Other papers pioneered the use of satellite aerosol products, specifically the MODIS DT product, for evaluating global climate model representation of aerosol characteristics [179–181]. As the years passed, it became more common to rely on the DT product among other satellite-derived aerosol products for observationally based estimates of direct aerosol effects and forcing, and to use these products with AERONET to evaluate and constrain different aspects of the global models we rely on to estimate future climate scenarios [172,174,182–193].

The MODIS DT aerosol products have influenced one aspect of aerosol in climate prediction that requires specific attention, and that is the insight into aerosol-cloud relationships and aerosol indirect effects. The first paper in this category that used the DT product was [125], which demonstrated the inverse relationship between smoke loading and cloud cover over the Amazon basin when AOD was at moderate to high levels, and then showed that the decrease in cloud cover created a positive radiative effect (warming) that easily overpowered the direct negative effect of the aerosol itself (cooling). Following [125], many studies made use of the MODIS DT aerosol product in conjunction with other products, observations, and modeling to identify, quantify, or interpret aerosol-cloud associations in these datasets [194–207]. Without modeling, identified associations between MODIS DT AOD and cloud or other parameters may suggest, but do not prove, a physical process. However, the observed relationships between variables provide a greater challenge for models to duplicate than does the challenge of representing each variable independently.

The studies cited above are far from an exhaustive list, and we chose to highlight these few on the basis of their pioneering contribution or because of their impact, as evidenced by their high number of citations. The MODIS DT aerosol products were part of a revolution of satellite observational data that helped to narrow the uncertainties on some aspects of climate change prediction, such as aerosol direct radiative effects and forcing. At the same time, these observations, especially documented relationships between different parameters, challenged the community to consider some climate processes in a new light. The MODIS DT aerosol products, and the studies that made use of these products made a contribution to climate change assessment and prediction. The Fourth Intergovernmental Panel on Climate Change (IPCC) Assessment (AR4) highlighted the product with its own two-panel color figure [208] and made multiple references to the product and the observationally based estimates of aerosol radiative effects and forcing associated with this product. AR4 was the first IPCC report to include a category of observation based on its chart of aerosol direct radiative forcing and cited three studies that had all used the MODIS DT aerosol products [172,174,177]. Six years later, with the Fifth IPCC Assessment (AR5) [209], the novelty of constraining total column AOD and clear-sky direct aerosol radiative forcing with global satellite datasets such as the MODIS DT product had dissipated. Instead, the report emphasized climate-relevant processes with an expanded section on aerosol-cloud processes, stating “satellite-based remote sensing continues to be the primary source of global data for aerosol-cloud interaction”, and then stating a concern about the fidelity of the satellite datasets in proximity to clouds. The section in the AR5 on aerosol-cloud interaction is supported by dozens of references to both modeling and observational studies. Included in those references are many that used the MODIS DT aerosol products to find associations between DT AOD and other parameters, as cited above.

## 7.2. Impact on Long-Range Particle Transport

One important area that the MODIS aerosol product advanced considerably is quantification and characterization of long-range particle transport. The inspiration to use the product quantitatively for this purpose came from [210], which showed the potential of using the DT satellite product to identify dust aerosol over ocean but relied on field data to quantify the dust deposition. The first paper to address this geophysical application quantitatively using the MODIS product estimated the amount of African dust that crossed the Atlantic ocean [44], suggesting that African dust brought a significant amount of nutrients to the Amazon basin. Following that paper, the product has been used multiple times to produce an independent observationally based estimate of intercontinental particle transport
Other papers pioneered the use of satellite aerosol products, specifically the MODIS DT product, for evaluating global climate model representation of aerosol characteristics [179–181]. As the years passed, it became more common to rely on the DT product among other satellite-derived aerosol products for observationally based estimates of direct aerosol effects and forcing, and to use these products with AERONET to evaluate and constrain different aspects of the global models we rely on to estimate future climate scenarios [172,174,182–193].

The MODIS DT aerosol products have influenced one aspect of aerosol in climate prediction that requires specific attention, and that is the insight into aerosol-cloud relationships and aerosol indirect effects. The first paper in this category that used the DT product was [125], which demonstrated the inverse relationship between smoke loading and cloud cover over the Amazon basin when AOD was at moderate to high levels, and then showed that the decrease in cloud cover created a positive radiative effect (warming) that easily overpowered the direct negative effect of the aerosol itself (cooling). Following [125], many studies made use of the MODIS DT aerosol product in conjunction with other products, observations, and modeling to identify, quantify, or interpret aerosol-cloud associations in these datasets [194–207]. Without modeling, identified associations between MODIS DT AOD and cloud or other parameters may suggest, but do not prove, a physical process. However, the observed relationships between variables provide a greater challenge for models to duplicate than does the challenge of representing each variable independently.

The studies cited above are far from an exhaustive list, and we chose to highlight these few on the basis of their pioneering contribution or because of their impact, as evidenced by their high number of citations. The MODIS DT aerosol products were part of a revolution of satellite observational data that helped to narrow the uncertainties on some aspects of climate change prediction, such as aerosol direct radiative effects and forcing. At the same time, these observations, especially documented relationships between different parameters, challenged the community to consider some climate processes in a new light. The MODIS DT aerosol products, and the studies that made use of these products made a contribution to climate change assessment and prediction. The Fourth Intergovernmental Panel on Climate Change (IPCC) Assessment (AR4) highlighted the product with its own two-panel color figure [208] and made multiple references to the product and the observationally based estimates of aerosol radiative effects and forcing associated with this product. AR4 was the first IPCC report to include a category of observation based on its chart of aerosol direct radiative forcing and cited three studies that had all used the MODIS DT aerosol products [172,174,177]. Six years later, with the Fifth IPCC Assessment (AR5) [209], the novelty of constraining total column AOD and clear-sky direct aerosol radiative forcing with global satellite datasets such as the MODIS DT product had dissipated. Instead, the report emphasized climate-relevant processes with an expanded section on aerosol-cloud processes, stating “satellite-based remote sensing continues to be the primary source of global data for aerosol-cloud interaction”, and then stating a concern about the fidelity of the satellite datasets in proximity to clouds. The section in the AR5 on aerosol-cloud interaction is supported by dozens of references to both modeling and observational studies. Included in those references are many that used the MODIS DT aerosol products to find associations between DT AOD and other parameters, as cited above.

## 7.2. Impact on Long-Range Particle Transport

One important area that the MODIS aerosol product advanced considerably is quantification and characterization of long-range particle transport. The inspiration to use the product quantitatively for this purpose came from [210], which showed the potential of using the DT satellite product to identify dust aerosol over ocean but relied on field data to quantify the dust deposition. The first paper to address this geophysical application quantitatively using the MODIS product estimated the amount of African dust that crossed the Atlantic ocean [44], suggesting that African dust brought a significant amount of nutrients to the Amazon basin. Following that paper, the product has been used multiple times to produce an independent observationally based estimate of intercontinental particle transport
and deposition [46,48,211–214]. The observationally based estimates provided an important constraint on modeled estimates [215–220] and were used to consider microbial transport on dust particles [221].

### 7.3. Impact on Air-Quality Monitoring and Mitigation

Although initially designed for climate applications, the concept of using the satellite-retrieved total column aerosol loading to infer the particle mass (PM) at ground level has been one of the most wide-spread applications involving the DT product. A bridge between AOD and PM on the ground is the total column mass loading, and this was first attempted by [54]. The first studies investigating the possibility of using MODIS-retrieved AOD for a true air-quality application were [128,129]. These papers were published within two weeks of each other. This was quickly followed by another pioneering study [222], leading to an effort to make the product available and usable to the operational air-quality forecast community [130]. In the 15 years since these early efforts, there has been an abundance of increasingly sophisticated attempts to make use of the MODIS DT and other aerosol products for air-quality monitoring, health impact assessment, and mitigation [223–240]. The cited papers are far from an exhaustive list, but they do represent a sample of highly cited studies that made use of the MODIS DT aerosol product in the field of air quality and exposure science.

### 7.4. Impact on Assimilation Systems

A key development in Earth system science that ran in parallel with the advancements in satellite remote sensing was the concept and implementation of global data assimilation systems [241]. These are modeling/observational hybrids that use an objective scheme to create physically consistent fields of different parameters in the Earth system. Originally, assimilation was the first step in numerical weather prediction, in which observations of meteorological variables such as wind, temperature, and humidity fields would be assimilated into 3D grids and used as input in a forecast model. As new Earth system parameters became available from satellite remote sensing, including aerosol, assimilation systems began to ingest these parameters in addition to the meteorological observations. The first system to ingest the MODIS DT aerosol product was the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) [131]. This was followed soon after by the European Center for Medium-Range Weather Forecasts (ECMWF) integrated forecast system [132]. In both of these cases, the operational AOD product had to be carefully screened to assure only the highest-quality product entered the system, but the studies reported that ingesting the MODIS DT product significantly improved the aerosol-forecasting ability of the models and reduced variance when comparing with ground truth [131]. Initially, only the DT AOD over ocean was used; however, after the second-generation DT product over land was made operational [18,19], the assimilation systems also began using the global land AOD [242]. Other examples using MODIS DT AOD in assimilation systems include [243–245]. The success with the MODIS DT AOD product was an encouraging first step, and assimilation systems have now matured to encompass a wide range of different geophysical observations including AOD from multiple satellites and products [246,247].

In many ways, the original goals in developing the DT aerosol products, which were to provide new insight into the global aerosol system and to help constrain estimates of climate forcing and change, are best served by assimilation systems which act to integrate many observations into a single system. The MODIS DT aerosol product played a significant role in advancing the capabilities of these important systems.

### 7.5. Impact on Aerosol Remote Sensing

In addition to having impact on Earth science and applications, the DT aerosol algorithm has been a major influence in the field of aerosol remote sensing. The innovations put into place to create a nearly global aerosol product, over ocean and land, have been copied, adapted, and improved upon by many other groups over the past 30 years. For example, the MODIS pre-launch algorithm for atmospheric correction over land [248] intended to use the at-launch MODIS DT aerosol product as
and deposition [46,48,211–214]. The observationally based estimates provided an important constraint on modeled estimates [215–220] and were used to consider microbial transport on dust particles [221].

### 7.3. Impact on Air-Quality Monitoring and Mitigation

Although initially designed for climate applications, the concept of using the satellite-retrieved total column aerosol loading to infer the particle mass (PM) at ground level has been one of the most wide-spread applications involving the DT product. A bridge between AOD and PM on the ground is the total column mass loading, and this was first attempted by [54]. The first studies investigating the possibility of using MODIS-retrieved AOD for a true air-quality application were [128,129]. These papers were published within two weeks of each other. This was quickly followed by another pioneering study [222], leading to an effort to make the product available and usable to the operational air-quality forecast community [130]. In the 15 years since these early efforts, there has been an abundance of increasingly sophisticated attempts to make use of the MODIS DT and other aerosol products for air-quality monitoring, health impact assessment, and mitigation [223–240]. The cited papers are far from an exhaustive list, but they do represent a sample of highly cited studies that made use of the MODIS DT aerosol product in the field of air quality and exposure science.

