Global evaluation of the Collection 5 MODIS dark-target aerosol products over land

Levy, Robert C.; Remer, L. A.; Kleidman, R. G.; Mattoo, S.; Ichoku, Charles; Kahn, R. A.; Eck, T. F.

doi:10.5194/acp-10-10399-2010

Cited by 1,154 publications

(986 citation statements)

References 62 publications

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“…This rule shows MODIS underestimates AODs in light aerosol loadings and permits negative values. It coincides with Levy's analysis [15].…”

Section: Figure 2 a Portion Of Decision Tree Flowchart By Analysis Ofmentioning

confidence: 71%

“…It is a common standard used in AOD validation as shown in many studies [11,13,15]. Furthermore, an accurate retrieval is defined by an instrument AOD retrieval that falls within the EE envelope,…”

Section: Methodsmentioning

confidence: 99%

“…Levy et al compared MODIS C005 AODs over dark land with AERONET collocations at 550 nm wavelength, the result shows RMSE=0.116, R 2 =0.778 (corresponding to correlation coefficient R=0.882) [15]. But their collocation points only cover dark targets.…”

Section: Figure 2 a Portion Of Decision Tree Flowchart By Analysis Ofmentioning

confidence: 99%

“…Under other conditions, however, they show systematic deviation. For example, under the condition of low aerosol loading, the retrieve of AOD is more accurate in the eastern United States, Western Europe and Southern Africa sites (because their spectrum is closer to the middle "dark" and moderate "green" regimes); whereas the AOD retrieval is often overestimated by MODIS in western United States and Central Asia's sites -because of its relatively bright surface and less greenness, MODIS overestimates the AOD [15]. In another example, Jethva et al found that the errors in the fine-dominated AOD over the Indian region were large for most AOD retrievals in the C005 data [16].…”

Section: Introductionmentioning

confidence: 99%

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Improving retrieval accuracy for aerosol optical depth by fusion of MODIS and CALIOP data

Han

Ding

et al. 2017

Teh. vjesn.

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Original scientific paper MODIS and CALIOP are two independent observation instruments in the A-train satellite constellation. They both provide aerosol optical depth (AOD) retrievals and scan the same points on the Earth's surface within a two-minute interval. With different design principles, MODIS and CALIOP instruments obtain varying AOD retrieval accuracies under different conditions. In this paper, we propose a two-stage fusion approach, including an analysis stage and an integration stage, to improve AOD retrieval accuracy. In the analysis stage, we systematically analyse conditions where MODIS retrieves well while CALIOP does not, and vice-versa. In the integration stage, we combine AOD retrievals from both instruments together by drawing on the other's strong points to make up one's weak points. We test the fusion approach on the two-year collocated data from MODIS, CALIOP and AERONET. The fusion result is significantly more accurate than AOD retrievals from any single observation facility. Keywords: MODIS; CALIOP; AERONET; Aerosol Optical Depth; FusionPoboljšanje točnosti pronalaženja podataka za optičku dubinu aerosola spajanjem podataka MODIS i CALIOP Izvorni znanstveni članak MODIS i CALIOP su dva neovisna promatračka instrumenta u konstelaciji A-train satelita. Obadva daju podatke za optičku dubinu aerosola (AODaerosol optical depth) i skeniraju iste točke na površini zemlje unutar intervala od dvije minute. Različitih principa dizajna, MODIS i CALIOP instrumenti daju različite točnosti podataka AOD pod različitim uvjetima. U ovom radu predlažemo pristup dvostepene fuzije, uključujući stadij analize i stadij integracije, kako bi se poboljšala točnost pronalaženja AOD podataka. U stadiju analize sistematski analiziramo uvjete pod kojima se MODIS-om podaci dobro pronalaze dok CALIOP-om ne i obrnuto. U stadiju integracije, kombiniramo AOD podatke obadva instrumenta zajedno koristeći prednosti jednoga da bi se nadomjestili nedostaci drugoga. Ispitujemo pristup fuzije na podacima skupljanim dvije godine uz MODIS, CALIOP i AERONET. Taj rezultat fuzije je znatno točniji od AOD prikupljenih podataka s bilo kojeg pojedinačnog instrumenta za promatranje.

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“…This rule shows MODIS underestimates AODs in light aerosol loadings and permits negative values. It coincides with Levy's analysis [15].…”

Section: Figure 2 a Portion Of Decision Tree Flowchart By Analysis Ofmentioning

confidence: 71%

Section: Methodsmentioning

confidence: 99%

Section: Figure 2 a Portion Of Decision Tree Flowchart By Analysis Ofmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Improving retrieval accuracy for aerosol optical depth by fusion of MODIS and CALIOP data

Han

Ding

et al. 2017

Teh. vjesn.

View full text Add to dashboard Cite

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“…It relies primarily on the use of low reflectance pixels or dark targets with low reflectance (Kaufman, 1993;Kaufman et al, 1990;Kaufman and Sendra, 1988) and requires prior knowledge of accurate ground surface reflectance (King et al, 1992). One major limitation of the MODIS DDV approach is that when the surface reflectance at 2.1 µm is above 0.15, no retrievals are performed and the assumption of transparency in this channel is not considered valid (Kaufman et al, 1997;Hsu et al, 2004;Remer et al, 2005;Levy et al, 2010). For the retrieval over bright surfaces such as arid, semi-arid and urban areas, Hsu et al (2004) developed the Deep Blue algorithm which uses the fact that the surface is much darker in the blue spectral region than that at longer wavelengths.…”

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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Comparison of aerosol optical depth between CALIOP and MODIS‐Aqua for CALIOP aerosol subtypes over the ocean

Kim

Yoon

et al. 2013

JGR Atmospheres

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[1] The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) has been compared with the Moderate Resolution Imaging Spectroradiometer (MODIS)-Aqua AOD using Level 2 products of both instruments. Such comparisons have been performed for five different aerosol subtypes classified by CALIOP algorithm, namely clean marine, dust, polluted dust, polluted continental, and biomass burning, over the ocean from June 2006 to December 2010. MODIS AOD at 550 nm (0.111 ± 0.079) for the collocated data pairs is about 63% higher than CALIOP AOD at 532 nm (0.068 ± 0.073). For clean marine, MODIS AOD (0.110 ± 0.064) is almost twice the CALIOP AOD (0.056 ± 0.038), and the difference between the AOD values has a strong latitude dependence likely related to the surface wind speed over the ocean. The difference in AOD for dust (13%) is observed to be the lowest among the five aerosol types under consideration, but it shows a slight regional variation. The discrepancy of AOD for dust also shows strong dependency on the layer mean of the particulate depolarization ratio. CALIOP AOD is higher than MODIS AOD for both polluted dust and polluted continental by 15% and 29%, respectively, for most of the ocean. One of the possible reasons for the difference is the misclassification of clean marine (or marine + dust) as polluted dust and polluted continental in the CALIOP algorithm. For biomass burning, uncertainty in the layer base altitude is thought to be one of the main reasons for the lower value of CALIOP AOD.

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Global evaluation of the Collection 5 MODIS dark-target aerosol products over land

Cited by 1,154 publications

References 62 publications

Improving retrieval accuracy for aerosol optical depth by fusion of MODIS and CALIOP data

Improving retrieval accuracy for aerosol optical depth by fusion of MODIS and CALIOP data

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

Comparison of aerosol optical depth between CALIOP and MODIS‐Aqua for CALIOP aerosol subtypes over the ocean

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