Suomi‐NPP VIIRS aerosol algorithms and data products

Jackson, John M.; Hongqing, Liu; László, István; Kondragunta, Shobha; Remer, L. A.; Huang, Jingfeng; Huang, Ho–Chun

doi:10.1002/2013jd020449

Cited by 229 publications

(204 citation statements)

References 58 publications

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“…This instrument is a scanning radiometer that can extend and improve upon the heritage of AVHRR and MODIS [16]. VIIRS aerosol retrieval is performed at the pixel level and produces aerosol products with a spatial resolution of 0.75 km [31]. The product of this process, known as IP, is then aggregated and designated as an Environmental Data Record (EDR) reported at 6 km (8 × 8 pixels) resolution at nadir [16].…”

Section: Viirs Datamentioning

confidence: 99%

Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan

et al. 2017

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Visible Infrared Imaging Radiometer Suite (VIIRS) is a next-generation polar-orbiting operational environmental sensor with a capability for global aerosol observations. A comprehensive validation of VIIRS products is significant for improving product quality, assessing environment quality for human life, and studying regional climate change. In this study, three-year (from 1 January 2014 to 31 December 2016) records of VIIRS Intermediate Product (IP) data and Moderate Resolution Imaging Spectroradiometer (MODIS) retrievals on aerosol optical depth (AOD) at 550 nm were evaluated by comparing them to ground sun photometer measurements over Wuhan. Results indicated that VIIRS IP retrievals were underestimated by 5% for the city. A comparison of VIIRS IP retrievals and ground sun photometer measurements showed a lower R 2 of 0.55 (0.79 for Terra-MODIS and 0.76 for Aqua-MODIS), with only 52% of retrievals falling within the expected error range established by MODIS over land (i.e., ±(0.05 + 0.15AOD)). Bias analyses with different Ångström exponents (AE) demonstrated that land aerosol model selection of the VIIRS retrieval over Wuhan was appropriate. However, the larger standard deviations (i.e., uncertainty) of VIIRS AODs than MODIS AODs could be attributed to the less robust retrieval algorithm. Monthly variations displayed largely underestimated AODs of VIIRS in winter, which could be caused by a large positive bias in surface reflectance estimation due to the sparse vegetation and greater surface brightness of Wuhan in this season. The spatial distribution of VIIRS and MODIS AOD observations revealed that the VIIRS IP AODs over high-pollution areas (AOD > 0.8) with sparse vegetation were underestimated by more than 20% in Wuhan, and 40% in several regions. Analysis of several clear rural areas (AOD < 0.2) with native vegetation indicated an overestimation of about 20% in the northeastern region of the city. These findings showed that the VIIRS IP AOD at 550 nm can provide a solid dataset with a high resolution (750 m) for quantitative scientific investigations and environmental monitoring over Wuhan. However, the performance of dark target algorithms in VIIRS was associated with aerosol types and ground vegetation conditions.

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Section: Viirs Datamentioning

confidence: 99%

Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan

et al. 2017

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“…Most cloud pixels are effectively screened by the cloud masking steps, but thin cloud pixels remain and show high ρ 660 above 0.05 (red color). This is a similar to the "visible reflectance" anomaly of the VIIRS aerosol algorithm (Jackson et al, 2013). Because pixels with ρ 660 above 0.02 are considered as severe turbid water and screened, the remaining cloud pixels are also masked using this test.…”

Section: Turbid Water Detectionmentioning

confidence: 91%

“…Many aerosol retrieval algorithms have been developed and improved using multi-channel sensors in LEO such as the Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-View Sensor (SeaWiFS), Medium Resolution Imaging Spectrometer (MERIS), Ozone Monitoring Instrument (OMI), and Visible Infrared Imaging Radiometer Suite (VIIRS) (Higurashi and Nakajima, 1999; J. Hsu et al, 2006Hsu et al, , 2013Jackson et al, 2013;Kaufman et al, 1997a;Levy et al, 2007Levy et al, , 2013Remer et al, 2005;Sayer et al, 2012;Torres et al, 1998Torres et al, , 2007Torres et al, , 2012. Multi-channel observations from LEO give global coverage at high accuracy but with the disadvantage of low temporal resolution.…”

