Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

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Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-8-6877-2015 ◽

2015 ◽

Vol 8 (7) ◽

pp. 6877-6947 ◽

Cited By ~ 2

Author(s):

R. C. Levy ◽

L. A. Munchak ◽

S. Mattoo ◽

F. Patadia ◽

L. A. Remer ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Infrared Imaging ◽

Quantitative Information ◽

Environmental Data ◽

Retrieval Algorithm ◽

Climate Data ◽

Data Record ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångstrom Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ∼ 0.025), while reducing the differences between AE. We characterize algorithm retrievibility through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

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Cross-Comparison between MODIS and VIIRS Snow Cover Products for the 2016 Hydrological Year

Climate ◽

10.3390/cli7040057 ◽

2019 ◽

Vol 7 (4) ◽

pp. 57 ◽

Cited By ~ 1

Author(s):

Shubhechchha Thapa ◽

Parveen K. Chhetri ◽

Andrew G. Klein

Keyword(s):

Snow Cover ◽

Infrared Imaging ◽

Environmental Data ◽

Data Series ◽

Climate Data ◽

Snow Cover Area ◽

Transition Zones ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Normalized Difference Snow Index

The VIIRS (Visible Infrared Imaging Radiometer Suite) instrument on board the Suomi-NPP (National Polar-Orbiting Partnership) satellite aims to provide long-term continuity of several environmental data series including snow cover initiated with MODIS (Moderate Resolution Imaging Spectroradiometer). Although it is speculated that MODIS and VIIRS snow cover products may differ because of their differing spatial resolutions and spectral coverage, quantitative comparisons between their snow products are currently limited. Therefore, this study intercompares MODIS and VIIRS snow products for the 2016 Hydrological Year over the Midwestern United States and southern Canada. Two hundred and forty-four swath snow products from MODIS/Aqua (MYD10L2) and the VIIRS EDR (Environmental Data Records) (VSCMO/binary) were intercompared using confusion matrices, comparison maps and false color imagery. Thresholding the MODIS NDSI (Normalized Difference Snow Index) Snow Cover product at a snow cover fraction of 30% generated binary snow maps are most comparable to the NOAA VIIRS binary snow product. Overall agreement between MODIS and VIIRS was found to be approximately 98%. This exceeds the VIIRS accuracy requirements of 90% probability of correct typing. The agreement was highest during the winter but lower during late fall and spring. MODIS and VIIRS often mapped snow/no-snow transition zones as a cloud. The assessment of total snow and cloud pixels and comparison snow maps of MODIS and VIIRS indicate that VIIRS is mapping more snow cover and less cloud cover compared to MODIS. This is evidenced by the average area of snow in MYD10L2 and VSCMO being 5.72% and 11.43%, no-snow 26.65% and 28.67% and cloud 65.02% and 59.91%, respectively. While VIIRS and MODIS have a similar capacity to map snow cover, VIIRS has the potential to map snow cover area more accurately, for the successful development of climate data records.

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Improving GOES Advanced Baseline Imager (ABI) Aerosol Optical Depth (AOD) Retrievals using an Empirical Bias Correction Algorithm

10.5194/amt-2020-115 ◽

2020 ◽

Author(s):

Hai Zhang ◽

Shobha Kondragunta ◽

Istvan Laszlo ◽

Mi Zhou

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Bias Correction ◽

Land Surface ◽

Infrared Imaging ◽

Retrieval Algorithm ◽

Correction Algorithm ◽

Time Step ◽

Modis Aod ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multi-band algorithm similar to those of polar-orbiting satellites’ sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). Therefore, ABI AOD is expected to have accuracy and precision comparable to MODIS AOD and VIIRS AOD. However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to errors in the land surface reflectance relationship between the bands used in the ABI AOD retrieval algorithm, which vary with respect to the Sun-satellite geometry. To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30-day period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period August 6 to December 31, 2018 are used to validate the bias correction algorithm. For the top 2 qualities ABI AOD, after bias correction, the correlation between ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root mean square error (RMSE) improves from 0.09 to 0.05. These results for the bias corrected top 2 qualities ABI AOD are comparable to those of the uncorrected high-quality ABI AOD. Thus, by using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the area coverage of ABI AOD is substantially increased without loss of data accuracy.