### 7.4. Impact on Assimilation Systems

A key development in Earth system science that ran in parallel with the advancements in satellite remote sensing was the concept and implementation of global data assimilation systems [241]. These are modeling/observational hybrids that use an objective scheme to create physically consistent fields of different parameters in the Earth system. Originally, assimilation was the first step in numerical weather prediction, in which observations of meteorological variables such as wind, temperature, and humidity fields would be assimilated into 3D grids and used as input in a forecast model. As new Earth system parameters became available from satellite remote sensing, including aerosol, assimilation systems began to ingest these parameters in addition to the meteorological observations. The first system to ingest the MODIS DT aerosol product was the Naval Research Laboratory (NRL) Atmospheric Variational Data Assimilation System (NAVDAS) [131]. This was followed soon after by the European Center for Medium-Range Weather Forecasts (ECMWF) integrated forecast system [132]. In both of these cases, the operational AOD product had to be carefully screened to assure only the highest-quality product entered the system, but the studies reported that ingesting the MODIS DT product significantly improved the aerosol-forecasting ability of the models and reduced variance when comparing with ground truth [131]. Initially, only the DT AOD over ocean was used; however, after the second-generation DT product over land was made operational [18,19], the assimilation systems also began using the global land AOD [242]. Other examples using MODIS DT AOD in assimilation systems include [243–245]. The success with the MODIS DT AOD product was an encouraging first step, and assimilation systems have now matured to encompass a wide range of different geophysical observations including AOD from multiple satellites and products [246,247].

In many ways, the original goals in developing the DT aerosol products, which were to provide new insight into the global aerosol system and to help constrain estimates of climate forcing and change, are best served by assimilation systems which act to integrate many observations into a single system. The MODIS DT aerosol product played a significant role in advancing the capabilities of these important systems.

### 7.5. Impact on Aerosol Remote Sensing

In addition to having impact on Earth science and applications, the DT aerosol algorithm has been a major influence in the field of aerosol remote sensing. The innovations put into place to create a nearly global aerosol product, over ocean and land, have been copied, adapted, and improved upon by many other groups over the past 30 years. For example, the MODIS pre-launch algorithm for atmospheric correction over land [248] intended to use the at-launch MODIS DT aerosol product as
input. Soon after launch, the atmospheric correction developers made a few adjustments to the AOD product, mostly creating a finer-resolution product needed by the land community [249]; however, the fundamental algorithm remained the same. By Collection 5, the atmospheric correction algorithm had added additional wavelengths to the aerosol retrieval (490 nm, 443 nm, 412 nm) and changed the wavelengths at which the surface reflectance ratios were calculated [250]. Eventually the MODIS atmospheric correction algorithm was ported to new sensors (e.g., Landsat-8 Operational Land Imager). At this stage, the aerosol retrieval part of the algorithm was still based on the original principles of [12], but the aerosol models, the wavelengths used in the inversion, and the surface reflectance ratios had all changed [251]. Most importantly, when the MODIS DT algorithm moved to the second generation over land [18,19], the MODIS atmospheric correction algorithm and its descendants did not; thus, the two algorithms now do not use the same inversion method. Nonetheless, the influence of the original MODIS DT algorithm on the atmospheric correction is apparent in that the atmospheric correction algorithm still assumes surface reflectance ratios to translate assumptions about the surface from wavelengths less sensitive to aerosol to wavelengths more sensitive, and then retrieve where the sensitivity is greatest.

Other algorithms can also trace their heritage back to the MODIS DT algorithm. For example, many algorithms and groups make use of the Dark Target concept over land, using relationships between visible and shortwave infrared bands to separate atmospheric and surface contributions to satellite-measured reflectance. These include the family of algorithms connected with the Along Track Scanning Radiometer (ATSR), Advanced ATSR (AATSR), and ATSR-2 [252,253] and derivatives from these original algorithms (e.g., [254]). Other over-land algorithms that follow the DT innovation for surface reflectance include the algorithms developed by the group at Yonsei University and applied to the Meteorological Imager (MI) [255] Geostationary Ocean Color Imager (GOCI) [256–258], Advanced Himawari Imager (AHI) [259], and others [260]. The NOAA aerosol algorithms over land, both the at-launch algorithm applied to VIIRS [261] and the Enterprise Processing System (EPS) applied to VIIRS and the Advanced Baseline Imagers [262], are Dark Target algorithms that have evolved from the original DT concept described in [12,60]. The Deep Blue (DB) aerosol algorithm [152], the DT’s sister algorithm on MODIS and VIIRS, turned into a DT-type land surface parameterization when the previously bright surface algorithm was adapted for vegetated surfaces [80,263]. Even the third aerosol algorithm on MODIS, the Multiangle Implementation of Atmospheric Correction (MAIAC) [264] makes use of assumed relationships between the shortwave infrared and the visible channels during its retrieval.

At least two algorithm families have adapted the DT ocean algorithm [13] in nearly its entirety. These are the MEdium Resolution Imaging Spectrometer (MERIS) Aerosol Load and Altitude from MERIS over Ocean (ALAMO) [265,266] and NOAA [261,262] algorithms. Other algorithms such as the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL)-POLarization and Directionality of Earth's Radiances (POLDER) algorithm were influenced by the DT ocean algorithm, incorporating a retrieval that mixes one fine mode and one coarse mode [267,268], but otherwise deviates from DT to make use of the multiangle polarization of POLDER.

We see the influence of DT innovations across the aerosol remote sensing community. Many algorithms followed the DT lead in turning to total column inversions from AERONET to define their aerosol models [257,260,261,269], but others continued to use other databases derived from in situ measurements [254]. The 3 × 3 spatial variability cloud mask [122] and the subpixel snow detection [127] have made their way into subsequent algorithms [257,260,270]. The fact is that the innovations that the DT algorithm brought to aerosol remote sensing are now implemented so widely that they are now standard practice across the community.
input. Soon after launch, the atmospheric correction developers made a few adjustments to the AOD product, mostly creating a finer-resolution product needed by the land community [249]; however, the fundamental algorithm remained the same. By Collection 5, the atmospheric correction algorithm had added additional wavelengths to the aerosol retrieval (490 nm, 443 nm, 412 nm) and changed the wavelengths at which the surface reflectance ratios were calculated [250]. Eventually the MODIS atmospheric correction algorithm was ported to new sensors (e.g., Landsat-8 Operational Land Imager). At this stage, the aerosol retrieval part of the algorithm was still based on the original principles of [12], but the aerosol models, the wavelengths used in the inversion, and the surface reflectance ratios had all changed [251]. Most importantly, when the MODIS DT algorithm moved to the second generation over land [18,19], the MODIS atmospheric correction algorithm and its descendants did not; thus, the two algorithms now do not use the same inversion method. Nonetheless, the influence of the original MODIS DT algorithm on the atmospheric correction is apparent in that the atmospheric correction algorithm still assumes surface reflectance ratios to translate assumptions about the surface from wavelengths less sensitive to aerosol to wavelengths more sensitive, and then retrieve where the sensitivity is greatest.

Other algorithms can also trace their heritage back to the MODIS DT algorithm. For example, many algorithms and groups make use of the Dark Target concept over land, using relationships between visible and shortwave infrared bands to separate atmospheric and surface contributions to satellite-measured reflectance. These include the family of algorithms connected with the Along Track Scanning Radiometer (ATSR), Advanced ATSR (AATSR), and ATSR-2 [252,253] and derivatives from these original algorithms (e.g., [254]). Other over-land algorithms that follow the DT innovation for surface reflectance include the algorithms developed by the group at Yonsei University and applied to the Meteorological Imager (MI) [255] Geostationary Ocean Color Imager (GOCI) [256–258], Advanced Himawari Imager (AHI) [259], and others [260]. The NOAA aerosol algorithms over land, both the at-launch algorithm applied to VIIRS [261] and the Enterprise Processing System (EPS) applied to VIIRS and the Advanced Baseline Imagers [262], are Dark Target algorithms that have evolved from the original DT concept described in [12,60]. The Deep Blue (DB) aerosol algorithm [152], the DT’s sister algorithm on MODIS and VIIRS, turned into a DT-type land surface parameterization when the previously bright surface algorithm was adapted for vegetated surfaces [80,263]. Even the third aerosol algorithm on MODIS, the Multiangle Implementation of Atmospheric Correction (MAIAC) [264] makes use of assumed relationships between the shortwave infrared and the visible channels during its retrieval.

At least two algorithm families have adapted the DT ocean algorithm [13] in nearly its entirety. These are the MEdium Resolution Imaging Spectrometer (MERIS) Aerosol Load and Altitude from MERIS over Ocean (ALAMO) [265,266] and NOAA [261,262] algorithms. Other algorithms such as the Polarization and Anisotropy of Reflectances for Atmospheric Science coupled with Observations from a Lidar (PARASOL)-POLarization and Directionality of Earth's Radiances (POLDER) algorithm were influenced by the DT ocean algorithm, incorporating a retrieval that mixes one fine mode and one coarse mode [267,268], but otherwise deviates from DT to make use of the multiangle polarization of POLDER.

We see the influence of DT innovations across the aerosol remote sensing community. Many algorithms followed the DT lead in turning to total column inversions from AERONET to define their aerosol models [257,260,261,269], but others continued to use other databases derived from in situ measurements [254]. The 3 × 3 spatial variability cloud mask [122] and the subpixel snow detection [127] have made their way into subsequent algorithms [257,260,270]. The fact is that the innovations that the DT algorithm brought to aerosol remote sensing are now implemented so widely that they are now standard practice across the community.
## 8. Discussion

After 30 years, the DT algorithm is well on its path forward, expanding to other sensors beyond MODIS. Currently a version of DT is running operationally using VIIRS observations as inputs [24,148] and a prototype DT algorithm has been applied to the Advanced Himawari Imager (AHI) [149] and the two Advanced Baseline Imagers (ABIs) currently in geosynchronous orbit on the Himawari, GOES-E, and GOES-W satellites [271]. The goal is to provide aerosol data users with a comprehensive view of the global aerosol system, combining the global coverage of polar-orbiting satellites in low Earth orbit (LEO) with the high temporal coverage of sensors in geosynchronous orbit (GEO). Using the same basic algorithm across all sensors and platforms provides a necessary level of consistency for global studies. Even so, differences in sensor characteristics including wavelengths, spatial and temporal resolution, geometries, and unresolved calibration offsets make the task challenging. The DT team intends to homogenize these differences as much as possible and present the data on a common moderate spatial and temporal resolution grid. Figure 13 demonstrates how a DT merged GEO/LEO view of the global aerosol system might look at a single point in time. The combination of DT retrievals from all of these sensors smoothly fills in holes left by any individual sensor. Such spatial coverage will repeat at every time step, likely to be at minimum every 30 min.

<figure><img src="image_17.png" /><figcaption>**Figure 13.** Aerosol optical depth at 0.55 µm, retrieved using the Collection 6.1 aerosol algorithm with inputs from Advanced Baseline Imagers (ABIs) on GOES-E and GOES-W, Advanced Himawari Imager (AHI) on Himawari-8, MODIS on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership, and the aggregated combination of the retrievals from all six sensors onto a common 0.1° by 0.1° spatial grid at 11:15 p.m. UTC ± 15 min on 3 December 2018.</figcaption></figure>

The Dark Target algorithm can also be applied to more advanced multiangle polarimeters that have hyperspectral or multiband wavelengths across the same range as MODIS. Examples of such sensors include the Polarization and Directionality of Earth Reflectances (POLDER) [268] and the radiometer and polarimeters of the Plankton, Aerosol, Clouds, Ocean Ecosystems (PACE) mission [272].
## 8. Discussion

After 30 years, the DT algorithm is well on its path forward, expanding to other sensors beyond MODIS. Currently a version of DT is running operationally using VIIRS observations as inputs [24,148] and a prototype DT algorithm has been applied to the Advanced Himawari Imager (AHI) [149] and the two Advanced Baseline Imagers (ABIs) currently in geosynchronous orbit on the Himawari, GOES-E, and GOES-W satellites [271]. The goal is to provide aerosol data users with a comprehensive view of the global aerosol system, combining the global coverage of polar-orbiting satellites in low Earth orbit (LEO) with the high temporal coverage of sensors in geosynchronous orbit (GEO). Using the same basic algorithm across all sensors and platforms provides a necessary level of consistency for global studies. Even so, differences in sensor characteristics including wavelengths, spatial and temporal resolution, geometries, and unresolved calibration offsets make the task challenging. The DT team intends to homogenize these differences as much as possible and present the data on a common moderate spatial and temporal resolution grid. Figure 13 demonstrates how a DT merged GEO/LEO view of the global aerosol system might look at a single point in time. The combination of DT retrievals from all of these sensors smoothly fills in holes left by any individual sensor. Such spatial coverage will repeat at every time step, likely to be at minimum every 30 min.

