 2022. a
Ji, Z., Li, Z., Zhang, Z., Fu, G., Hasekamp, O., Fan, C., Liu, Q., and de Leeuw, G.: Retrieval of Aerosol Properties Over the Ocean Using Data From the Second‐Generation Directional Polarization Camera (DPC‐2) Onboard the GF‐5(02) Satellite, J. Geophys. Res.-Atmos., 130, https://doi.org/10.1029/2025jd043908, 2025. a
Jin, S., Ma, Y., Chen, C., Dubovik, O., Hong, J., Liu, B., and Gong, W.: Performance evaluation for retrieving aerosol optical depth from the Directional Polarimetric Camera (DPC) based on the GRASP algorithm, Atmos. Meas. Tech., 15, 4323–4337, https://doi.org/10.5194/amt-15-4323-2022, 2022. a, b
Jin, S., Ma, Y., Wang, Z., Hong, J., Chen, F., Ti, R., Chen, C., Liu, Z., Zhai, S., and Gong, W.: Retrievals and performance assessment of global marine aerosol optical properties from DPC/GRASP, Journal of Atmospheric and Environmental Optics, 19, 680–697, 2024. a
Kleinman, L. I., Sedlacek III, A. J., Adachi, K., Buseck, P. R., Collier, S., Dubey, M. K., Hodshire, A. L., Lewis, E., Onasch, T. B., Pierce, J. R., Shilling, J., Springston, S. R., Wang, J., Zhang, Q., Zhou, S., and Yokelson, R. J.: Rapid evolution of aerosol particles and their optical properties downwind of wildfires in the western US, Atmos. Chem. Phys., 20, 13319–13341, https://doi.org/10.5194/acp-20-13319-2020, 2020. a
Levy, R. C., Remer, L. A., and Dubovik, O.: Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land, J. Geophys. Res.-Atmos., 112, D13210, https://doi.org/10.1029/2006jd007815, 2007a. a, b
Levy, R. C., Remer, L. A., Mattoo, S., Vermote, E. F., and Kaufman, Y. J.: Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance, J. Geophys. Res.-Atmos., 112, D13211, https://doi.org/10.1029/2006jd007811, 2007b. a
Levy, R. C., Remer, L. A., Kleidman, R. G., Mattoo, S., Ichoku, C., Kahn, R., and Eck, T. F.: Global evaluation of the Collection 5 MODIS dark-target aerosol products over land, Atmos. Chem. Phys., 10, 10399–10420, https://doi.org/10.5194/acp-10-10399-2010, 2010. a
Levy, R. C., Mattoo, S., Munchak, L. A., Remer, L. A., Sayer, A. M., Patadia, F., and Hsu, N. C.: The Collection 6 MODIS aerosol products over land and ocean, Atmos. Meas. Tech., 6, 2989–3034, https://doi.org/10.5194/amt-6-2989-2013, 2013. a, b
Li, J., Li, X., Carlson, B. E., Kahn, R. A., Lacis, A. A., Dubovik, O., and Nakajima, T.: Reducing multisensor satellite monthly mean aerosol optical depth uncertainty: 1. Objective assessment of current AERONET locations, J. Geophys. Res.-Atmos., 121, https://doi.org/10.1002/2016jd025469, 2016. a
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., Liao, H., Ramaswamy, V., Kahn, R. A., Zhang, P., Dubovik, O., Ding, A., Lacis, A. A., Zhang, L., and Dong, Y.: Scattering and absorbing aerosols in the climate system, Nature Reviews Earth & Environment, 3, 363–379, https://doi.org/10.1038/s43017-022-00296-7, 2022a. a
Li, L., Dubovik, O., Derimian, Y., Schuster, G. L., Lapyonok, T., Litvinov, P., Ducos, F., Fuertes, D., Chen, C., Li, Z., Lopatin, A., Torres, B., and Che, H.: Retrieval of aerosol components directly from satellite and ground-based measurements, Atmos. Chem. Phys., 19, 13409–13443, https://doi.org/10.5194/acp-19-13409-2019, 2019. a
Li, Z., Hou, W., Hong, J., Zheng, F., Luo, D., Wang, J., Gu, X., and Qiao, Y.: Directional Polarimetric Camera (DPC): Monitoring aerosol spectral optical properties over land from satellite observation, J. Quant. Spectrosc. Ra., 218, 21–37, https://doi.org/10.1016/j.jqsrt.2018.07.003, 2018. a, b, c, d, e