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MODIS Retrieval of Aerosol Optical Depth over Turbid Coastal Water

10.20944/preprints201705.0157.v1 ◽

2017 ◽

Author(s):

Yi WANG ◽

Jun Wang ◽

Robert C Levy ◽

Xiaoguang Xu ◽

Jeffrey S Reid

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Coastal Water ◽

Visible Spectrum ◽

Single Scattering ◽

Retrieval Algorithm ◽

Modis Aod ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Large Water

We present a new approach to retrieve Aerosol Optical Depth (AOD) from Moderate Resolution Imaging Spectroradiometer (MODIS) over the turbid coastal water. This approach supplements the operational Dark Target (DT) aerosol retrieval algorithm that currently don’t conduct any AOD retrieval in the regions with large water-leaving radiances in the visible spectrum. Over the global coastal water regions in all cloud-free conditions, this unavailability of AOD retrievals due to the inherent limitation in existing DT algorithm is ~20%. Here, we refine the MODIS DT algorithm by considering that water-leaving radiance at 2.1 μm is negligible regardless of water turbidity. This refinement, with the assumption that the aerosol single scattering properties over coastal turbid water are similar to that over the adjacent open-ocean pixels, yields ~18% more of MODIS-AERONET collocated pairs for six AEROENT stations in the coastal water regions. Furthermore, comparison with these AERONET observations show that the new AOD retrievals are in either equivalent or better accuracy than those retrieved by the MODIS operational algorithm (over coastal land and non-turbid coastal water). Combining the new retrievals with the existing MODIS operational retrievals not only yield an overall improvement of AOD over those coastal water regions, but also successfully extend the spatial and temporal coverage of MODIS AOD retrievals over the coastal regions where 60% of human population resides, and thereby, aerosol impacts on regional air quality and climate are expected to be significant.

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Validation of MODIS 3 km Land Aerosol Optical Depth from NASA's EOS Terra and Aqua Missions

10.5194/amt-2018-44 ◽

2018 ◽

Author(s):

Pawan Gupta ◽

Lorraine A. Remer ◽

Robert C. Levy ◽

Shana Mattoo

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Retrieval Algorithm ◽

Data Sets ◽

Aerosol Retrieval ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Sensor Degradation ◽

Over Time

Abstract. The two MODerate Resolution Imaging Spectroradiometer (MODIS) sensors, aboard Earth Observing Satellites (EOS) Terra and Aqua, have been making aerosol observations for more than 15 years. From these observations, the MODIS dark target (DT) aerosol retrieval algorithm provides aerosol optical depth (AOD) products, globally over both land and ocean. In addition to the standard resolution product (10 × 10 km2), the MODIS collection 6 (C006) data release included a higher resolution (3 × 3 km2). Other than accommodations for the two different resolutions, the 10 km, and 3 km DT algorithms are basically the same. In this study, we perform global validation of the higher resolution AOD over global land by comparing against AERONET measurements. The MODIS-AERONET collocated data sets consist of 161,410 high-confidence AOD pairs from 2000 to 2015 for MODIS Terra and 2003 to 2015 for MODIS-Aqua. We find that 62.5 % and 68.4 % of AODs retrieved from MODIS-Terra and MODIS-Aqua, respectively, fall within previously published expected error bounds of ±(0.05 + 0.2*AOD), with a high correlation (R = 0.87). The scatter is not random but exhibits a mean positive bias of ~ 0.06 for Terra and ~ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e. true AOD) but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS-AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with MODIS-Terra showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.

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Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

10.5194/amt-2018-7 ◽

2018 ◽

Author(s):

Falguni Patadia ◽

Robert Levy ◽

Shana Mattoo

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Infrared Imaging ◽

Gas Absorption ◽

Shortwave Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, onboard Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper we use High-resolution TRANsmission (HITRAN) database and Line-by-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases up to 0.07. As recent studies are attempting to create a long-term data record by joining multiple satellite datasets, including MODIS and VIIRS, the consistency of gas correction becomes even more crucial.