<figure><img src="image_16.png" /><figcaption>**Figure 13.** Aerosol optical depth at 0.55 µm, retrieved using the Collection 6.1 aerosol algorithm with inputs from Advanced Baseline Imagers (ABIs) on GOES-E and GOES-W, Advanced Himawari Imager (AHI) on Himawari-8, MODIS on Terra and Aqua, and Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership, and the aggregated combination of the retrievals from all six sensors onto a common 0.1° by 0.1° spatial grid at 11:15 p.m. UTC ± 15 min on 3 December 2018.</figcaption></figure>

The Dark Target algorithm can also be applied to more advanced multiangle polarimeters that have hyperspectral or multiband wavelengths across the same range as MODIS. Examples of such sensors include the Polarization and Directionality of Earth Reflectances (POLDER) [268] and the radiometer and polarimeters of the Plankton, Aerosol, Clouds, Ocean Ecosystems (PACE) mission [272].
This provides a well-understood baseline for global statistics of aerosol parameters and an opportunity to continue and maintain long-term monitoring of the aerosol system into the coming decades.

## 9. Conclusions

The Dark Target aerosol algorithm produced satellite-derived products that changed our view of the global aerosol system, enabled innovative scientific studies, contributed to new ways of monitoring and forecasting air quality, and influenced the field of aerosol remote sensing. By all accounts, the DT algorithm has been one of NASA’s important contributions to Earth science and applications. Looking back now, over three decades, we can pinpoint the features of the algorithm and the environment in which it was developed, produced, and disseminated that enabled its widespread acceptance and integration into various user communities.

1. The algorithm is based on physical understanding of aerosols, as well as their environment and radiative transfer. The algorithms are not mathematical inversions in the purest sense but are tuned to work within the realities of the physical world. Nevertheless, the algorithm retains as much flexibility as possible, given the information content of multiwavelength sensors.

2. Extensive pre- and postlaunch field experiments with multiple types of measurements including simulations of the future sensor, validation for proto-algorithms, and characterization of parameters that would become assumptions in the algorithm were essential elements contributing to the DT success.

3. Concurrent development of a robust ground validation program, involving AERONET, MAN, and the MAPSS validation system that automatically linked the ground truth with the satellite products was the single most critical element of success. Characterizing the uncertainty of the aerosol products provided assurance to potential users.

4. Having an "at-launch" algorithm ready to go on day 1 of the satellite mission required an investment of a decade of development time. It also required an algorithm that could function "on the fly" and not wait for an accumulation of statistics to constrain assumptions, such as a database of land surface reflectances.

5. The concept of data collections allowed state-of-the-science algorithms to produce stable time series. This kept the algorithms and calibration fresh and relevant, while guaranteeing consistency to user communities.

6. Investment in easily accessible visualization, analysis, and dissemination tools lowered the barrier of entry for users of all levels of ability. Providing the data in 1° × 1° gridded Level 3 arrays was key to enticing the global modeling community to use the data in their studies, while providing the 10 km and later the 3 km Level 2 data along the orbital swaths attracted the air quality community. Having a simple, functional data dissemination service, responsive to users, was another important element of success.

7. The products were delivered at multiple levels of difficulty to satisfy multiple needs. The goal was to maintain simplicity in the products, while not sacrificing the quality science behind the algorithm or the variety of retrieved parameters necessary for sophisticated analysis.

8. Continued investment in maintenance of the algorithm was essential, to keep the algorithm relevant as user needs evolved, particularly as the sensors degraded.

We note that many of the elements contributing to the success of the DT algorithm and products lay beyond the scope of the DT team. NASA’s investment in AERONET, field experiments, reprocessing, a user-friendly data infrastructure, and a long-term commitment to maintaining and improving the algorithm were equally (or more) important than the accomplishments of the team itself.

Now looking forward, we have an opportunity to take these “lessons learned” and apply them to a constellation of new opportunities. The DT algorithm has already been ported to the polar-orbiting VIIRS sensor and to the geosynchronous AHI sensor. Each time the algorithm is applied to a new sensor, we face new challenges. Both VIIRS and AHI introduced new viewing geometries, and they
This provides a well-understood baseline for global statistics of aerosol parameters and an opportunity to continue and maintain long-term monitoring of the aerosol system into the coming decades.

## 9. Conclusions

The Dark Target aerosol algorithm produced satellite-derived products that changed our view of the global aerosol system, enabled innovative scientific studies, contributed to new ways of monitoring and forecasting air quality, and influenced the field of aerosol remote sensing. By all accounts, the DT algorithm has been one of NASA’s important contributions to Earth science and applications. Looking back now, over three decades, we can pinpoint the features of the algorithm and the environment in which it was developed, produced, and disseminated that enabled its widespread acceptance and integration into various user communities.

1. The algorithm is based on physical understanding of aerosols, as well as their environment and radiative transfer. The algorithms are not mathematical inversions in the purest sense but are tuned to work within the realities of the physical world. Nevertheless, the algorithm retains as much flexibility as possible, given the information content of multiwavelength sensors.

2. Extensive pre- and postlaunch field experiments with multiple types of measurements including simulations of the future sensor, validation for proto-algorithms, and characterization of parameters that would become assumptions in the algorithm were essential elements contributing to the DT success.

3. Concurrent development of a robust ground validation program, involving AERONET, MAN, and the MAPSS validation system that automatically linked the ground truth with the satellite products was the single most critical element of success. Characterizing the uncertainty of the aerosol products provided assurance to potential users.

4. Having an "at-launch" algorithm ready to go on day 1 of the satellite mission required an investment of a decade of development time. It also required an algorithm that could function "on the fly" and not wait for an accumulation of statistics to constrain assumptions, such as a database of land surface reflectances.

5. The concept of data collections allowed state-of-the-science algorithms to produce stable time series. This kept the algorithms and calibration fresh and relevant, while guaranteeing consistency to user communities.

6. Investment in easily accessible visualization, analysis, and dissemination tools lowered the barrier of entry for users of all levels of ability. Providing the data in 1° × 1° gridded Level 3 arrays was key to enticing the global modeling community to use the data in their studies, while providing the 10 km and later the 3 km Level 2 data along the orbital swaths attracted the air quality community. Having a simple, functional data dissemination service, responsive to users, was another important element of success.

7. The products were delivered at multiple levels of difficulty to satisfy multiple needs. The goal was to maintain simplicity in the products, while not sacrificing the quality science behind the algorithm or the variety of retrieved parameters necessary for sophisticated analysis.

8. Continued investment in maintenance of the algorithm was essential, to keep the algorithm relevant as user needs evolved, particularly as the sensors degraded.

We note that many of the elements contributing to the success of the DT algorithm and products lay beyond the scope of the DT team. NASA’s investment in AERONET, field experiments, reprocessing, a user-friendly data infrastructure, and a long-term commitment to maintaining and improving the algorithm were equally (or more) important than the accomplishments of the team itself.

Now looking forward, we have an opportunity to take these “lessons learned” and apply them to a constellation of new opportunities. The DT algorithm has already been ported to the polar-orbiting VIIRS sensor and to the geosynchronous AHI sensor. Each time the algorithm is applied to a new sensor, we face new challenges. Both VIIRS and AHI introduced new viewing geometries, and they
required adjustments to gaseous absorption and to specific aspects of the traditional algorithm because
of missing wavelengths. However, the future of the DT algorithm and of operational satellite remote
sensing in general is the unification of multiple sensors aboard many satellite platforms in a variety of
orbits. The best way to move forward toward this unification is to apply a single algorithm or family
of algorithms across all the sensors and platforms, managing consistency in the process. In this way,
the attempt of characterizing the global aerosol system that began over 30 years ago will grow closer
to reality.

**Author Contributions:** Conceptualization, L.A.R., R.C.L., S.M., and D.T.; software, S.M., Y.S., V.S., and L.A.M.; validation, Y.S., V.S., L.A.M., C.I., R.C.L., R.G.K., and B.N.H.; formal analysis, P.G., Y.S., V.S., Y.Z., M.K., F.P., and R.-R.L.; writing—original draft preparation, L.A.R.; writing—review and editing, D.T., C.I., L.A.M., V.S., Y.S., Y.Z., R.C.L., S.G., S.M., R-R.L., M.K., F.P., P.G., R.G.K., and B.N.H.; visualization, V.S., Y.S., P.G., M.K., R-R.L., R.K., and R.C.L.; supervision, R.C.L. and S.M.; project administration, R.C.L.; funding acquisition, R.C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by “NASA Earth Science Senior Review 2017 MODIS algorithm maintenance” and “NASA Making Earth System Data Records for Use in Research Environments (NNH17ZDA001N-MEASURES)”. C.I. is also grateful for partial support received during the preparation of this article from the Educational Partnership Program of the National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce, under Agreement No. #NA16SEC4810006.

**Acknowledgments:** The authors acknowledge the many individuals who have contributed to the Dark Target algorithm but who are too many to list by name. These include all the members of the AERONET team, all the individual AERONET investigators, and all the site managers who together have created an indispensable tool for studying the global aerosol system. A special acknowledgement to D. Allen Chu who contributed significant hands-on work during the first two decades of the Dark Target journey. We also want to acknowledge the leadership within the MODIS Science Team and at NASA Earth Science for investing in the concept of Earth System Science, in which the global aerosol system plays a role. Finally, we remember Yoram J. Kaufman who conceived the algorithm and led this team for 15 years and, by doing so, changed our understanding of the global aerosol system.

**Conflicts of Interest:** The authors declare no conflict of interest.

**References**

1. Kaufman, Y.; Tanré, D.; Boucher, O. A satellite view of aerosols in the climate system. *Nature* **2002**, *419*, 215–223. [CrossRef]

2. Tanré, D.; Kaufman, Y.; Nakajima, T.; Ramanathan, V. Preface to special section on Global Aerosol System. *J. Geophys. Res.* **2005**, *110*, D10S01. [CrossRef]

3. Stier, P.; Feichter, J.; Roeckner, E.; Kloster, S.; Esch, M. The evolution of the global aerosol system in a transient climate simulation from 1860 to 2100. *Atmos. Chem. Phys.* **2006**, *6*, 3059–3076. [CrossRef]

4. Lawton, J. Earth System Science. *Science* **2001**, *292*, 1965. [CrossRef]

5. Bowdle, D.A.; Rothermel, J.; Arnold, J.E.; Williams, S.F. GLObal Backscatter Experiment (GLOBE) Pacific survey mission. In *4th Airborne Geoscience Workshop*; NASA: Washington, WA, USA, 1991; pp. 107–110.

