Remote sensing of aerosols over land surfaces from POLDER‐ADEOS‐1 polarized measurements

Deuzé, J. L.; Bréon, François‐Marie; Devaux, C.; Goloub, Philippe; Herman, M.; Lafrance, Bruno; Maignan, Fabienne; Marchand, A.; Nadal, F.; Perry, Guillaume; Tanré, D.

doi:10.1029/2000jd900364

Cited by 450 publications

(312 citation statements)

References 43 publications

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“…To increase the signal to noise ratio, the algorithm is applied to 3×3 POLDER pixels, leading to a resolution in the aerosol AOT of 21 km×18 km. AOT retrieval from the POLDER polarized measurements is described by Deuzé et al (2001). The AOT is retrieved at the 670 nm and 865 nm channels equipped with polarized filters.…”

Section: Evaluation Of the Polder-2 Aerosol Optical Thicknessmentioning

confidence: 99%

Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data

et al. 2006

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Abstract. We analyze the relationship between daily fine particle mass concentration (PM2.5) and columnar aerosol optical thickness derived from the Polarization and Directionality of Earth's Reflectances (POLDER) satellite sensor. The study is focused over France during the POLDER-2 lifetime between April and October 2003. We have first compared the POLDER derived aerosol optical thickness (AOT) with integrated volume size distribution derived from ground-based Sun Photometer observations. The good correlation (R=0.72) with sub-micron volume fraction indicates that POLDER derived AOT is sensitive to the fine aerosol mass concentration. Considering 1974 match-up data points over 28 fine particle monitoring sites, the POLDER-2 derived AOT is fairly well correlated with collocated PM2.5 measurements, with a correlation coefficient of 0.55. The correlation coefficient reaches a maximum of 0.80 for particular sites. We have analyzed the probability to find an appropriate air quality category (AQC) as defined by U.S. Environmental Protection Agency (EPA) from POLDER-2 AOT measurements. The probability can be up to 88.8% (±3.7%) for the "Good" AQC and 89.1% (±3.6%) for the "Moderate" AQC.

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Section: Evaluation Of the Polder-2 Aerosol Optical Thicknessmentioning

confidence: 99%

Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data

et al. 2006

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“…The key advantage of the polarization approach is its ability to systematically correct for the surface contribution (Deuze, et al, 2001;Deuze, et al, 1993;Herman, et al, 1997). The main contribution of land surfaces to the TOA polarised portion of total reflected radiance at short wavelengths is generally smaller and less variable compared to that of the atmosphere and can be more easily modelled and removed in principle.…”

Section: Introductionmentioning

confidence: 99%

“…The main contribution of land surfaces to the TOA polarised portion of total reflected radiance at short wavelengths is generally smaller and less variable compared to that of the atmosphere and can be more easily modelled and removed in principle. However, this method is only suitable for large scattering (large aerosol loads), small aerosol particles (Deuze, et al, 2001) or small phase angles (Rondeaux and Herman, 1991;Breon et al, 1995).…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

et al. 2012

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Abstract.A novel approach for the joint retrieval of aerosol optical depth (AOD) and aerosol type, using Meteosat Second Generation -Spinning Enhanced Visible and Infrared Imagers (MSG/SEVIRI) observations in two solar channels, is presented. The retrieval is based on a Time Series (TS) technique, which makes use of the two visible bands at 0.6 µm and 0.8 µm in three orderly scan times (15 min interval between two scans) to retrieve the AOD over land. Using the radiative transfer equation for plane-parallel atmosphere, two coupled differential equations for the upward and downward fluxes are derived. The boundary conditions for the upward and downward fluxes at the top and at the bottom of the atmosphere are used in these equations to provide an analytic solution for the AOD. To derive these fluxes, the aerosol single scattering albedo (SSA) and asymmetry factor are required to provide a solution. These are provided from a set of six pre-defined aerosol types with the SSA and asymmetry factor. We assume one aerosol type for a grid of 1 • ×1 • and the surface reflectance changes little between two subsequent observations. A k-ratio approach is used in the inversion to find the best solution of atmospheric properties and surface reflectance. The k-ratio approach assumes that the surface reflectance is little influenced by aerosol scattering at 1.6 µm and therefore the ratio of surface reflectances in the solar band for two subsequent observations can be wellapproximated by the ratio of the reflectances at 1.6 µm. A further assumption is that the surface reflectance varies only slightly over a period of 30 min. The algorithm makes use of numerical minimisation routines to obtain the optimal solution of atmospheric properties and surface reflectance by selection of the most suitable aerosol type from pre-defined sets.A detailed analysis of the retrieval results shows that it is suitable for AOD retrieval over land from SEVIRI data. Six AErosol RObotic NETwork (AERONET) sites with different surface types are used for detailed analysis and 42 other AERONET sites are used for validation. From 445 collocations representing stable and homogeneous aerosol type, we find that >75 % of the MSG-retrieved AOD at 0.6 and 0.8 µm values compare favourably with AERONET observed AOD values, within an error envelope of ± 0.05 ± 0.15τ and a high correlation coefficient (R>0.86). The AOD datasets derived Published by Copernicus Publications on behalf of the European Geosciences Union. L. Mei et al.: Retrieval of aerosol optical depth over landusing the TS method with SEVIRI data is also compared with collocated AOD products derived from NASA TERRA and AQUA MODIS (The Moderate-resolution Imaging Spectroradiometer) data using the Dark Dense Vegetation (DDV) method and the Deep Blue algorithms. Using the TS method, the AOD could be retrieved for more pixels than with the NASA Deep Blue algorithm. This method is potentially also useful for surface reflectance retrieval using SEVIRI observations. The current paper focuses on AOD retrieval ...

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“…These are used as an indicator of both the cumulative (total number concentration) aerosol contributions and aerosol type (size). This parameter, which is sometimes labelled as an aerosol index (Deuzé et al, 2001), can be used as a firstorder check on the radiative efficiency i.e., the ability to represent the absorption, scattering and the size of aerosols in the atmosphere because aerosol backscattering (radiance) depends (in a single scattering sense) on the product of the AOD and the phase function. This product, in turn, is related to α*AOD500 since an increase in the backscattering phase function corresponds to a decrease in particle size and hence an increase in α.…”

Section: Correlation Analysismentioning

confidence: 99%

Characterization of atmospheric aerosols across Canada from a ground‐based sunphotometer network: AEROCAN

et al. 2001

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Remote sensing of aerosols over land surfaces from POLDER‐ADEOS‐1 polarized measurements

Cited by 450 publications

References 43 publications

Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data

Characterization of aerosol pollution events in France using ground-based and POLDER-2 satellite data

Retrieval of aerosol optical depth over land based on a time series technique using MSG/SEVIRI data

Characterization of atmospheric aerosols across Canada from a ground‐based sunphotometer network: AEROCAN

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