Retrieval of aerosol optical thickness over land surfaces from top‐of‐atmosphere radiance

Hoyningen‐Huene, W. von; Freitag, M.; Burrows, John

doi:10.1029/2001jd002018

Cited by 169 publications

(127 citation statements)

References 33 publications

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“…Although spheroids may be unable to represent the entire shape complexity for dust, this spheroid method is preferable over methods for spheres. An important issue is also the choice of the correct refractive index for dust (Volten et al, 2001). Observational data for the Sahara region (Dubovik et al, 2002;Sinyuk et al, 2003) demonstrate that the dust absorbing strength is spectrally dependent and decreases from the UV (refractive index near 0.005) to the near-IR (refractive index near 0.001).…”

Section: Definition Of Common Aerosol Componentsmentioning

confidence: 99%

“…The distribution of aspect ratios ranging between 1.44 and 3.0 was derived by Dubovik et al (2006) by fitting phase matrices of dust measured by Volten et al (2001) in laboratory experiments. Although spheroids may be unable to represent the entire shape complexity for dust, this spheroid method is preferable over methods for spheres.…”

Section: Definition Of Common Aerosol Componentsmentioning

confidence: 99%

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Aerosol retrieval experiments in the ESA Aerosol_cci project

et al. 2013

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Within the ESA Climate Change Initiative (CCI) project Aerosol cci (2010-2013), algorithms for the production of long-term total column aerosol optical depth (AOD) datasets from European Earth Observation sensors are developed. Starting with eight existing pre-cursor algorithms three analysis steps are conducted to improve and qualify the algorithms: (1) a series of experiments applied to one month of global data to understand several major sensitivities to assumptions needed due to the ill-posed nature of the underlying inversion problem, (2) a round robin exercise of "best" versions of each of these algorithms (defined using the step 1 outcome) applied to four months of global data to identify mature algorithms, and (3) a comprehensive validation exercise applied to one complete year of global data produced by the algorithms selected as mature based on the round robin exercise. The algorithms tested included four using AATSR, three using MERIS and one using PARASOL. This paper summarizes the first step. Three experiments were conducted to assess the potential impact of major assumptions in the various aerosol retrieval algorithms. In the first experiment a common set of four aerosol components was used to provide all algorithms with the same assumptions. The second experiment introduced an aerosol property climatology, derived from a combination of model and sun photometer observations, as a priori information in the retrievals on the occurrence of the common aerosol components. The third experiment assessed the impact of using a common nadir cloud mask for AATSR and MERIS algorithms in order to characterize the sensitivity to remaining cloud contamination in the retrievals against the baseline dataset versions. The impact of the algorithm changes was assessed for one month (September 2008) of data: qualitatively by inspection of monthly mean AOD maps and quantitatively by comparing daily gridded satellite data against daily averaged AERONET sun photometer observations for the different versions of each algorithm globally (land and coastal) and for three regions with different aerosol regimes. The analysis allowed for an assessment of sensitivities of all algorithms, which helped define the best algorithm versions for the subsequent round robin exercise; all algorithms (except for MERIS) showed some, in parts significant, improvement. In particular, using common aerosol components and partly also a priori aerosol-type climatology is beneficial. On the other hand the use of an AATSR-based common cloud mask meant a clear improvement (though with significant reduction of coverage) for the MERIS standard product, but not for the algorithms using AATSR. It is noted that all these observations are mostly consistent for all five analyses (global land, global coastal, three regional), which can be understood well, since the set of aerosol components defined in Sect. 3.1 was explicitly designed to cover different global aerosol regimes (with low and high absorption fine mode, sea salt and dust)

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Section: Definition Of Common Aerosol Componentsmentioning

confidence: 99%

Section: Definition Of Common Aerosol Componentsmentioning

confidence: 99%

Aerosol retrieval experiments in the ESA Aerosol_cci project

et al. 2013

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“…The corresponding LUTs have been calculated using the scalar version of 6S code (Vermote et al, 1997) for a range of atmospheric aerosols such as the smoke aerosol, continental aerosol, dust aerosol, maritime aerosol, and urban (black carbon) aerosol. The optimum value of AOT and aerosol model is selected on the basis of best-fit of surface reflectances to the model implemented in the algorithm (North et al, 1999;North 2002), and adapted for efficient processing of global data by Grey et al (2006a).…”

Section: Swansea University Algorithmmentioning

confidence: 99%

The inter-comparison of major satellite aerosol retrieval algorithms using simulated intensity and polarization characteristics of reflected light

et al. 2010

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Abstract. Remote sensing of aerosol from space is a challenging and typically underdetermined retrieval task, requiring many assumptions to be made with respect to the aerosol and surface models. Therefore, the quality of a priori information plays a central role in any retrieval process (apart from the cloud screening procedure and the forward radiative transfer model, which to be most accurate should include the treatment of light polarization and molecular-aerosol coupling). In this paper the performance of various algorithms with respect to the of spectral aerosol optical thickness determination from optical spaceborne measurements is studied. The algorithms are based on various types of measurements (spectral, angular, polarization, or some combination of these). It is confirmed that multiangular spectropolarimetric measurements provide more powerful constraints compared to spectral intensity measurements alone, particularly those acquired at a single view angle and which rely on a priori assumptions regarding the particle phase function in the retrieval process.

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“…The contribution of surface reflectance on AOD retrieval is well documented (Von Hoyningen-Huene et al, 2003;Lee et al, 2006;Guo et al, 2009;Wong et al, 2010). Any attempt to retrieve aerosol characteristics from a satellite image depends on the method of decoupling both surface and aerosol reflectance and its subsequent modeling using a radiative transfer model (RTM).…”

Section: Introductionmentioning

confidence: 99%

“…Aerosol properties retrieval over ocean surfacesfrom satellite-based measurements utilizes the Near Infra-Red (NIR) channels (Von Hoyningen-Huene et al, 2003). This is because the ocean surfaces experiences relatively low spectral surface reflectance which can be ignored in the process of aerosol retrieval (Geogdzhayev et al, 2002).…”

Section: Introductionmentioning

confidence: 99%