6. Griggs, M. Measurements of Atmospheric Aerosol Optical Thickness over Water Using ERTS-1 Data. *J. Air Poll. Control. Assoc.* **1975**, *25*, 626. [CrossRef] [PubMed]

7. Fraser, R.S. Satellite measurement of mass of Sahara dust in the atmosphere. *Appl. Opt.* **1976**, *15*, 2471–2479. [CrossRef]

8. Mekler, Y.; Quenzel, H.; Ohring, G.; Marcus, I. Relative atmospheric aerosol content from ERTS observations. *J. Geophys. Res.* **1977**, *82*, 967–972. [CrossRef]

9. Stowe, L.L.; Carey, R.M.; Pellegrino, P.P. Monitoring the Mt. Pinatubo aerosol layer with NOAA/11 AVHRR data. *Geophys. Res. Lett.* **1992**, *19*, 159–162. [CrossRef]

10. Husar, R.B.; Stowe, L.L.; Prospero, J.M. Characterization of tropospheric aerosols over the oceans with the NOAA Advanced Very High Resolution Radiometer optical thickness operational product. *J. Geophys. Res.* **1997**, *102*, 16889–16910. [CrossRef]

11. Tilford, S.G.; Asrar, G.; Backlund, P.W. Mission to planet earth. *Adv. Space Res.* **1994**, *14*, 5–9. [CrossRef]

12. Kaufman, Y.J.; Tanré, D.; Remer, L.A.; Vermote, E.; Chu, A.; Holben, B.N. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. *J. Geophys. Res.* **1997**, *102*, 17051–17067. [CrossRef]
required adjustments to gaseous absorption and to specific aspects of the traditional algorithm because
of missing wavelengths. However, the future of the DT algorithm and of operational satellite remote
sensing in general is the unification of multiple sensors aboard many satellite platforms in a variety of
orbits. The best way to move forward toward this unification is to apply a single algorithm or family
of algorithms across all the sensors and platforms, managing consistency in the process. In this way,
the attempt of characterizing the global aerosol system that began over 30 years ago will grow closer
to reality.

**Author Contributions:** Conceptualization, L.A.R., R.C.L., S.M., and D.T.; software, S.M., Y.S., V.S., and L.A.M.; validation, Y.S., V.S., L.A.M., C.I., R.C.L., R.G.K., and B.N.H.; formal analysis, P.G., Y.S., V.S., Y.Z., M.K., F.P., and R.-R.L.; writing—original draft preparation, L.A.R.; writing—review and editing, D.T., C.I., L.A.M., V.S., Y.S., Y.Z., R.C.L., S.G., S.M., R-R.L., M.K., F.P., P.G., R.G.K., and B.N.H.; visualization, V.S., Y.S., P.G., M.K., R-R.L., R.K., and R.C.L.; supervision, R.C.L. and S.M.; project administration, R.C.L.; funding acquisition, R.C.L. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by “NASA Earth Science Senior Review 2017 MODIS algorithm maintenance” and “NASA Making Earth System Data Records for Use in Research Environments (NNH17ZDA001N-MEASURES)”. C.I. is also grateful for partial support received during the preparation of this article from the Educational Partnership Program of the National Oceanic and Atmospheric Administration (NOAA), US Department of Commerce, under Agreement No. #NA16SEC4810006.

**Acknowledgments:** The authors acknowledge the many individuals who have contributed to the Dark Target algorithm but who are too many to list by name. These include all the members of the AERONET team, all the individual AERONET investigators, and all the site managers who together have created an indispensable tool for studying the global aerosol system. A special acknowledgement to D. Allen Chu who contributed significant hands-on work during the first two decades of the Dark Target journey. We also want to acknowledge the leadership within the MODIS Science Team and at NASA Earth Science for investing in the concept of Earth System Science, in which the global aerosol system plays a role. Finally, we remember Yoram J. Kaufman who conceived the algorithm and led this team for 15 years and, by doing so, changed our understanding of the global aerosol system.

**Conflicts of Interest:** The authors declare no conflict of interest.

References

1. Kaufman, Y.; Tanré, D.; Boucher, O. A satellite view of aerosols in the climate system. *Nature* **2002**, *419*, 215–223. [CrossRef]

2. Tanré, D.; Kaufman, Y.; Nakajima, T.; Ramanathan, V. Preface to special section on Global Aerosol System. *J. Geophys. Res.* **2005**, *110*, D10S01. [CrossRef]

3. Stier, P.; Feichter, J.; Roeckner, E.; Kloster, S.; Esch, M. The evolution of the global aerosol system in a transient climate simulation from 1860 to 2100. *Atmos. Chem. Phys.* **2006**, *6*, 3059–3076. [CrossRef]

4. Lawton, J. Earth System Science. *Science* **2001**, *292*, 1965. [CrossRef]

5. Bowdle, D.A.; Rothermel, J.; Arnold, J.E.; Williams, S.F. GLObal Backscatter Experiment (GLOBE) Pacific survey mission. In *4th Airborne Geoscience Workshop*; NASA: Washington, WA, USA, 1991; pp. 107–110.

6. Griggs, M. Measurements of Atmospheric Aerosol Optical Thickness over Water Using ERTS-1 Data. *J. Air Poll. Control. Assoc.* **1975**, *25*, 626. [CrossRef] [PubMed]

7. Fraser, R.S. Satellite measurement of mass of Sahara dust in the atmosphere. *Appl. Opt.* **1976**, *15*, 2471–2479. [CrossRef]

8. Mekler, Y.; Quenzel, H.; Ohring, G.; Marcus, I. Relative atmospheric aerosol content from ERTS observations. *J. Geophys. Res.* **1977**, *82*, 967–972. [CrossRef]

9. Stowe, L.L.; Carey, R.M.; Pellegrino, P.P. Monitoring the Mt. Pinatubo aerosol layer with NOAA/11 AVHRR data. *Geophys. Res. Lett.* **1992**, *19*, 159–162. [CrossRef]

10. Husar, R.B.; Stowe, L.L.; Prospero, J.M. Characterization of tropospheric aerosols over the oceans with the NOAA Advanced Very High Resolution Radiometer optical thickness operational product. *J. Geophys. Res.* **1997**, *102*, 16889–16910. [CrossRef]

11. Tilford, S.G.; Asrar, G.; Backlund, P.W. Mission to planet earth. *Adv. Space Res.* **1994**, *14*, 5–9. [CrossRef]

12. Kaufman, Y.J.; Tanré, D.; Remer, L.A.; Vermote, E.; Chu, A.; Holben, B.N. Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer. *J. Geophys. Res.* **1997**, *102*, 17051–17067. [CrossRef]
13. Tanré, D.; Kaufman, Y.J.; Herman, M.; Mattoo, S. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. *J. Geophys. Res.* **1997**, *102*, 16971–16988. [CrossRef]

14. Holben, B.N.; Eck, T.F.; Slutsker, I.; Tanre, D.; Buis, J.P.; Setzer, A.; Vermote, E.; Reagan, J.A.; Kaufman, Y.; Nakajima, T.; et al. AERONET—A federated instrument network and data archive for aerosol characterization. *Rem. Sens. Environ.* **1998**, *66*, 1–16. [CrossRef]

15. Levy, R.C.; Remer, L.A.; Kleidman, R.G.; Mattoo, S.; Ichoku, C.; Kahn, R.; Eck, T.F. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. *Atmos. Chem. Phys.* **2010**, *10*, 10399–10420. [CrossRef]

16. Remer, L.A.; Kaufman, Y.J.; Tanré, D.; Mattoo, S.; Chu, D.A.; Martins, J.V.; Li, R.-R.; Ichoku, C.; Levy, R.C.; Kleidman, R.G.; et al. The MODIS aerosol algorithm, products and validation. *J. Atmos. Sci.* **2005**, *62*, 947–973. [CrossRef]

17. Remer, L.A.; Mattoo, S.; Levy, R.C.; Munchak, L.A. MODIS 3 km aerosol product: Algorithm and global perspective. *Atmos. Meas. Tech.* **2013**, *6*, 1829–1844. [CrossRef]

18. Levy, R.C.; Remer, L.A.; Dubovik, O. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. *J. Geophys. Res.* **2007**, *112*, D13210. [CrossRef]

19. Levy, R.C.; Remer, L.A.; Mattoo, S.; Vermote, E.F.; Kaufman, Y.J. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. *J. Geophys. Res.* **2007**, *112*, D13211. [CrossRef]

20. Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. *Atmos. Meas. Tech.* **2013**, *6*, 2989–3034. [CrossRef]

21. Levy, R.C.; Mattoo, S.; Sawyer, V.; Shi, Y.; Colarco, P.R.; Lyapustin, A.I.; Wang, Y.; Remer, L.A. Exploring systematic offsets between aerosol products from the two MODIS sensors. *Atmos. Meas. Tech.* **2018**, *11*, 4073–4092. [CrossRef]

22. Gupta, P.; Levy, R.C.; Mattoo, S.; Remer, L.A.; Munchak, L.A. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm. *Atmos. Meas. Tech.* **2016**, *9*, 3293–3308. [CrossRef]

23. Patadia, F.; Levy, R.C.; Mattoo, S. Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation. *Atmos. Meas. Tech.* **2018**, *11*, 3205–3219. [CrossRef]

24. Sawyer, V.; Levy, R.; Mattoo, S.; Cureton, G.; Shi, Y.; Remer, L.A. Continuing the MODIS Dark Target aerosol time series with VIIRS. *Remote Sens.* **2020**, *12*, 308. [CrossRef]

25. Rao, C.R.N.; Stowe, L.L.; McClain, E.P. Remote sensing of aerosols over the oceans using AVHRR data Theory, practice and applications. *Int. J. Rem. Sens.* **1989**, *10*, 743–749. [CrossRef]

26. Stowe, L.L.; Ignatov, A.M.; Singh, R.R. Development, validation, and potential enhancements to the second-generation operational aerosol product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration. *J. Geophys. Res. Atmos.* **1997**, *102*, 16923–16934. [CrossRef]

27. Long, C.S.; Stowe, L.L. Using the NOAA/AVHRR to study stratospheric aerosol optical thicknesses following the Mt. Pinatubo eruption. *Geophys. Res. Lett.* **1994**, *21*, 2215–2218. [CrossRef]

28. Ignatov, A.; Stowe, L.; Singh, R. Sensitivity study of the Angstrom exponent derived from AVHRR over oceans. *Adv. Space Res.* **1998**, *21*, 439–442. [CrossRef]

29. Durkee, P.A.; Pfeil, F.; Frost, E.; Shema, R. Global analysis of aerosol particle characteristics. *Atmos. Environ.* *Part A Gen. Topics* **1991**, *25*, 2457–2471. [CrossRef]

30. Nakajima, T.; Higurashi, A. A use of two-channel radiances for an aerosol characterization from space. *Geophys. Res. Lett.* **1998**, *25*, 3815–3818. [CrossRef]

31. Higurashi, A.; Nakajima, T. Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR. *J. Atmos. Sci.* **1999**, *56*, 924–941. [CrossRef]

32. Mishchenko, M.I.; Geogdzhayev, I.V.; Cairns, B.; Rossow, W.B.; Lacis, A.A. Aerosol retrievals over the ocean by use of channels 1 and 2 AVHRR data: Sensitivity analysis and preliminary results. *Appl. Opt.* **1999**, *38*, 7325–7341. [CrossRef] [PubMed]

33. Geogdzhayev, I.V.; Mishchenko, M.I.; Rossow, W.B.; Cairns, B.; Lacis, A.A. Global Two-Channel AVHRR Retrievals of Aerosol Properties over the Ocean for the Period of NOAA-9 Observations and Preliminary Retrievals Using NOAA-7 and NOAA-11 Data. *J. Atmos. Sci.* **2002**, *59*, 262–278. [CrossRef]
13. Tanré, D.; Kaufman, Y.J.; Herman, M.; Mattoo, S. Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. *J. Geophys. Res.* **1997**, *102*, 16971–16988. [CrossRef]

14. Holben, B.N.; Eck, T.F.; Slutsker, I.; Tanre, D.; Buis, J.P.; Setzer, A.; Vermote, E.; Reagan, J.A.; Kaufman, Y.; Nakajima, T.; et al. AERONET—A federated instrument network and data archive for aerosol characterization. *Rem. Sens. Environ.* **1998**, *66*, 1–16. [CrossRef]

15. Levy, R.C.; Remer, L.A.; Kleidman, R.G.; Mattoo, S.; Ichoku, C.; Kahn, R.; Eck, T.F. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. *Atmos. Chem. Phys.* **2010**, *10*, 10399–10420. [CrossRef]

