Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

Lee, Jae-Hwa; Kim, Jhoon; Song, Chul H.; Ryu, Ja‐Hyoung; Ahn, Yu-Hwan; Song, Chang‐Keun

doi:10.1016/j.rse.2009.12.021

Cited by 105 publications

(81 citation statements)

References 25 publications

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“…2.1 Overview of the GOCI YAER V1 and V2 algorithm framework A prototype of the GOCI YAER algorithm for use over the ocean (Lee et al, 2010) was developed using MODIS Level 1B (L1B) top-of-atmosphere (TOA) reflected radiance data and improved using nonspherical AOPs . Then, using real GOCI L1B TOA radiance data, the GOCI YAER V1 algorithm for use over land and ocean surfaces was developed .…”

Section: Goci Yaer Valgorithmmentioning

confidence: 99%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleighcorrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τ G is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τ G has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τ A from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τ G − τ A ) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τ G are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

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Section: Goci Yaer Valgorithmmentioning

confidence: 99%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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“…Over ocean we use an algorithm that has been used for various applications: retrieving aerosol properties from Geostationary Ocean Color Imager [Lee et al, 2010b], testing effects of nonspherical dust models on AOD retrieval accuracy [Lee et al, 2012], correcting for thin cirrus clouds in AOD retrievals [Lee et al, 2013], and deriving aerosol direct radiative effects [Lee et al, 2014]. This algorithm is expected to perform well particularly for dust cases as it includes nonspherical dust properties in the forward model; other aspects of the algorithm are similar to other algorithms that have been applied to similar satellite sensors [e.g., Sayer et al, 2012;Levy et al, 2013].…”

Section: Viirs Deep Blue Aerosol Productsmentioning

confidence: 99%

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

et al. 2015

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This study extends the application of the previously developed Aerosol Single-scattering albedo and layer Height Estimation (ASHE) algorithm, which was originally applied to smoke aerosols only, to both smoke and dust aerosols by including nonspherical dust properties in the retrieval process. The main purpose of the algorithm is to derive aerosol height information over wide areas using aerosol products from multiple satellite sensors simultaneously: aerosol optical depth (AOD) and Ångström exponent from the Visible Infrared Imaging Radiometer Suite (VIIRS), UV aerosol index from the Ozone Mapping and Profiler Suite (OMPS), and total backscatter coefficient profile from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The case studies suggest that the ASHE algorithm performs well for both smoke and dust aerosols, showing root-mean-square error of the retrieved aerosol height as compared to CALIOP observations from 0.58 to 1.31 km and mean bias from À0.70 to 1.13 km. In addition, the algorithm shows the ability to retrieve single-scattering albedo to within 0.03 of Aerosol Robotic Network inversion data for moderate to thick aerosol loadings (AOD of~1.0). For typical single-layered aerosol cases, the estimated uncertainty in the retrieved height ranges from 1.20 to 1.80 km over land and from 1.15 to 1.58 km over ocean when favorable conditions are met. Larger errors are observed for multilayered aerosol events, due to the limited sensitivities of the passive sensors to such cases.

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“…Figure 1 shows the flowchart for the GOCI YAER algorithm. The improvements made to the algorithm as compared to described in Lee et al (2010b) will be discussed according to the sequence shown in the flowchart. The algorithm uses topof-atmosphere (TOA) reflectance (ρ TOA ) as input data,…”

Section: Improvements Of the Goci Yaer Algorithmmentioning

confidence: 99%

“…It observes East Asia hourly during the daytime, a total of eight times per day. A prototype of the GOCI Yonsei Aerosol Retrieval (YAER) algorithm was developed (Lee et al, 2010b) and is improved in this study to include dynamic (changing with AOD) and nonspherical aerosol models as introduced in Lee et al (2012). Aerosol optical properties (AOPs) such as aerosol optical depth, size information, and absorptivity can be retrieved hourly from the GOCI YAER algorithm with spatial resolution of 6 km × 6 km.…”

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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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Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager

Cited by 105 publications

References 25 publications

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

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

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