Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements

Abstract. A new Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm has been applied in Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and recently provides globally high spatial resolution Aerosol Optical Depth (AOD) products at 1 km. Meanwhile, several improvements are modified in classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS collection 6.1 products. However, validation and comparison for MAIAC, DT and DB algorithms is still lacking in China. In this paper, a comprehensive assessment and comparison of AOD products at 550 nm wavelength based three aerosol retrieval algorithms in MODIS sensor using ground-truth measurements from Aerosol Robotic Network (AERONET) sites over China during 2000 to 2017 is presented. In general, after quality assurance (QA) filter, the coefficient of determination (R2=0.854), correlation coefficient (R=0.929), root-mean-square error (RMSE=0.178), mean bias (Bias=0.019) and the fraction fall within expected error (Within_EE=67.10 %, EE=±(0.05+0.15×AOD)) results for MAIAC algorithm show better accuracy than those from DT and DB algorithms. While the R2, R, RMSE, Bias and Within_EE of DT algorithm are 0.817, 0.930, 0.192, 0.077, 55.36 %, respectively, those corresponding statistics for DB algorithm are 0.827, 0.921, 0.190, 0.018, 63.32 %. Moreover, the spatiotemporal completeness for MAIAC (29.69 %) product is also better than DT (8.00 %) and DB (19.50 %) products after QA filter. In addition, the land type dependence characteristic, view geometry dependence, spatiotemporal retrieval accuracy and spatial variation pattern difference for three products are also analyzed in details.

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Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Atmospheric Chemistry and Physics ◽

10.5194/acp-19-8243-2019 ◽

2019 ◽

Vol 19 (12) ◽

pp. 8243-8268 ◽

Cited By ~ 12

Author(s):

Ning Liu ◽

Bin Zou ◽

Huihui Feng ◽

Wei Wang ◽

Yuqi Tang ◽

...

Keyword(s):

Zenith Angle ◽

Atmospheric Correction ◽

Ground Truth ◽

The North ◽

Deep Blue ◽

Aerosol Retrieval ◽

Angle Scattering ◽

Retrieval Algorithms ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Types

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-9565-2015 ◽

2015 ◽

Vol 8 (9) ◽

pp. 9565-9609 ◽

Cited By ~ 6

Author(s):

M. Choi ◽

J. Kim ◽

J. Lee ◽

M. Kim ◽

Y. Je Park ◽

...

Keyword(s):

Optical Properties ◽

Land Surface ◽

Pearson Correlation ◽

Ocean Color ◽

Single Scattering ◽

Surface Reflectance ◽

Robotic Networks ◽

Deep Blue ◽

Aerosol Retrieval ◽

Highly Correlated

Abstract. The Geostationary Ocean Color Imager (GOCI) onboard the Communication, Ocean, and Meteorology Satellites (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 to the GOCI Yonsei Aerosol Retrieval (YAER) algorithm over ocean and land together with validation results during the DRAGON-NE Asia 2012 campaign. 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, Angstrom exponent (AE) between 440 and 860 nm, and aerosol type from selected aerosol models in calculating AOD. Assumed aerosol models are compiled from global Aerosol Robotic Networks (AERONET) inversion data, and categorized according to AOD, FMF, and SSA. Nonsphericity is considered, and unified aerosol models are used over land and ocean. Different assumptions for surface reflectance are applied over ocean and land. Surface reflectance over the ocean varies with geometry and wind speed, while surface reflectance over land is obtained from the 1–3 % darkest pixels in a 6 km × 6 km area during 30 days. In the East China Sea and Yellow Sea, significant area is covered persistently by turbid waters, for which the land algorithm is used for aerosol retrieval. To detect turbid water pixels, TOA reflectance difference at 660 nm is used. GOCI YAER products are validated using other aerosol products from AERONET and the MODIS Collection 6 aerosol data from "Dark Target (DT)" and "Deep Blue (DB)" algorithms during the DRAGON-NE Asia 2012 campaign from March to May 2012. Comparison of AOD from GOCI and AERONET gives a Pearson correlation coefficient of 0.885 and a linear regression equation with GOCI AOD =1.086 × AERONET AOD – 0.041. GOCI and MODIS AODs are more highly correlated over ocean than land. Over land, especially, GOCI AOD shows better agreement with MODIS DB than MODIS DT because of the choice of surface reflectance assumptions. Other GOCI YAER products show lower correlation with AERONET than AOD, but are still qualitatively useful.