16. Remer, L.A.; Kaufman, Y.J.; Tanré, D.; Mattoo, S.; Chu, D.A.; Martins, J.V.; Li, R.-R.; Ichoku, C.; Levy, R.C.; Kleidman, R.G.; et al. The MODIS aerosol algorithm, products and validation. *J. Atmos. Sci.* **2005**, *62*, 947–973. [CrossRef]

17. Remer, L.A.; Mattoo, S.; Levy, R.C.; Munchak, L.A. MODIS 3 km aerosol product: Algorithm and global perspective. *Atmos. Meas. Tech.* **2013**, *6*, 1829–1844. [CrossRef]

18. Levy, R.C.; Remer, L.A.; Dubovik, O. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land. *J. Geophys. Res.* **2007**, *112*, D13210. [CrossRef]

19. Levy, R.C.; Remer, L.A.; Mattoo, S.; Vermote, E.F.; Kaufman, Y.J. Second-generation operational algorithm: Retrieval of aerosol properties over land from inversion of Moderate Resolution Imaging Spectroradiometer spectral reflectance. *J. Geophys. Res.* **2007**, *112*, D13211. [CrossRef]

20. Levy, R.C.; Mattoo, S.; Munchak, L.A.; Remer, L.A.; Sayer, A.M.; Patadia, F.; Hsu, N.C. The Collection 6 MODIS aerosol products over land and ocean. *Atmos. Meas. Tech.* **2013**, *6*, 2989–3034. [CrossRef]

21. Levy, R.C.; Mattoo, S.; Sawyer, V.; Shi, Y.; Colarco, P.R.; Lyapustin, A.I.; Wang, Y.; Remer, L.A. Exploring systematic offsets between aerosol products from the two MODIS sensors. *Atmos. Meas. Tech.* **2018**, *11*, 4073–4092. [CrossRef]

22. Gupta, P.; Levy, R.C.; Mattoo, S.; Remer, L.A.; Munchak, L.A. A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm. *Atmos. Meas. Tech.* **2016**, *9*, 3293–3308. [CrossRef]

23. Patadia, F.; Levy, R.C.; Mattoo, S. Correcting for trace gas absorption when retrieving aerosol optical depth from satellite observations of reflected shortwave radiation. *Atmos. Meas. Tech.* **2018**, *11*, 3205–3219. [CrossRef]

24. Sawyer, V.; Levy, R.; Mattoo, S.; Cureton, G.; Shi, Y.; Remer, L.A. Continuing the MODIS Dark Target aerosol time series with VIIRS. *Remote Sens.* **2020**, *12*, 308. [CrossRef]

25. Rao, C.R.N.; Stowe, L.L.; McClain, E.P. Remote sensing of aerosols over the oceans using AVHRR data Theory, practice and applications. *Int. J. Rem. Sens.* **1989**, *10*, 743–749. [CrossRef]

26. Stowe, L.L.; Ignatov, A.M.; Singh, R.R. Development, validation, and potential enhancements to the second-generation operational aerosol product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration. *J. Geophys. Res. Atmos.* **1997**, *102*, 16923–16934. [CrossRef]

27. Long, C.S.; Stowe, L.L. Using the NOAA/AVHRR to study stratospheric aerosol optical thicknesses following the Mt. Pinatubo eruption. *Geophys. Res. Lett.* **1994**, *21*, 2215–2218. [CrossRef]

28. Ignatov, A.; Stowe, L.; Singh, R. Sensitivity study of the Angstrom exponent derived from AVHRR over oceans. *Adv. Space Res.* **1998**, *21*, 439–442. [CrossRef]

29. Durkee, P.A.; Pfeil, F.; Frost, E.; Shema, R. Global analysis of aerosol particle characteristics. *Atmos. Environ.* *Part A Gen. Topics* **1991**, *25*, 2457–2471. [CrossRef]

30. Nakajima, T.; Higurashi, A. A use of two-channel radiances for an aerosol characterization from space. *Geophys. Res. Lett.* **1998**, *25*, 3815–3818. [CrossRef]

31. Higurashi, A.; Nakajima, T. Development of a Two-Channel Aerosol Retrieval Algorithm on a Global Scale Using NOAA AVHRR. *J. Atmos. Sci.* **1999**, *56*, 924–941. [CrossRef]

32. Mishchenko, M.I.; Geogdzhayev, I.V.; Cairns, B.; Rossow, W.B.; Lacis, A.A. Aerosol retrievals over the ocean by use of channels 1 and 2 AVHRR data: Sensitivity analysis and preliminary results. *Appl. Opt.* **1999**, *38*, 7325–7341. [CrossRef] [PubMed]

33. Geogdzhayev, I.V.; Mishchenko, M.I.; Rossow, W.B.; Cairns, B.; Lacis, A.A. Global Two-Channel AVHRR Retrievals of Aerosol Properties over the Ocean for the Period of NOAA-9 Observations and Preliminary Retrievals Using NOAA-7 and NOAA-11 Data. *J. Atmos. Sci.* **2002**, *59*, 262–278. [CrossRef]
34. Ignatov, A.; Sapper, J.; Cox, S.; Laszlo, I.; Nalli, N.R.; Kidwell, K.B. Operational Aerosol Observations (AEROBS) from AVHRR/3 On Board NOAA-KLM Satellites. *J. Atmos. Oceanic Technol.* **2004**, *21*, 3–26.
[CrossRef]

35. Hsu, N.C.; Lee, J.; Sayer, A.M.; Carletta, N.; Chen, S.-H.; Tucker, C.J.; Holben, B.N.; Tsay, S.-C. Retrieving near-global aerosol loading over land and ocean from AVHRR. *J. Geophys. Res. Atmos.* **2017**, *122*, 9968–9989.
[CrossRef]

36. Herman, J.R.; Bhartia, P.K.; Torres, O.; Hsu, C.; Seftor, C.; Celarier, E. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. *J. Geophys. Res.* **1997**, *102*, 16911–16922. [CrossRef]

37. Tanré, D.; Herman, M.; Kaufman, Y.J. Information on the Aerosol Size Distribution contained in the Solar Reflected Spectral Radiances. *J. Geophys. Res.* **1996**, *101*, 19043–19060. [CrossRef]

38. D'Almeida, G.A. On the variability of desert aerosol radiative characteristics. *J. Geophys. Res.* **1987**, *92*, 3017–3026. [CrossRef]

39. Tanré, D.; Remer, L.A.; Kaufman, Y.J.; Mattoo, S.; Hobbs, P.V.; Livingston, J.M.; Russell, P.B.; Smirnov, A. Retrieval of Aerosol Optical Thickness and Size Distribution over Ocean from the MODIS Airborne Simulator during TARFOX. *J. Geophys. Res.* **1999**, *104*, 2261–2278. [CrossRef]

40. Tanré, D.; Kaufman, Y.J.; Holben, B.N.; Chatenet, B.; Karnieli, A.; Lavenu, F.; Blarel, L.; Dubovik, O.; Remer, L.A.; Smirnov, A. Climatology of dust aerosol size distribution and optical properties derived from remotely sensed data in the solar spectrum. *J. Geophys. Res.* **2001**, *106*, 18205–18218. [CrossRef]

41. Dubovik, O.; King, M.D. A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements. *J. Geophys. Res.* **2000**, *105*, 20673–20696. [CrossRef]

42. Dubovik, O.; Smirnov, A.; Holben, B.N.; King, M.D.; Kaufman, Y.J.; Eck, T.F.; Slutsker, I. Accuracy assessment of aerosol optical properties retrieval from AERONET sun and sky radiance measurements. *J. Geophys. Res.* **2000**, *105*, 9791–9806. [CrossRef]

43. Dubovik, O.; Holben, B.N.; Eck, T.F.; Smirnov, A.; Kaufman, Y.J.; King, M.D.; Tanré, D.; Slutsker, I. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. *J. Atmos. Sci.* **2002**, *59*, 590–608. [CrossRef]

44. Kaufman, Y.J.; Koren, I.; Remer, L.A.; Tanré, D.; Ginoux, P.; Fan, S. Dust transport and deposition observed from the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) spacecraft over the Atlantic Ocean. *J. Geophys. Res.* **2005**, *110*, D10S12. [CrossRef]

45. Kaufman, Y.J.; Boucher, O.; Tanré, D.; Chin, M.; Remer, L.A.; Takemura, T. Aerosol anthropogenic component estimated from satellite data. *Geophys. Res. Lett.* **2005**, *32*, L17804. [CrossRef]

46. Yu, H.; Remer, L.A.; Chin, M.; Bian, H.-S.; Tan, Q.; Yuan, T.; Zhang, Y. Aerosols from Overseas Rival Domestic Emissions over North America. *Science* **2012**, *337*, 566–569. [CrossRef] [PubMed]

47. Kleidman, R.G.; O'Neill, N.T.; Remer, L.A.; Kaufman, Y.J.; Eck, T.F.; Tanré, D.; Dubovik, O.; Holben, B.N. Comparison of MODIS and AERONET remote sensing retrievals of aerosol fine mode fraction over ocean. *J. Geophys. Res.* **2005**, *110*, D22205. [CrossRef]

48. Yu, H.; Remer, L.A.; Chin, M.; Bian, H.; Kleidman, R.G.; Diehl, T. A satellite-based assessment of transpacific transport of pollution aerosol. *J. Geophys. Res.* **2008**, *113*, D14S12. [CrossRef]

49. Yu, H.; Chin, M.; Remer, L.A.; Kleidman, R.G.; Bellouin, N.; Bian, H.S.; Diehl, T. Variability of marine aerosol fine-mode fraction and estimates of anthropogenic aerosol component over cloud-free oceans from the Moderate Resolution Imaging Spectroradiometer (MODIS). *J. Geophys. Res.* **2009**, *114*, D10206. [CrossRef]

50. King, M.D.; Menzel, W.P.; Grant, P.S.; Myers, J.S.; Arnold, G.T.; Platnick, S.E.; Gumley, L.E.; Tsay, S.-C.; Moeller, C.C.; Fitzgerald, M.; et al. Airborne scanning spectrometer for remote sensing of cloud, aerosol, water vapor and surface properties. *J. Atmos. Ocean. Technol.* **1996**, *13*, 777–794. [CrossRef]

51. Gassó, S.; Hegg, D.A. Comparison of columnar aerosol optical properties measured by the MODIS airborne simulator with in situ measurements. *Rem. Sens. Environ.* **1998**, *66*, 138–152. [CrossRef]

52. Russell, P.B.; Hobbs, P.V.; Stowe, L.L. Aerosol properties and radiative effects in the United States East Coast haze plume: An overview of the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). *J. Geophys. Res.* **1999**, *104*, 2213–2222. [CrossRef]

53. Hobbs, P.V. An overview of the University of Washington airborne measurements and results from the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). *J. Geophys. Res.* **1999**, *104*, 2233–2238. [CrossRef]
34. Ignatov, A.; Sapper, J.; Cox, S.; Laszlo, I.; Nalli, N.R.; Kidwell, K.B. Operational Aerosol Observations (AEROBS) from AVHRR/3 On Board NOAA-KLM Satellites. *J. Atmos. Oceanic Technol.* **2004**, *21*, 3–26.
[CrossRef]

35. Hsu, N.C.; Lee, J.; Sayer, A.M.; Carletta, N.; Chen, S.-H.; Tucker, C.J.; Holben, B.N.; Tsay, S.-C. Retrieving near-global aerosol loading over land and ocean from AVHRR. *J. Geophys. Res. Atmos.* **2017**, *122*, 9968–9989.
[CrossRef]

36. Herman, J.R.; Bhartia, P.K.; Torres, O.; Hsu, C.; Seftor, C.; Celarier, E. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. *J. Geophys. Res.* **1997**, *102*, 16911–16922. [CrossRef]

37. Tanré, D.; Herman, M.; Kaufman, Y.J. Information on the Aerosol Size Distribution contained in the Solar Reflected Spectral Radiances. *J. Geophys. Res.* **1996**, *101*, 19043–19060. [CrossRef]

