B02: Remote sensing of aerosol properties and surface reflectance in the Arctic from satellite observations
The conditions in the Arctic have been changing significantly during the last decades when Arctic amplification. The changes from (i) the increase of temperature, and (ii) the changes in pollution emitted locally (e.g., human settlements, industry, increase in shipping), or transported to the Arctic from Europe, Asia or North America (e.g., urban agglomerations, industry and ires). The scientific objectives of B02 address the need to quantify the change in Aerosol Optical Thickness (AOT) and the surface spectral reflectance (SSR) during the evolving period of Arctic amplification. This will enable to answer questions such as: what has been, is and will be the relative importance of local sources and transported aerosol in the Arctic? The Arctic region is large and the number of ground-based measurements is small and sparsely distributed. Consequently, only satellite measurements can provide the unique and required long-term local regional coverage across the Arctic at high temporal sampling. Active remote sensing yields measurements during both night and day, but has much lower intrinsic coverage than daytime passive remote sensing. Consequently, in B02 we use long-term passive remote sensing observations from different satellite borne instrumentation made during the past four decades over the Arctic. Recognizing the different AOT retrieval challenges for high and low SSR for cloud free conditions, we developed in phase I inversion algorithms optimised for both conditions. In addition, the first analysis using AOT from the climate data record, obtained from the measurements of the Advanced Very-High-Resolution Radiometer (AVHRR) over open waters in the Arctic has been analyzed. This shows statistically significant AOT changes, e.g. in the Atlantic corridor, close to the Bering Strait and elsewhere.
Building on the retrieval algorithms developed and first geophysical analyses, in phase II the AOT and SSR record will be retrieved during polar day from different passive remote sensing observations. These will be validated by comparison with observations from ground-based and other satellite borne instruments. A consolidated data set will result. Having established the data quality, geophysical analyses of the different long term dat sets will be undertaken to establish the evolution of changes in AOT and SSR, their origins and consequences.
Changes in top of the atmosphere reflectance, measured by satellite instruments, yield the changes in aerosol and surface spectral reflectance in the Arctic.
In phase II we will answer the following questions related to the hypothesis:
- What are the changes in the Aerosol Optical Thickness (AOT) and surface spectral reflectance (SSR) observed from space over the past decades?
- Are these changes attributable to natural or anthropogenic origins, i.e. are predicted changes in agreement with the identified changes?
Achievements phase I
B02 exploits satellite data for detection of changes in Arctic aersosol. This is quite challenging and different approaches are needed from different surface types those spectral surface reflectance (SSR) is also of interest. Over the Arctic open waters a first long-term record of Aerosol Optical Thickness (AOT) covering a period of more than 35 years shows a significant increase of AOT over the Fram Strait during haze season, and over the Chuchki Sea during September. The record also indicate a significant increase of AOT over the northeast passage during July and September. Improved retrievals for dark to moderately bright surfaces, such as snow/ice-free land and ocean (Jafariserajehlou et al., 2019) were developed. Progress has also been made in the field of AOT/SSR retrievals over bright surfaces (snow/ice covered areas) with a novel retrieval, which benefits from improved knowledge of aerosol typing and SSR treatment.
Role within (AC)³
Dr. Marco Vountas
University of Bremen
Institute of Environmental Physics (IUP)
Prof. Dr. John P. Burrows
University of Bremen
Institute of Environmental Physics
Dr. Soheila Jafariserajehlou
PhD (in phase I)
University of Bremen
Institute of Environmental Physics (IUP)
Dr. Luca Lelli
Postdoc (in phase I)
University of Bremen
Institute for Environmental Physics (IUP)
Mei, L., Rozanov, V., Rozanov, A., and Burrows, J.: SCIATRAN software package (V4.6), 2022: update and further development of aerosol, clouds, surface reflectance databases and models, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2022-153, in review.