Li, Z., Hou, W., Hong, J., Fan, C., Wei, Y., Liu, Z., Lei, X., Qiao, Y., Hasekamp, O. P., Fu, G., Wang, J., Dubovik, O., Qie, L., Zhang, Y., Xu, H., Xie, Y., Song, M., Zou, P., Luo, D., Wang, Y., and Tu, B.: The polarization crossfire (PCF) sensor suite focusing on satellite remote sensing of fine particulate matter PM2.5 from space, J. Quant. Spectrosc. Ra., 286, 108217, https://doi.org/10.1016/j.jqsrt.2022.108217, 2022b. a
Litvinov, P., Hasekamp, O., and Cairns, B.: Models for surface reflection of radiance and polarized radiance: Comparison with airborne multi-angle photopolarimetric measurements and implications for modeling top-of-atmosphere measurements, Remote Sens. Environ., 115, 781–792, https://doi.org/10.1016/j.rse.2010.11.005, 2011. a, b, c
Liu, C., Gao, M., Hu, Q., Brasseur, G. P., and Carmichael, G. R.: Stereoscopic Monitoring: A Promising Strategy to Advance Diagnostic and Prediction of Air Pollution, B. Am. Meteorol. Soc., 102, E730–E737, https://doi.org/10.1175/bams-d-20-0217.1, 2021. a
Loeb, N. G. and Su, W.: Direct Aerosol Radiative Forcing Uncertainty Based on a Radiative Perturbation Analysis, J. Climate, 23, 5288–5293, https://doi.org/10.1175/2010jcli3543.1, 2010. a
Maignan, F., Bréon, F.-M., Fédèle, E., and Bouvier, M.: Polarized reflectances of natural surfaces: Spaceborne measurements and analytical modeling, Remote Sens. Environ., 113, 2642–2650, https://doi.org/10.1016/j.rse.2009.07.022, 2009. a
Mallet, M., Voldoire, A., Solmon, F., Nabat, P., Drugé, T., and Roehrig, R.: Impact of biomass burning aerosols (BBA) on the tropical African climate in an ocean–atmosphere–aerosol coupled climate model, Atmos. Chem. Phys., 24, 12509–12535, https://doi.org/10.5194/acp-24-12509-2024, 2024. a, b
Mishchenko, M., Cairns, B., Kopp, G., Maring, H., Fafaul, B., Knobelspiesse, K., and Chowdhary, J.: Accurate monitoring of terrestrial aerosols and total solar irradiance: The NASA Glory mission, in: 2010 IEEE International Geoscience and Remote Sensing Symposium, Honolulu, HI, USA, 25–30 July 2010, IEEE, 758–760, https://doi.org/10.1109/igarss.2010.5652996, 2010. a
Mishchenko, M. I. and Travis, L. D.: Light scattering by polydisperse, rotationally symmetric nonspherical particles: Linear polarization, J. Quant. Spectrosc. Ra., 51, 759–778, https://doi.org/10.1016/0022-4073(94)90130-9, 1994. a
Mishchenko, M. I., Cairns, B., Hansen, J. E., Travis, L. D., Burg, R., Kaufman, Y. J., Vanderlei Martins, J., and Shettle, E. P.: Monitoring of aerosol forcing of climate from space: analysis of measurement requirements, J. Quant. Spectrosc. Ra., 88, 149–161, https://doi.org/10.1016/j.jqsrt.2004.03.030, 2004. a
Mishchenko, M. I., Geogdzhayev, I. V., Rossow, W. B., Cairns, B., Carlson, B. E., Lacis, A. A., Liu, L., and Travis, L. D.: Long-Term Satellite Record Reveals Likely Recent Aerosol Trend, Science, 315, 1543–1543, https://doi.org/10.1126/science.1136709, 2007. a
O'Reilly, J. E. and Werdell, P. J.: Chlorophyll algorithms for ocean color sensors - OC4, OC5 & OC6, Remote Sens. Environ., 229, 32–47, https://doi.org/10.1016/j.rse.2019.04.021, 2019. a
Pan, X., Ichoku, C., Chin, M., Bian, H., Darmenov, A., Colarco, P., Ellison, L., Kucsera, T., da Silva, A., Wang, J., Oda, T., and Cui, G.: Six global biomass burning emission datasets: intercomparison and application in one global aerosol model, Atmos. Chem. Phys., 20, 969–994, https://doi.org/10.5194/acp-20-969-2020, 2020. a, b, c