38. D'Almeida, G.A. On the variability of desert aerosol radiative characteristics. *J. Geophys. Res.* **1987**, *92*, 3017–3026. [CrossRef]

39. Tanré, D.; Remer, L.A.; Kaufman, Y.J.; Mattoo, S.; Hobbs, P.V.; Livingston, J.M.; Russell, P.B.; Smirnov, A. Retrieval of Aerosol Optical Thickness and Size Distribution over Ocean from the MODIS Airborne Simulator during TARFOX. *J. Geophys. Res.* **1999**, *104*, 2261–2278. [CrossRef]

40. Tanré, D.; Kaufman, Y.J.; Holben, B.N.; Chatenet, B.; Karnieli, A.; Lavenu, F.; Blarel, L.; Dubovik, O.; Remer, L.A.; Smirnov, A. Climatology of dust aerosol size distribution and optical properties derived from remotely sensed data in the solar spectrum. *J. Geophys. Res.* **2001**, *106*, 18205–18218. [CrossRef]

41. Dubovik, O.; King, M.D. A flexible inversion algorithm for retrieval of aerosol optical properties from sun and sky radiance measurements. *J. Geophys. Res.* **2000**, *105*, 20673–20696. [CrossRef]

42. Dubovik, O.; Smirnov, A.; Holben, B.N.; King, M.D.; Kaufman, Y.J.; Eck, T.F.; Slutsker, I. Accuracy assessment of aerosol optical properties retrieval from AERONET sun and sky radiance measurements. *J. Geophys. Res.* **2000**, *105*, 9791–9806. [CrossRef]

43. Dubovik, O.; Holben, B.N.; Eck, T.F.; Smirnov, A.; Kaufman, Y.J.; King, M.D.; Tanré, D.; Slutsker, I. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. *J. Atmos. Sci.* **2002**, *59*, 590–608. [CrossRef]

44. Kaufman, Y.J.; Koren, I.; Remer, L.A.; Tanré, D.; Ginoux, P.; Fan, S. Dust transport and deposition observed from the Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) spacecraft over the Atlantic Ocean. *J. Geophys. Res.* **2005**, *110*, D10S12. [CrossRef]

45. Kaufman, Y.J.; Boucher, O.; Tanré, D.; Chin, M.; Remer, L.A.; Takemura, T. Aerosol anthropogenic component estimated from satellite data. *Geophys. Res. Lett.* **2005**, *32*, L17804. [CrossRef]

46. Yu, H.; Remer, L.A.; Chin, M.; Bian, H.-S.; Tan, Q.; Yuan, T.; Zhang, Y. Aerosols from Overseas Rival Domestic Emissions over North America. *Science* **2012**, *337*, 566–569. [CrossRef] [PubMed]

47. Kleidman, R.G.; O'Neill, N.T.; Remer, L.A.; Kaufman, Y.J.; Eck, T.F.; Tanré, D.; Dubovik, O.; Holben, B.N. Comparison of MODIS and AERONET remote sensing retrievals of aerosol fine mode fraction over ocean. *J. Geophys. Res.* **2005**, *110*, D22205. [CrossRef]

48. Yu, H.; Remer, L.A.; Chin, M.; Bian, H.; Kleidman, R.G.; Diehl, T. A satellite-based assessment of transpacific transport of pollution aerosol. *J. Geophys. Res.* **2008**, *113*, D14S12. [CrossRef]

49. Yu, H.; Chin, M.; Remer, L.A.; Kleidman, R.G.; Bellouin, N.; Bian, H.S.; Diehl, T. Variability of marine aerosol fine-mode fraction and estimates of anthropogenic aerosol component over cloud-free oceans from the Moderate Resolution Imaging Spectroradiometer (MODIS). *J. Geophys. Res.* **2009**, *114*, D10206. [CrossRef]

50. King, M.D.; Menzel, W.P.; Grant, P.S.; Myers, J.S.; Arnold, G.T.; Platnick, S.E.; Gumley, L.E.; Tsay, S.-C.; Moeller, C.C.; Fitzgerald, M.; et al. Airborne scanning spectrometer for remote sensing of cloud, aerosol, water vapor and surface properties. *J. Atmos. Ocean. Technol.* **1996**, *13*, 777–794. [CrossRef]

51. Gassó, S.; Hegg, D.A. Comparison of columnar aerosol optical properties measured by the MODIS airborne simulator with in situ measurements. *Rem. Sens. Environ.* **1998**, *66*, 138–152. [CrossRef]

52. Russell, P.B.; Hobbs, P.V.; Stowe, L.L. Aerosol properties and radiative effects in the United States East Coast haze plume: An overview of the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). *J. Geophys. Res.* **1999**, *104*, 2213–2222. [CrossRef]

53. Hobbs, P.V. An overview of the University of Washington airborne measurements and results from the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX). *J. Geophys. Res.* **1999**, *104*, 2233–2238. [CrossRef]
54. Gassó, S.; Hegg, D.A. On the retrieval of columnar aerosol mass and CCN concentration by MODIS. *J. Geophys. Res.* **2003**, *108*, 4010. [CrossRef]

55. Matsumoto, T.; Russell, P.; Mina, C.; Van Ark, W.; Banta, V. Airborne Tracking Sunphotometer. *J. Atmos. Ocean. Technol.* **1987**, *4*, 336–339. [CrossRef]

56. Russell, P.B.; Livingston, J.M.; Hignett, P.; Kinne, S.; Wong, J.; Chien, A.; Bergstrom, R.; Durkee, P.; Hobbs, P.V. Aerosol-induced radiative flux changes off the United States mid-Atlantic coast: Comparison of values calculated from sunphotometer and in situ data with those measured by airborne pyranometer. *J. Geophys. Res.* **1999**, *104*, 2289–2307. [CrossRef]

57. Remer, L.A.; Kaufman, Y.J. Aerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived from four years of MODIS data. *Atmos. Chem. Phys.* **2006**, *6*, 237–253. [CrossRef]

58. Kaufman, Y.J.; Sendra, C. Algorithm for atmospheric corrections. *Int. J. Remote Sens.* **1988**, *9*, 1357–1381. [CrossRef]

59. Kaufman, Y.J.; Remer, L.A. Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies. *IEEE Trans. Geosci. Rem. Sens.* **1994**, *32*, 672–683. [CrossRef]

60. Kaufman, Y.J.; Wald, A.E.; Remer, L.A.; Gao, B.-C.; Li, R.-R.; Flynn, L. The MODIS 2.1 µm Channel—Correlation with visible reflectance for use in remote sensing of aerosol. *IEEE Trans. Geosci.* **1997**, *35*, 1286–1298. [CrossRef]

61. Remer, L.A.; Wald, A.E.; Kaufman, Y.J. Angular and Seasonal Variation of Spectral Surface Reflectance Ratios: Application to the Remote Sensing of Aerosol Over Land. *IEEE Trans. Geosci. Rem. Sens.* **2001**, *39*, 275–283. [CrossRef]

62. Kaufman, Y.J.; Gobron, N.; Pinty, B.; Widlowski, J.-L.; Verstraete, M.M. Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm—Theory. *Geophys. Res. Lett.* **2002**, *29*, 2116. [CrossRef]

63. Kaufman, Y.J.; Gitelson, A.; Karnieli, A.; Ganor, E.; Fraser, R.S.; Nakajima, T.; Mattoo, S.; Holben, B.N. Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements. *J. Geophys. Res.* **1994**, *99*, 10341–10356. [CrossRef]

64. Remer, L.A.; Kaufman, Y.J. Dynamical Aerosol Model: Urban/industrial Aerosol. *J. Geophys. Res.* **1998**, *103*, 13859–13871. [CrossRef]

65. Remer, L.A.; Kaufman, Y.J.; Holben, B.N.; Thompson, A.M.; McNamara, D. A model of tropical biomass burning smoke aerosol size distribution. *J. Geophys. Res.* **1998**, *103*, 31879–31891. [CrossRef]

66. Remer, L.A.; Gassó, S.; Hegg, D.A.; Kaufman, Y.J.; Holben, B.N. Urban/industrial aerosol: Ground-based sun/sky radiometer and airborne in situ measurements. *J. Geophys. Res.* **1997**, *102*, 16849–16859. [CrossRef]

67. Yamasoe, M.A.; Kaufman, Y.J.; Dubovik, O.; Remer, L.A.; Holben, B.N.; Artaxo, P. Retrieval of the real part of the refractive index of aerosols from sun/sky radiometers during SCAR-B. *J. Geophys. Res.* **1998**, *103*, 31893–31902. [CrossRef]

68. Chu, D.A.; Kaufman, Y.J.; Remer, L.A.; Holben, B.N. Remote sensing of smoke from MODIS airborne simulator during the SCAR-B experiment. *J. Geophys. Res.* **1998**, *103*, 31979–31988. [CrossRef]

69. King, M.D.; Menzel, W.P.; Kaufman, Y.J.; Tanré, D.; Gao, B.-C.; Platnick, S.E.; Ackerman, S.A.; Remer, L.A.; Pincus, R.; Hubanks, P.A. Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. *IEEE Trans. Geo. Rem. Sens.* **2003**, *41*, 442–458. [CrossRef]

70. Berrick, S.W.; Leptoukh, G.; Farley, J.D.; Rui, H. Giovanni: A Web Service Workflow-Based Data Visualization and Analysis System. *IEEE Trans. Geo. Rem. Sens.* **2009**, *47*, 106–113. [CrossRef]

71. Holben, B.N.; Tanre, D.; Smirnov, A.; Eck, T.F.; Slutsker, I.; Abuhassan, N.; Newcomb, W.W.; Schafer, J.; Chatenet, B.; Lavenue, F.; et al. An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET. *J. Geophys. Res.* **2001**, *106*, 12067–12097. [CrossRef]

72. Smirnov, A.; Holben, B.N.; Eck, T.F.; Dubovik, O.; Slutsker, I. Cloud screening and quality control algorithms for the AERONET database. *Rem. Sens. Environ.* **2000**, *73*, 337–349. [CrossRef]

73. Smirnov, A.; Holben, B.N.; Slutsker, I.; Giles, D.M.; McClain, C.R.; Eck, T.F.; Sakerin, S.M.; Macke, A.; Croot, P.; Zibordi, G.; et al. Maritime Aerosol Network as a component of Aerosol Robotic Network. *J. Geophys. Res.* **2009**, *114*, D06204. [CrossRef]

74. Giles, D.M.; Sinyuk, A.; Sorokin, M.G.; Schafer, J.S.; Smirnov, A.; Slutsker, I.; Eck, T.F.; Holben, B.N.; Lewis, J.R.; Campbell, J.R.; et al. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database—Automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. *Atmos. Meas. Tech.* **2019**, *12*, 169–209. [CrossRef]
54. Gassó, S.; Hegg, D.A. On the retrieval of columnar aerosol mass and CCN concentration by MODIS. *J. Geophys. Res.* **2003**, *108*, 4010. [CrossRef]

55. Matsumoto, T.; Russell, P.; Mina, C.; Van Ark, W.; Banta, V. Airborne Tracking Sunphotometer. *J. Atmos. Ocean. Technol.* **1987**, *4*, 336–339. [CrossRef]

56. Russell, P.B.; Livingston, J.M.; Hignett, P.; Kinne, S.; Wong, J.; Chien, A.; Bergstrom, R.; Durkee, P.; Hobbs, P.V. Aerosol-induced radiative flux changes off the United States mid-Atlantic coast: Comparison of values calculated from sunphotometer and in situ data with those measured by airborne pyranometer. *J. Geophys. Res.* **1999**, *104*, 2289–2307. [CrossRef]