L. Mei, V. Rozanov, Z. Jiao, J. P. Burrows, 2022, A new snow bidirectional reflectance distribution function model in spectral regions from UV to SWIR: Model development and application to ground-based, aircraft and satellite observations, ISPRS J. Photogramm. Remote Sens., 188, 269-285, https://doi.org/10.1016/j.isprsjprs.2022.04.010
Jafariserajehlou, S., 2021: Aerosol, Surface and Cloud retrieval using passive remote sensing over the Arctic, Dissertation, Universität Bremen, http://dx.doi.org/10.26092/elib/1170
Mei, L., Rozanov, V., Jäkel, E., Cheng, X., Vountas, M., and Burrows, J. P., 2021: The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation, Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021.
Mei, L., Rozanov, V., Pohl, C., Vountas, M., and Burrows, J. P., 2021: The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study, Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021.
Jafariserajehlou, S., Rozanov, V. V., Vountas, M., Gatebe, C. K., and Burrows, J. P., 2021: Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic, Atmos. Meas. Tech., 14, 369–389, https://doi.org/10.5194/amt-14-369-2021.
M. Vountas, K. Belinska, V. V. Rozanov, L. Lelli, L. Mei, S. Jafariserajehlou, J. P. Burrows, 2020: Retrieval of aerosol optical thickness and surface parameters based on multi-spectral and multi-viewing space-borne measurements, J. Quant. Spectro. Rad. Trans., Volume 256, 107311, ISSN 0022-4073, https://doi.org/10.1016/j.jqsrt.2020.107311.
Mei, L., Rozanov, V., Burrows, J. P., 2020: A fast and accurate radiative transfer model for aerosol remote sensing, J. Quant. Spectrosc. Radiat. Transfer, 256, 107270, https://doi.org/10.1016/j.jqsrt.2020.107270
L. Mei, V. Rozanov, Ch. Ritter, B. Heinold, Z. Jiao, M. Vountas, J. P.Burrows, 2020, Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model, IEEE Transactions on Geoscience and Remote Sensing, https://doi.org/10.1109/TGRS.2020.2972339
L. Mei, S. Vandenbussche, V. Rozanov, E. Proestakis, V. Amiridis, S. Callewaert, M. Vountas, J. P.Burrows, 2020, On the retrieval of aerosol optical depth over cryosphere using passive remote sensing, Remote Sensing of Enviroment, 241, 111731, https://doi.org/10.1016/j.rse.2020.111731.
Ding, A., Z. Jiao, Y. Dong, X. Zhang, J.I. Peltoniemi, L. Mei, J. Guo, S. Yin, L. Cui, Y. Chang, and R. Xie, 2019: Evaluation of the Snow Albedo Retrieved from the Snow Kernel Improved Ross-Roujean BRDF Model, Remote Sensing, 11, 1611, doi:10.3390/rs11131611
Mei, L., V.V. Rozanov, H. Jethva, K.G. Meyer, L. Lelli, M. Vountas, and J.P. Burrows, 2019: Extending XBAER algorithm to aerosol and cloud condition, accepted for publication in IEEE Transactions on Geoscience and Remote Sensing, doi:10.1109/TGRS.2019.2919910
Wendisch, M., A. Macke, A. Ehrlich, C. Lüpkes, M. Mech, D. Chechin, K. Dethloff, C. Barrientos, H. Bozem, M. Brückner, H.