Popp, T., De Leeuw, G., Bingen, C., Brühl, C., Capelle, V., Chedin, A., Clarisse, L., Dubovik, O., Grainger, R., Griesfeller, J., Heckel, A., Kinne, S., Klüser, L., Kosmale, M., Kolmonen, P., Lelli, L., Litvinov, P., Mei, L., North, P., Pinnock, S., Povey, A., Robert, C., Schulz, M., Sogacheva, L., Stebel, K., Stein Zweers, D., Thomas, G., Tilstra, L., Vandenbussche, S., Veefkind, P., Vountas, M., and Xue, Y.: Development, Production and Evaluation of Aerosol Climate Data Records from European Satellite Observations (Aerosol_cci), Remote Sensing, 8, 421, https://doi.org/10.3390/rs8050421, 2016. a
Qie, L., Li, Z., Zhu, S., Xu, H., Xie, Y., Qiao, R., Hong, J., and Tu, B.: In-flight radiometric and polarimetric calibration of the Directional Polarimetric Camera onboard the GaoFen-5 satellite over the ocean, Appl. Optics, 60, 7186, https://doi.org/10.1364/ao.422980, 2021. a, b, c, d
Ramanathan, V., Crutzen, P. J., Kiehl, J. T., and Rosenfeld, D.: Aerosols, Climate, and the Hydrological Cycle, Science, 294, 2119–2124, https://doi.org/10.1126/science.1064034, 2001. a, b
Satheesh, S. K. and Srinivasan, J.: Enhanced aerosol loading over Arabian Sea during the pre‐monsoon season: Natural or anthropogenic?, Geophys. Res. Lett., 29, https://doi.org/10.1029/2002gl015687, 2002. a
Sayer, A. M., Hsu, N. C., Bettenhausen, C., Lee, J., Redemann, J., Schmid, B., and Shinozuka, Y.: Extending “Deep Blue” aerosol retrieval coverage to cases of absorbing aerosols above clouds: Sensitivity analysis and first case studies, J. Geophys. Res.-Atmos., 121, 4830–4854, https://doi.org/10.1002/2015jd024729, 2016. a
Schaaf, C. B., Gao, F., Strahler, A. H., Lucht, W., Li, X., Tsang, T., Strugnell, N. C., Zhang, X., Jin, Y., Muller, J.-P., Lewis, P., Barnsley, M., Hobson, P., Disney, M., Roberts, G., Dunderdale, M., Doll, C., d'Entremont, R. P., Hu, B., Liang, S., Privette, J. L., and Roy, D.: First operational BRDF, albedo nadir reflectance products from MODIS, Remote Sens. Environ., 83, 135–148, https://doi.org/10.1016/s0034-4257(02)00091-3, 2002. a, b
Schutgens, N., Dubovik, O., Hasekamp, O., Torres, O., Jethva, H., Leonard, P. J. T., Litvinov, P., Redemann, J., Shinozuka, Y., de Leeuw, G., Kinne, S., Popp, T., Schulz, M., and Stier, P.: AEROCOM and AEROSAT AAOD and SSA study – Part 1: Evaluation and intercomparison of satellite measurements, Atmos. Chem. Phys., 21, 6895–6917, https://doi.org/10.5194/acp-21-6895-2021, 2021. a
Sinyuk, A., Dubovik, O., Holben, B., Eck, T. F., Breon, F.-M., Martonchik, J., Kahn, R., Diner, D. J., Vermote, E. F., Roger, J.-C., Lapyonok, T., and Slutsker, I.: Simultaneous retrieval of aerosol and surface properties from a combination of AERONET and satellite data, Remote Sens. Environ., 107, 90–108, https://doi.org/10.1016/j.rse.2006.07.022, 2007. a
Sinyuk, A., Holben, B. N., Eck, T. F., Giles, D. M., Slutsker, I., Korkin, S., Schafer, J. S., Smirnov, A., Sorokin, M., and Lyapustin, A.: The AERONET Version 3 aerosol retrieval algorithm, associated uncertainties and comparisons to Version 2, Atmos. Meas. Tech., 13, 3375–3411, https://doi.org/10.5194/amt-13-3375-2020, 2020. a, b
Spurr, R. J.: VLIDORT: A linearized pseudo-spherical vector discrete ordinate radiative transfer code for forward model and retrieval studies in multilayer multiple scattering media, J. Quant. Spectrosc. Ra., 102, 316–342, https://doi.org/10.1016/j.jqsrt.2006.05.005, 2006. a, b
Su, X., Wang, L., Zhang, M., Qin, W., and Bilal, M.: A High-Precision Aerosol Retrieval Algorithm (HiPARA) for Advanced Himawari Imager (AHI) data: Development and verification, Remote Sens. Environ., 253, 112221, https://doi.org/10.1016/j.rse.2020.112221, 2021. a, b
Torres, O., Tanskanen, A., Veihelmann, B., Ahn, C., Braak, R., Bhartia, P. K., Veefkind, P., and Levelt, P.: Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview, J. Geophys. Res.-Atmos., 112, https://doi.org/10.1029/2007jd008809, 2007. a