57. Remer, L.A.; Kaufman, Y.J. Aerosol direct radiative effect at the top of the atmosphere over cloud free ocean derived from four years of MODIS data. *Atmos. Chem. Phys.* **2006**, *6*, 237–253. [CrossRef]

58. Kaufman, Y.J.; Sendra, C. Algorithm for atmospheric corrections. *Int. J. Remote Sens.* **1988**, *9*, 1357–1381. [CrossRef]

59. Kaufman, Y.J.; Remer, L.A. Detection of Forests Using Mid-IR Reflectance: An Application for Aerosol Studies. *IEEE Trans. Geosci. Rem. Sens.* **1994**, *32*, 672–683. [CrossRef]

60. Kaufman, Y.J.; Wald, A.E.; Remer, L.A.; Gao, B.-C.; Li, R.-R.; Flynn, L. The MODIS 2.1 µm Channel—Correlation with visible reflectance for use in remote sensing of aerosol. *IEEE Trans. Geosci.* **1997**, *35*, 1286–1298. [CrossRef]

61. Remer, L.A.; Wald, A.E.; Kaufman, Y.J. Angular and Seasonal Variation of Spectral Surface Reflectance Ratios: Application to the Remote Sensing of Aerosol Over Land. *IEEE Trans. Geosci. Rem. Sens.* **2001**, *39*, 275–283. [CrossRef]

62. Kaufman, Y.J.; Gobron, N.; Pinty, B.; Widlowski, J.-L.; Verstraete, M.M. Relationship between surface reflectance in the visible and mid-IR used in MODIS aerosol algorithm—Theory. *Geophys. Res. Lett.* **2002**, *29*, 2116. [CrossRef]

63. Kaufman, Y.J.; Gitelson, A.; Karnieli, A.; Ganor, E.; Fraser, R.S.; Nakajima, T.; Mattoo, S.; Holben, B.N. Size distribution and scattering phase function of aerosol particles retrieved from sky brightness measurements. *J. Geophys. Res.* **1994**, *99*, 10341–10356. [CrossRef]

64. Remer, L.A.; Kaufman, Y.J. Dynamical Aerosol Model: Urban/industrial Aerosol. *J. Geophys. Res.* **1998**, *103*, 13859–13871. [CrossRef]

65. Remer, L.A.; Kaufman, Y.J.; Holben, B.N.; Thompson, A.M.; McNamara, D. A model of tropical biomass burning smoke aerosol size distribution. *J. Geophys. Res.* **1998**, *103*, 31879–31891. [CrossRef]

66. Remer, L.A.; Gassó, S.; Hegg, D.A.; Kaufman, Y.J.; Holben, B.N. Urban/industrial aerosol: Ground-based sun/sky radiometer and airborne in situ measurements. *J. Geophys. Res.* **1997**, *102*, 16849–16859. [CrossRef]

67. Yamasoe, M.A.; Kaufman, Y.J.; Dubovik, O.; Remer, L.A.; Holben, B.N.; Artaxo, P. Retrieval of the real part of the refractive index of aerosols from sun/sky radiometers during SCAR-B. *J. Geophys. Res.* **1998**, *103*, 31893–31902. [CrossRef]

68. Chu, D.A.; Kaufman, Y.J.; Remer, L.A.; Holben, B.N. Remote sensing of smoke from MODIS airborne simulator during the SCAR-B experiment. *J. Geophys. Res.* **1998**, *103*, 31979–31988. [CrossRef]

69. King, M.D.; Menzel, W.P.; Kaufman, Y.J.; Tanré, D.; Gao, B.-C.; Platnick, S.E.; Ackerman, S.A.; Remer, L.A.; Pincus, R.; Hubanks, P.A. Cloud and aerosol properties, precipitable water, and profiles of temperature and water vapor from MODIS. *IEEE Trans. Geo. Rem. Sens.* **2003**, *41*, 442–458. [CrossRef]

70. Berrick, S.W.; Leptoukh, G.; Farley, J.D.; Rui, H. Giovanni: A Web Service Workflow-Based Data Visualization and Analysis System. *IEEE Trans. Geo. Rem. Sens.* **2009**, *47*, 106–113. [CrossRef]

71. Holben, B.N.; Tanre, D.; Smirnov, A.; Eck, T.F.; Slutsker, I.; Abuhassan, N.; Newcomb, W.W.; Schafer, J.; Chatenet, B.; Lavenue, F.; et al. An emerging ground-based aerosol climatology: Aerosol Optical Depth from AERONET. *J. Geophys. Res.* **2001**, *106*, 12067–12097. [CrossRef]

72. Smirnov, A.; Holben, B.N.; Eck, T.F.; Dubovik, O.; Slutsker, I. Cloud screening and quality control algorithms for the AERONET database. *Rem. Sens. Environ.* **2000**, *73*, 337–349. [CrossRef]

73. Smirnov, A.; Holben, B.N.; Slutsker, I.; Giles, D.M.; McClain, C.R.; Eck, T.F.; Sakerin, S.M.; Macke, A.; Croot, P.; Zibordi, G.; et al. Maritime Aerosol Network as a component of Aerosol Robotic Network. *J. Geophys. Res.* **2009**, *114*, D06204. [CrossRef]

74. Giles, D.M.; Sinyuk, A.; Sorokin, M.G.; Schafer, J.S.; Smirnov, A.; Slutsker, I.; Eck, T.F.; Holben, B.N.; Lewis, J.R.; Campbell, J.R.; et al. Advancements in the Aerosol Robotic Network (AERONET) Version 3 database—Automated near-real-time quality control algorithm with improved cloud screening for Sun photometer aerosol optical depth (AOD) measurements. *Atmos. Meas. Tech.* **2019**, *12*, 169–209. [CrossRef]
75. King, M.D.; Kaufman, Y.J.; Tanré, D.; Nakajima, T. Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future. *Bull. Amer. Meteor. Soc.* **1999**, *80*, 2229–2260. [CrossRef]

76. Munchak, L.A.; Levy, R.C.; Mattoo, S.; Remer, L.A.; Holben, B.N.; Schafer, J.S.; Hostetler, C.A.; Ferrare, R.A. MODIS 3 km aerosol product: Applications over land in an urban/suburban region. *Atmos. Meas. Tech.* **2013**, *6*, 1747–1759. [CrossRef]

77. Gupta, P.; Remer, L.A.; Levy, R.C.; Mattoo, S. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions. *Atmos. Meas. Tech.* **2018**, *11*, 3145–3159. [CrossRef]

78. Xiao, Q.; Zhang, H.; Choi, M.; Li, S.; Kondragunta, S.; Kim, J.; Holben, B.; Levy, R.C.; Liu, Y. Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia. *Atmos. Chem. Phys.* **2016**, *16*, 1255–1269. [CrossRef]

79. Xie, Y.; Zhang, Y.; Xiong, X.; Qu, J.J.; Che, H. Validation of MODIS aerosol optical depth product over China using CARSNET measurements. *Atmos. Environ.* **2011**, *45*, 5970–5978. [CrossRef]

80. Hsu, N.C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A.M.; Hansell, R.; Seftor, C.S.; Huang, J.; Tsay, S.-C. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. *J. Geophys. Res. Atmos.* **2013**, *118*, 9296–9315. [CrossRef]

81. Liu, H.; Remer, L.A.; Huang, J.; Huang, H.-C.; Kondragunta, S.; Laszlo, I.; Oo, M.; Jackson, J.M. Preliminary evaluation of S-NPP VIIRS aerosol optical thickness. *J. Geophys. Res. Atmos.* **2014**, *119*, 3942–3962. [CrossRef]

82. Nichol, J.E.; Bilal, M. Validation of MODIS 3 km resolution aerosol optical depth retrievals over Asia. *Remote Sens.* **2016**, *8*, 328. [CrossRef]

83. O'Neill, N.T.; Eck, T.F.; Smirnov, A.; Holben, B.N.; Thulasiraman, S. Spectral discrimination of coarse and fine mode optical depth. *J. Geophys. Res.* **2003**, *108*, 4559–4573. [CrossRef]

84. Ichoku, C.; Chu, D.A.; Mattoo, S.; Kaufman, Y.J.; Remer, L.A.; Tanré, D.; Slutsker, I.; Holben, B.N. A spatio-temporal approach for global validation and analysis of MODIS aerosol products. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

85. Petrenko, M.; Ichoku, C.; Leptoukh, G. Multi-sensor Aerosol Products Sampling System (MAPSS). *Atmos. Meas. Tech.* **2012**, *5*, 913–926. [CrossRef]

86. MAPSS: Multi-Sensor Aerosol Products Sampling System. Available online: https://giovanni.gsfc.nasa.gov/mapss/ (accessed on 28 July 2020).

87. Chu, D.A.; Kaufman, Y.J.; Ichoku, C.; Remer, L.A.; Tanré, D.; Holben, B.N. Validation of MODIS aerosol optical depth retrieval over land. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

88. Remer, L.A.; Tanré, D.; Kaufmann, Y.J.; Ichoku, C.; Mattoo, S.; Levy, R.; Chu, D.A.; Holben, B.; Dubovik, O.; Smirnov, A.; et al. Validation of MODIS aerosol retrieval over ocean. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

89. Chu, D.A.; Remer, L.A.; Kaufman, Y.J.; Schmid, B.; Redemann, J.; Knobelspiesse, K.; Chern, J.-D.; Livingston, J.; Russell, P.B.; Xiong, X.; et al. Evaluation of aerosol properties over ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia. *J. Geophys. Res.* **2005**, *110*, D07308. [CrossRef]

90. Ichoku, C.; Remer, L.A.; Kaufman, Y.J.; Levy, R.; Chu, D.A.; Tanré, D.; Holben, B.N. MODIS observation of aerosols and estimation of aerosol radiative forcing over southern Africa during SAFARI 2000. *J. Geophys. Res.* **2003**, *108*, 8499. [CrossRef]

91. Levy, R.C.; Remer, L.A.; Tanré, D.; Kaufman, Y.J.; Ichoku, C.; Holben, B.N.; Livingston, J.M.; Russell, P.B.; Maring, H. Evaluation of the MODIS retrievals of dust aerosol over the ocean during PRIDE. *J. Geophys. Res.* **2003**, *108*. [CrossRef]

92. Levy, R.C.; Remer, L.A.; Martins, J.V.; Kaufman, Y.J.; Plana-Fattori, A.; Redemann, J.; Russell, P.B.; Wenny, B. Evaluation of the MODIS aerosol retrievals over ocean and land during CLAMS. *J. Atmos. Sci.* **2005**, *62*, 974–992. [CrossRef]

93. Xia, X.A.; Chen, H.B.; Wang, P.C. Validation of MODIS aerosol retrievals and evaluation of potential cloud contamination in East Asia. *J. Environ. Sci. (China)* **2004**, *16*, 832–837.