-C. Clemen, S. Crewell, T. Donth, R. Dupuy, C. Dusny, K. Ebell, U. Egerer, R. Engelmann, C. Engler, O. Eppers, M. Gehrmann, X. Gong, M. Gottschalk, C. Gourbeyre, H. Griesche, J. Hartmann, M. Hartmann, B. Heinold, A. Herber, H. Herrmann, G. Heygster, P. Hoor, S. Jafariserajehlou, E. Jäkel, E. Järvinen, O. Jourdan, U. Kästner, S. Kecorius, E.M. Knudsen, F. Köllner, J. Kretzschmar, L. Lelli, D. Leroy, M. Maturilli, L. Mei, S. Mertes, G. Mioche, R. Neuber, M. Nicolaus, T. Nomokonova, J. Notholt, M. Palm, M. van Pinxteren, J. Quaas, P. Richter, E. Ruiz-Donoso, M. Schäfer, K. Schmieder, M. Schnaiter, J. Schneider, A. Schwarzenböck, P. Seifert, M.D. Shupe, H. Siebert, G. Spreen, J. Stapf, F. Stratmann, T. Vogl, A. Welti, H. Wex, A. Wiedensohler, M. Zanatta, S. Zeppenfeld, 2019: The Arctic Cloud Puzzle: Using ACLOUD/PASCAL Multi-Platform Observations to Unravel the Role of Clouds and Aerosol Particles in Arctic Amplification, Bull. Amer. Meteor. Soc., 100 (5), 841–871, doi:10.1175/BAMS-D-18-0072.1
Mei, L.L., V. Rozanov, R. Christoph, H. Bernd, Z.T. Jiao, M. Vountas, and J.P. Burrows, 2019: Retrieval of Aerosol Optical Thickness in the Arctic Snow-Covered Regions Using Passive Remote Sensing: Impact of Aerosol Typing and Surface Reflection Model, submitted to IEEE Transactions on Geoscience and Remote Sensing (under review)
Mei, L., J. Strandgren, V. Rozanov, M. Vountas, J. P. Burrows, and Y. J. Wang, 2019: Study of satellite retrieved aerosol optical depth spatial resolution effect on particulate matter concentration prediction, Int. J. Remote Sens., 40 (18), 7084-7112, doi:10.1080/01431161.2019.1601279
Jafariserajehlou, S., L. Mei, M. Vountas, V. Rozanov, J.P. Burrows, and R. Hollmann, 2019: A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019
Jiao, Z., A. Ding, A. Kokhanovsky, C. Schaaf, F. Bréon, Y. Dong, Z. Wang, Y. Liu, X. Zhang, S. Yin, L. Cui, L. Mei, Y. Chang, 2019: Development of a Snow Kernel to Better Model the Anisotropic Reflectance of Pure Snow into a Kernel-Driven BRDF Model Framework, Remote Sensing Environment, 221, 198-209, doi:10.1016/j.rse.2018.11.001
Che, Y., L. Mei, Y. Xue, J. Guang, L. She, and Y. Li, Y., 2018: Validation of Aerosol Products from AATSR and MERIS/AATSR Synergy Algorithms – Part 1: Global Evaluation. Remote Sens., 10, 1414, doi:10.3390/rs10091414
Mei, L., V. Rozanov, M. Vountas, J.P. Burrows, and A. Richter, 2018: XBAER-derived aerosol optical thickness from OLCI/Sentinel-3 observation, Atmospheric Chemistry and Physics, 18 (4), 2511–2523, doi:10.5194/acp-18-2511-2018
Lelli, L. and Vountas, M., 2018: Chapter 5 – Aerosol and Cloud Bottom Altitude Covariations From Multisensor Spaceborne Measurements, In Remote Sensing of Aerosols, Clouds, and Precipitation, edited by Tanvir Islam, Yongxiang Hu, Alexander Kokhanovsky and Jun Wang, Elsevier, pp 109-127, ISBN 9780128104378, https://doi.org/10.1016/B978-0-12-810437-8.00005-0
Lelli, L., V. V. Rozanov, M. Vountas, J. P. Burrows, 2017: Polarized radiative transfer through terrestrial atmosphere accounting for rotational Raman scattering, J. Quant. Spect. Rad. Trans., 200, 70-89, doi:10.1016/j.jqsrt.2017.05.027
Mei, L., V. Rozanov, M. Vountas, J. P. Burrows, R. C. Levy, W. Lotz, 2017: Retrieval of aerosol optical properties using MERIS observations: Algorithm and some first results, Rem. Sens. Environ., 197, 125-140, doi:10.1016/j.rse.2016.11.015
She, L., Mei, L., Xue, Y. Che, Y., and Guang, J., 2017: SAHARA: A Simplified AtmospHeric Correction AlgoRithm for Chinese gAofen Data: 1. Aerosol Algorithm, Remote Sens., 9, 253, doi:10.3390/rs9030253
Wendisch, M., M. Brückner, J. P. Burrows, S. Crewell, K. Dethloff, K. Ebell, Ch. Lüpkes, A. Macke, J. Notholt, J. Quaas, A. Rinke, and I. Tegen, 2017: Understanding causes and effects of rapid warming in the Arctic. Eos, 98, doi:10.1029/2017EO064803