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Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation

Atmospheric Measurement Techniques ◽

10.5194/amt-11-3205-2018 ◽

2018 ◽

Vol 11 (6) ◽

pp. 3205-3219 ◽

Cited By ~ 13

Author(s):

Falguni Patadia ◽

Robert C. Levy ◽

Shana Mattoo

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Infrared Imaging ◽

Gas Absorption ◽

Shortwave Radiation ◽

Radiative Transfer Model ◽

Transfer Model ◽

Data Sets ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. Retrieving aerosol optical depth (AOD) from top-of-atmosphere (TOA) satellite-measured radiance requires separating the aerosol signal from the total observed signal. Total TOA radiance includes signal from the underlying surface and from atmospheric constituents such as aerosols, clouds and gases. Multispectral retrieval algorithms, such as the dark-target (DT) algorithm that operates upon the Moderate Resolution Imaging Spectroradiometer (MODIS, on board Terra and Aqua satellites) and Visible Infrared Imaging Radiometer Suite (VIIRS, on board Suomi-NPP) sensors, use wavelength bands in “window” regions. However, while small, the gas absorptions in these bands are non-negligible and require correction. In this paper, we use the High-resolution TRANsmission (HITRAN) database and Line-By-Line Radiative Transfer Model (LBLRTM) to derive consistent gas corrections for both MODIS and VIIRS wavelength bands. Absorptions from H2O, CO2 and O3 are considered, as well as other trace gases. Even though MODIS and VIIRS bands are “similar”, they are different enough that applying MODIS-specific gas corrections to VIIRS observations results in an underestimate of global mean AOD (by 0.01), but with much larger regional AOD biases of up to 0.07. As recent studies have been attempting to create a long-term data record by joining multiple satellite data sets, including MODIS and VIIRS, the consistency of gas correction has become even more crucial.

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The NASA MODIS-VIIRS Continuity Cloud Optical Properties Products

Remote Sensing ◽

10.3390/rs13010002 ◽

2020 ◽

Vol 13 (1) ◽

pp. 2 ◽

Cited By ~ 1

Author(s):

Steven Platnick ◽

Kerry Meyer ◽

Galina Wind ◽

Robert E. Holz ◽

Nandana Amarasinghe ◽

...

Keyword(s):

Optical Properties ◽

Infrared Imaging ◽

Climate Data ◽

Earth Observing System ◽

Cloud Properties ◽

Data Record ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Decadal Climate ◽

Climate Data Record

The NASA Aqua MODIS and Suomi National Polar-Orbiting Partnership (SNPP) Visible Infrared Imaging Radiometer Suite (VIIRS) climate data record continuity cloud properties products (CLDPROP) were publicly released in April 2019 with an update later that year (Version 1.1). These cloud products, having heritage with the NASA Moderate-resolution Imaging Spectroradiometer (MODIS) MOD06 cloud optical properties product and the NOAA GOES-R Algorithm Working Group (AWG) Cloud Height Algorithm (ACHA), represent an effort to bridge the multispectral imager records of NASA’s Earth Observing System (EOS) and NOAA’s current generation of operational weather satellites to achieve a continuous, multi-decadal climate data record for clouds that can extend well into the 2030s. CLDPROP offers a “continuity of approach,” applying common algorithms and ancillary datasets to both MODIS and VIIRS, including utilizing only a subset of spectral channels available on both sensors to help mitigate instrument differences. The initial release of the CLDPROP_MODIS and CLDPROP_VIIRS data records spans the SNPP observational record (2012-present). Here, we present an overview of the algorithms and an evaluation of the intersensor continuity of the core CLDPROP_MODIS and CLDPROP_VIIRS cloud optical property datasets, i.e., cloud thermodynamic phase, optical thickness, effective particle size, and derived water path. The evaluation includes analyses of pixel-level MODIS/VIIRS co-locations as well as spatial and temporal aggregated statistics, with a focus on identifying and understanding the root causes of individual dataset discontinuities. The results of this evaluation will inform future updates to the CLDPROP products and help scientific users determine the appropriate use of the product datasets for their specific needs.