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Geoscientific Model Development ◽

10.5194/gmd-9-2377-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2377-2389 ◽

Cited By ~ 4

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Peter M. Norris ◽

Steven Platnick ◽

Shana Mattoo ◽

...

Keyword(s):

Remote Sensing ◽

Weather Prediction ◽

Retrieval Algorithm ◽

Model Parameters ◽

Aerosol Retrieval ◽

Model Aerosol ◽

Numerical Experimentation ◽

Retrieval Algorithms ◽

Sensor Cloud ◽

Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

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Inter-comparison of three AATSR Level 2 (L2) AOD products over China

10.5194/acp-2016-195 ◽

2016 ◽

Author(s):

Y. Che ◽

Y. Xue ◽

L. Mei ◽

J. Guang ◽

H. Xu ◽

...

Keyword(s):

Mainland China ◽

Western China ◽

Mean Bias Error ◽

Retrieval Algorithm ◽

Bias Error ◽

Aerosol Retrieval ◽

Retrieval Algorithms ◽

Level 3 ◽

The Stability ◽

Level 2

Abstract. The Advanced Along-Track Scanning Radiometer (AATSR) aboard on ENVISAT is used to observe the Earth by dual-view. The AATSR data can be used to retrieve aerosol optical depth (AOD) over both land and ocean, which is an important merit in the characterization of aerosol properties. In recent years, aerosol retrieval algorithms both over land and ocean have been developed, taking advantages of the feature of dual-view which can help eliminate contribution of Earth's surface to top of atmosphere (TOA) reflectance. Aerosol_cci project as a part of Climate Change Initiative (CCI) provides users three AOD retrieval algorithms for AATSR data, including the Swansea algorithm (SU), the ATSR-2ATSR dual view aerosol retrieval algorithm (ADV) and the Oxford-RAL Retrieval of Aerosol and Cloud algorithm (ORAC). The Validation team of Aerosol-CCI project has validated AOD (both Level 2 and Level 3 products) and AE (Level 2 product only) against the AERONET data in a round robin evaluation using validation tool of AeroCOM (Aerosol Comparison between Observations and Models) project. For the purpose of evaluating different performances of these three algorithms on calculating AODs over mainland China, we introduce ground-based data from the CARSNET (the China Aerosol Remote Sensing Network) which is designed for aerosol observation in China. Because China is vast in territory and of great differences in surface, the combination of the AEROENT and the CATRNET data can validate L2 AOD products more comprehensively. The validation results show different performances of these products in 2007, 2008 and 2010. The SU algorithm has very good performance over sites with different surface conditions in mainland China from March to October, but it underestimates AOD slightly with varying mean bias error (MBE) from 0.05 to 0.10 over surface of barren or sparsely vegetation in western China. The ADV product has same precision with high correlation coefficient (CC) larger than 0.90 over most of sites and same error distribution as the SU product. The main limits of ADV algorithm are underestimation and applicability, especially it occurs obvious underestimation over sites of Datong, Lanzhou and Urmuchi where the dominated land cover is grassland with MBE larger than 0.2 and the main source of aerosol is coal combustion and dust. The ORAC algorithm has the ability of retrieving AOD at different ranges including high AOD (larger than 1.0), however, the stability will decease significantly as AOD grows, especially when AOD > 1.0. In addition, ORAC product get matches successfully collocated with CARSNET in winter (December, January and February), whereas other validation results lack matches during winter.

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