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confidence: 99%

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

et al. 2016

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Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorological Satellite (COMS) is the first multi-channel ocean color imager in geostationary orbit. Hourly GOCI top-of-atmosphere radiance has been available for the retrieval of aerosol optical properties over East Asia since March 2011. This study presents improvements made to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm together with validation results during the Distributed Regional Aerosol Gridded Observation Networks -Northeast Asia 2012 campaign (DRAGON-NE Asia 2012 campaign). The evaluation during the spring season over East Asia is important because of high aerosol concentrations and diverse types of Asian dust and haze. Optical properties of aerosol are retrieved from the GOCI YAER algorithm including aerosol optical depth (AOD) at 550 nm, fine-mode fraction (FMF) at 550 nm, single-scattering albedo (SSA) at 440 nm, Ångström exponent (AE) between 440 and 860 nm, and aerosol type. The aerosol models are created based on a global analysis of the Aerosol Robotic Networks (AERONET) inversion data, and covers a broad range of size distribution and absorptivity, including nonspherical dust properties. The Cox-Munk ocean bidirectional reflectance distribution function (BRDF) model is used over ocean, and an improved minimum reflectance technique is used over land. Because turbid water is persistent over the Yellow Sea, the land algorithm is used for such cases. The aerosol products are evaluated against AERONET observations and MODIS Collection 6 aerosol products retrieved from Dark Target (DT) and Deep Blue (DB) algorithms during the DRAGON-NE Asia 2012 campaign conducted from March to May 2012. Comparison of AOD from GOCI and AERONET resulted in a Pearson correlation coefficient of 0.881 and a linear regression equation with GOCI AOD = 1.083 × AERONET AOD − 0.042. The correlation between GOCI and MODIS AODs is higher over ocean than land. GOCI AOD shows better agreement with MODIS DB than MODIS DT. The other GOCI YAER products (AE, FMF, and SSA) show lower correlation with AERONET than AOD, but still show some skills for qualitative use.

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“…However, neither one of these instruments were specifically designed to retrieve aerosol properties. The more recent dedicated aerosol sounders, such as ATSR (The Along Track Scanning Radiometer 2, LlewellynJones and Remedios, 2012), MODIS (Moderate Resolution Imaging Spectroradiometer, Levy et al, 2010), VIISR (Visible Infrared Imaging Radiometer Suite, Jackson et al, 2013) and MISR (Multi-angle Imaging SpectroRadiometer, Kahn and Gaitley, 2015) offer data from 1995, 2000 and 2002 onwards respectively. It is therefore apparent that neither sun photometer nor satellite records of AOD are available for all the decades where industrialization has had a significant effect on the aerosol load.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

et al. 2016

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Abstract. In order to have a good estimate of the current forcing by anthropogenic aerosols, knowledge on past aerosol levels is needed. Aerosol optical depth (AOD) is a good measure for aerosol loading. However, dedicated measurements of AOD are only available from the 1990s onward. One option to lengthen the AOD time series beyond the 1990s is to retrieve AOD from surface solar radiation (SSR) measurements taken with pyranometers. In this work, we have evaluated several inversion methods designed for this task. We compared a look-up table method based on radiative transfer modelling, a non-linear regression method and four machine learning methods (Gaussian process, neural network, random forest and support vector machine) with AOD observations carried out with a sun photometer at an Aerosol Robotic Network (AERONET) site in Thessaloniki, Greece. Our results show that most of the machine learning methods produce AOD estimates comparable to the lookup table and non-linear regression methods. All of the applied methods produced AOD values that corresponded well to the AERONET observations with the lowest correlation coefficient value being 0.87 for the random forest method. While many of the methods tended to slightly overestimate low AODs and underestimate high AODs, neural network and support vector machine showed overall better correspondence for the whole AOD range. The differences in producing both ends of the AOD range seem to be caused by differences in the aerosol composition. High AODs were in most cases those with high water vapour content which might affect the aerosol single scattering albedo (SSA) through uptake of water into aerosols. Our study indicates that machine learning methods benefit from the fact that they do not constrain the aerosol SSA in the retrieval, whereas the LUT method assumes a constant value for it. This would also mean that machine learning methods could have potential in reproducing AOD from SSR even though SSA would have changed during the observation period.

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Suomi‐NPP VIIRS aerosol algorithms and data products

Cited by 229 publications

References 58 publications

Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan

Validation of VIIRS AOD through a Comparison with a Sun Photometer and MODIS AODs over Wuhan

GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign

Retrieval of aerosol optical depth from surface solar radiation measurements using machine learning algorithms, non-linear regression and a radiative transfer-based look-up table

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