Wang, X., Cai, D., Chen, S., Lou, J., Liu, F., Jiao, L., Cheng, H., Zhang, C., Hua, T., and Che, H.: Spatio-temporal trends of dust emissions triggered by desertification in China, CATENA, 200, 105160, https://doi.org/10.1016/j.catena.2021.105160, 2021. a, b
Wei, X., Cui, Q., Ma, L., Zhang, F., Li, W., and Liu, P.: Global aerosol-type classification using a new hybrid algorithm and Aerosol Robotic Network data, Atmos. Chem. Phys., 24, 5025–5045, https://doi.org/10.5194/acp-24-5025-2024, 2024. a
Yang, B., Zhao, H., and Chen, W.: Semi-empirical models for polarized reflectance of land surfaces: Intercomparison using space-borne POLDER measurements, J. Quant. Spectrosc. Ra., 202, 13–20, https://doi.org/10.1016/j.jqsrt.2017.07.014, 2017. a
Yang, X., Zhao, C., Yang, Y., and Fan, H.: Long-term multi-source data analysis about the characteristics of aerosol optical properties and types over Australia, Atmos. Chem. Phys., 21, 3803–3825, https://doi.org/10.5194/acp-21-3803-2021, 2021. a
Zhang, L., Li, J., Jiang, Z., Dong, Y., Ying, T., and Zhang, Z.: Clear-Sky Direct Aerosol Radiative Forcing Uncertainty Associated with Aerosol Optical Properties Based on CMIP6 models, J. Climate, 35, 3007–3019, https://doi.org/10.1175/jcli-d-21-0479.1, 2022. a
Zhang, Z.: DPC/GaoFen-5 Global Aerosol and Surface Properties Dataset, V1, Peking University Open Research Data Platform [data set], https://doi.org/10.18170/DVN/YMRDFC, 2026. a
Zhang, Z., Li, J., Dong, Y., Zhang, C., Ying, T., and Li, Q.: Long-Term Trends in Aerosol Single Scattering Albedo Cause Bias in MODIS Aerosol Optical Depth Trends, IEEE T. Geosci. Remote, 62, 1–9, https://doi.org/10.1109/tgrs.2024.3424981, 2024. a
Zhang, Z., Li, J., Che, H., Dong, Y., Dubovik, O., Eck, T., Gupta, P., Holben, B., Kim, J., Lind, E., Saud, T., Tripathi, S. N., and Ying, T.: Long-term trends in aerosol properties derived from AERONET measurements, Atmos. Chem. Phys., 25, 4617–4637, https://doi.org/10.5194/acp-25-4617-2025, 2025a. a
Zhang, Z., Li, Z., Fu, G., Hasekamp, O., Fan, C., Qie, L., Xie, Y., Li, L., Ji, Z., and Liu, Q.: Global Aerosol Retrieval Over Land Using the Chinese Satellite Polarimeter DPC-2/GF-5(02), IEEE T. Geosci. Remote, 63, 1–14, https://doi.org/10.1109/tgrs.2025.3633391, 2025b. a
Zheng, F., Hou, W., and Li, Z.: Optimal estimation retrieval for directional polarimetric camera onboard Chinese Gaofen-5 satellite: an analysis on multi-angle dependence and a posteriori error, Acta Phys. Sin., 68, 040701, https://doi.org/10.7498/aps.68.20181682, 2019. a, b
Zhu, S., Li, Z., Qie, L., Xu, H., Ge, B., Xie, Y., Qiao, R., Xie, Y., Hong, J., Meng, B., Tu, B., and Chen, F.: In-Flight Relative Radiometric Calibration of a Wide Field of View Directional Polarimetric Camera Based on the Rayleigh Scattering over Ocean, Remote Sensing, 14, 1211, https://doi.org/10.3390/rs14051211, 2022. a, b, c, d, e, f
Short summary
This study developed a numerical algorithm to simultaneously retrieve aerosol and surface parameters from multi-angle polarimetric observations from space. Our research shows that accurate single scattering albedo retrieval requires degree of linear polarization observation uncertainties below 0.01. The retrieval results agrees with ground-based measurements well, and successfully characterize regional pollution events. We also generate global maps of retrieved aerosol and surface parameters.
This study developed a numerical algorithm to simultaneously retrieve aerosol and surface...