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Improving GOES Advanced Baseline Imager (ABI) aerosol optical depth (AOD) retrievals using an empirical bias correction algorithm

Atmospheric Measurement Techniques ◽

10.5194/amt-13-5955-2020 ◽

2020 ◽

Vol 13 (11) ◽

pp. 5955-5975

Author(s):

Hai Zhang ◽

Shobha Kondragunta ◽

Istvan Laszlo ◽

Mi Zhou

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Bias Correction ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Infrared Imaging ◽

Retrieval Algorithm ◽

Correction Algorithm ◽

Time Step

Abstract. The Advanced Baseline Imager (ABI) on board the Geostationary Operational Environmental Satellite-R (GOES-R) series enables retrieval of aerosol optical depth (AOD) from geostationary satellites using a multiband algorithm similar to those of polar-orbiting satellites' sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS). However, this work demonstrates that the current version of GOES-16 (GOES-East) ABI AOD has diurnally varying biases due to limitations in the land surface reflectance relationships between the 0.47 µm band and the 2.2 µm band and between the 0.64 µm band and 2.2 µm band used in the ABI AOD retrieval algorithm, which vary with the Sun–satellite geometry and NDVI (normalized difference vegetation index). To reduce these biases, an empirical bias correction algorithm has been developed based on the lowest observed ABI AOD of an adjacent 30 d period and the background AOD at each time step and at each pixel. The bias correction algorithm improves the performance of ABI AOD compared to AErosol RObotic NETwork (AERONET) AOD, especially for the high and medium (top 2) quality ABI AOD. AOD data for the period 6 August to 31 December 2018 are used to evaluate the bias correction algorithm. After bias correction, the correlation between the top 2 quality ABI AOD and AERONET AOD improves from 0.87 to 0.91, the mean bias improves from 0.04 to 0.00, and root-mean-square error (RMSE) improves from 0.09 to 0.05. These results for the bias-corrected top 2 qualities ABI AOD are comparable to those of the corrected high-quality ABI AOD. By using the top 2 qualities of ABI AOD in conjunction with the bias correction algorithm, the areal coverage of ABI AOD is increased by about 100 % without loss of data accuracy.

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An Effective and Efficient Enhanced Fixed Rank Smoothing Method for the Spatiotemporal Fusion of Multiple-Satellite Aerosol Optical Depth Products

Remote Sensing ◽

10.3390/rs12071102 ◽

2020 ◽

Vol 12 (7) ◽

pp. 1102

Author(s):

Bin Zou ◽

Ning Liu ◽

Wei Wang ◽

Huihui Feng ◽

Xiangping Liu ◽

...

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Mainland China ◽

Bayesian Maximum Entropy ◽

Time Cost ◽

Long Time ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Fixed Rank

Current reported spatiotemporal solutions for fusing multisensor aerosol optical depth (AOD) products used to recover gaps either suffer from unacceptable accuracy levels, i.e., fixed rank smooth (FRS), or high time costs, i.e., Bayesian maximum entropy (BME). This problem is generally more serious when dealing with multiple AOD products in a long time series or over large geographic areas. This study proposes a new, effective, and efficient enhanced FRS method (FRS-EE) to fuse satellite AOD products with uncertainty constraints. AOD products used in the fusion experiment include Moderate Resolution Imaging SpectroRadiometer (MODIS) DB/DT_DB_Combined AOD and Multiangle Imaging SpectroRadiometer (MISR) AOD across mainland China from 2016 to 2017. Results show that the average completeness of original, initial FRS fused, and FRS-EE fused AODs with uncertainty constraints are 22.80%, 95.18%, and 65.84%, respectively. Although the correlation coefficient (R = 0.77), root mean square error (RMSE = 0.30), and mean bias (Bias = 0.023) of the initial FRS fused AODs are relatively lower than those of original AODs compared to Aerosol Robotic Network (AERONET) AOD records, the accuracy of FRS-EE fused AODs, which are R = 0.88, RMSE = 0.20, and Bias = 0.022, is obviously improved. More importantly, in regions with fully missing original AODs, the accuracy of FRS-EE fused AODs is close to that of original AODs in regions with valid retrievals. Meanwhile, the time cost of FRS-EE for AOD fusion was only 2.91 h; obviously lower than the 30.46 months taken for BME.

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