94. Ichoku, C.; Remer, L.A.; Eck, T.F. Quantitative evaluation and intercomparison of morning and afternoon Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol measurements from Terra and Aqua. *J. Geophys. Res.* **2005**, *110*, D10S03. [CrossRef]

95. Tripathi, S.N.; Dey, S.; Chandel, A.; Srivastava, S.; Singh, R.P.; Holben, B.N. Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India. *Ann. Geophys.* **2005**, *23*, 1093–1101. [CrossRef]
75. King, M.D.; Kaufman, Y.J.; Tanré, D.; Nakajima, T. Remote Sensing of Tropospheric Aerosols from Space: Past, Present, and Future. *Bull. Amer. Meteor. Soc.* **1999**, *80*, 2229–2260. [CrossRef]

76. Munchak, L.A.; Levy, R.C.; Mattoo, S.; Remer, L.A.; Holben, B.N.; Schafer, J.S.; Hostetler, C.A.; Ferrare, R.A. MODIS 3 km aerosol product: Applications over land in an urban/suburban region. *Atmos. Meas. Tech.* **2013**, *6*, 1747–1759. [CrossRef]

77. Gupta, P.; Remer, L.A.; Levy, R.C.; Mattoo, S. Validation of MODIS 3 km land aerosol optical depth from NASA's EOS Terra and Aqua missions. *Atmos. Meas. Tech.* **2018**, *11*, 3145–3159. [CrossRef]

78. Xiao, Q.; Zhang, H.; Choi, M.; Li, S.; Kondragunta, S.; Kim, J.; Holben, B.; Levy, R.C.; Liu, Y. Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD retrievals against ground sunphotometer observations over East Asia. *Atmos. Chem. Phys.* **2016**, *16*, 1255–1269. [CrossRef]

79. Xie, Y.; Zhang, Y.; Xiong, X.; Qu, J.J.; Che, H. Validation of MODIS aerosol optical depth product over China using CARSNET measurements. *Atmos. Environ.* **2011**, *45*, 5970–5978. [CrossRef]

80. Hsu, N.C.; Jeong, M.-J.; Bettenhausen, C.; Sayer, A.M.; Hansell, R.; Seftor, C.S.; Huang, J.; Tsay, S.-C. Enhanced Deep Blue aerosol retrieval algorithm: The second generation. *J. Geophys. Res. Atmos.* **2013**, *118*, 9296–9315. [CrossRef]

81. Liu, H.; Remer, L.A.; Huang, J.; Huang, H.-C.; Kondragunta, S.; Laszlo, I.; Oo, M.; Jackson, J.M. Preliminary evaluation of S-NPP VIIRS aerosol optical thickness. *J. Geophys. Res. Atmos.* **2014**, *119*, 3942–3962. [CrossRef]

82. Nichol, J.E.; Bilal, M. Validation of MODIS 3 km resolution aerosol optical depth retrievals over Asia. *Remote Sens.* **2016**, *8*, 328. [CrossRef]

83. O'Neill, N.T.; Eck, T.F.; Smirnov, A.; Holben, B.N.; Thulasiraman, S. Spectral discrimination of coarse and fine mode optical depth. *J. Geophys. Res.* **2003**, *108*, 4559–4573. [CrossRef]

84. Ichoku, C.; Chu, D.A.; Mattoo, S.; Kaufman, Y.J.; Remer, L.A.; Tanré, D.; Slutsker, I.; Holben, B.N. A spatio-temporal approach for global validation and analysis of MODIS aerosol products. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

85. Petrenko, M.; Ichoku, C.; Leptoukh, G. Multi-sensor Aerosol Products Sampling System (MAPSS). *Atmos. Meas. Tech.* **2012**, *5*, 913–926. [CrossRef]

86. MAPSS: Multi-Sensor Aerosol Products Sampling System. Available online: https://giovanni.gsfc.nasa.gov/mapss/ (accessed on 28 July 2020).

87. Chu, D.A.; Kaufman, Y.J.; Ichoku, C.; Remer, L.A.; Tanré, D.; Holben, B.N. Validation of MODIS aerosol optical depth retrieval over land. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

88. Remer, L.A.; Tanré, D.; Kaufmann, Y.J.; Ichoku, C.; Mattoo, S.; Levy, R.; Chu, D.A.; Holben, B.; Dubovik, O.; Smirnov, A.; et al. Validation of MODIS aerosol retrieval over ocean. *Geophys. Res. Lett.* **2002**, *29*. [CrossRef]

89. Chu, D.A.; Remer, L.A.; Kaufman, Y.J.; Schmid, B.; Redemann, J.; Knobelspiesse, K.; Chern, J.-D.; Livingston, J.; Russell, P.B.; Xiong, X.; et al. Evaluation of aerosol properties over ocean from Moderate Resolution Imaging Spectroradiometer (MODIS) during ACE-Asia. *J. Geophys. Res.* **2005**, *110*, D07308. [CrossRef]

90. Ichoku, C.; Remer, L.A.; Kaufman, Y.J.; Levy, R.; Chu, D.A.; Tanré, D.; Holben, B.N. MODIS observation of aerosols and estimation of aerosol radiative forcing over southern Africa during SAFARI 2000. *J. Geophys. Res.* **2003**, *108*, 8499. [CrossRef]

91. Levy, R.C.; Remer, L.A.; Tanré, D.; Kaufman, Y.J.; Ichoku, C.; Holben, B.N.; Livingston, J.M.; Russell, P.B.; Maring, H. Evaluation of the MODIS retrievals of dust aerosol over the ocean during PRIDE. *J. Geophys. Res.* **2003**, *108*. [CrossRef]

92. Levy, R.C.; Remer, L.A.; Martins, J.V.; Kaufman, Y.J.; Plana-Fattori, A.; Redemann, J.; Russell, P.B.; Wenny, B. Evaluation of the MODIS aerosol retrievals over ocean and land during CLAMS. *J. Atmos. Sci.* **2005**, *62*, 974–992. [CrossRef]

93. Xia, X.A.; Chen, H.B.; Wang, P.C. Validation of MODIS aerosol retrievals and evaluation of potential cloud contamination in East Asia. *J. Environ. Sci. (China)* **2004**, *16*, 832–837.

94. Ichoku, C.; Remer, L.A.; Eck, T.F. Quantitative evaluation and intercomparison of morning and afternoon Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol measurements from Terra and Aqua. *J. Geophys. Res.* **2005**, *110*, D10S03. [CrossRef]

95. Tripathi, S.N.; Dey, S.; Chandel, A.; Srivastava, S.; Singh, R.P.; Holben, B.N. Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India. *Ann. Geophys.* **2005**, *23*, 1093–1101. [CrossRef]
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97. Wang, Y.; Yuan, Q.; Li, T.; Shen, H.; Zheng, L.; Zhang, L. Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces. *Atmos. Environ.* **2019**, *200*, 280–301. [CrossRef]

98. Wei, J.; Li, Z.; Peng, Y.; Sun, L. MODIS Collection 6.1 aerosol optical depth products over land and ocean: Validation and comparison. *Atmos. Environ.* **2019**, *201*, 428–440. [CrossRef]

99. Bilal, M.; Nazeer, M.; Nichol, J.; Qiu, Z.; Wang, L.; Bleiweiss, M.P.; Shen, X.; Campbell, J.R.; Lolli, S. Evaluation of Terra-MODIS C6 and C6. 1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004–2014. *Remote Sens.* **2019**, *11*, 486. [CrossRef]

100. Bright, J.M.; Gueymard, C.A. Climate-specific and global validation of MODIS Aqua and Terra aerosol optical depth at 452 AERONET stations. *Sol. Energy* **2019**, *183*, 594–605. [CrossRef]

101. Zhang, M.; Liu, J.; Li, W.; Bilal, M.; Zhao, F.; Zhang, C.; Yuan, B.; Khedher, K.M. Evaluation of the Aqua-MODIS C6 and C6. 1 Aerosol Optical Depth Products in the Yellow River Basin, China. *Atmosphere* **2019**, *10*, 426. [CrossRef]

102. Choi, M.; Lim, H.; Kim, J.; Lee, S.; Eck, T.F.; Holben, B.N.; Garay, M.J.; Hyer, E.J.; Saide, P.E.; Liu, H. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign. *Atmos. Meas. Tech.* **2019**, *12*, 4619–4641. [CrossRef]

103. Tian, X.; Gao, Z. Validation and Accuracy Assessment of MODIS C6. 1 Aerosol Products over the Heavy Aerosol Loading Area. *Atmosphere* **2019**, *10*, 548. [CrossRef]

104. Fan, X.; Xia, X.; Chen, H. Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain. *Atmosphere* **2019**, *10*, 480. [CrossRef]

105. Che, H.; Yang, L.; Liu, C.; Xia, X.; Wang, Y.; Wang, H.; Wang, H.; Lu, X.; Zhang, X. Long-term validation of MODIS C6 and C6. 1 Dark Target aerosol products over China using CARSNET and AERONET. *Chemosphere* **2019**, *236*, 124268. [CrossRef] [PubMed]

106. Mei, L.; Zhao, C.; de Leeuw, G.; Che, H.; Che, Y.; Rozanov, V.; Vountas, M.; Burrows, J.P. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption. *Atmos. Res.* **2019**, *228*, 161–175. [CrossRef]

107. Huang, J.; Kondragunta, S.; Laszlo, I.; Liu, H.; Remer, L.A.; Zhang, H.; Superczynski, S.; Ciren, P.; Holben, B.N.; Petrenko, M. Validation and expected error estimation of Suomi-NPP VIIRS aerosol optical thickness and Ångström exponent with AERONET. *J. Geophys. Res. Atmos.* **2016**, *121*, 7139–7160. [CrossRef]

108. Huang, Y.; Zhu, B.; Zhu, Z.; Zhang, T.; Gong, W.; Ji, Y.; Xia, X.; Wang, L.; Zhou, X.; Chen, D. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface. *Earth Space Sci.* **2019**, *6*, 2575–2592. [CrossRef]

109. Li, Y.; Shi, G.; Sun, Z. Evaluation and improvement of MODIS aerosol optical depth products over China. *Atmos. Environ.* **2020**, *223*, 117251. [CrossRef]

110. You, Y.; Zhao, T.; Xie, Y.; Zheng, Y.; Zhu, J.; Xia, J.; Cao, L.; Wang, C.; Che, H.; Liao, Y.; et al. Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017. *Atmos. Environ.* **2020**, *223*, 117257. [CrossRef]

111. Yang, X.; Zhao, C.; Luo, N.; Zhao, W.; Shi, W.; Yan, X. Evaluation and Comparison of Himawari-8 L2 V1. 0, V2. 1 and MODIS C6. 1 aerosol products over Asia and the oceania regions. *Atmos. Environ.* **2020**, *220*, 117068. [CrossRef]

112. Holben, B.N.; Eck, T.F.; Slutsker, I.; Smirnov, A.; Sinyuk, A.; Schafer, J.; Giles, D.; Dubovik, O. Aeronet's Version 2.0 quality assurance criteria. *Proc. SPIE Int. Soc. Opt. Eng.* **2006**, *6408*, 64080Q. [CrossRef]

113. Schmid, B.; Redemann, J.; Russell, P.B.; Hobbs, P.V.; Hlavka, D.L.; McGill, M.J.; Holben, B.N.; Welton, E.J.; Campbell, J.R.; Torres, O.; et al. Coordinated airborne, spaceborne, and ground-based measurements of massive thick aerosol layers during the dry season in southern Africa. *J. Geophys. Res.* **2003**, *108*, 8496. [CrossRef]
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97. Wang, Y.; Yuan, Q.; Li, T.; Shen, H.; Zheng, L.; Zhang, L. Evaluation and comparison of MODIS Collection 6.1 aerosol optical depth against AERONET over regions in China with multifarious underlying surfaces. *Atmos. Environ.* **2019**, *200*, 280–301. [CrossRef]

98. Wei, J.; Li, Z.; Peng, Y.; Sun, L. MODIS Collection 6.1 aerosol optical depth products over land and ocean: Validation and comparison. *Atmos. Environ.* **2019**, *201*, 428–440. [CrossRef]

99. Bilal, M.; Nazeer, M.; Nichol, J.; Qiu, Z.; Wang, L.; Bleiweiss, M.P.; Shen, X.; Campbell, J.R.; Lolli, S. Evaluation of Terra-MODIS C6 and C6. 1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004–2014. *Remote Sens.* **2019**, *11*, 486. [CrossRef]

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101. Zhang, M.; Liu, J.; Li, W.; Bilal, M.; Zhao, F.; Zhang, C.; Yuan, B.; Khedher, K.M. Evaluation of the Aqua-MODIS C6 and C6. 1 Aerosol Optical Depth Products in the Yellow River Basin, China. *Atmosphere* **2019**, *10*, 426. [CrossRef]

102. Choi, M.; Lim, H.; Kim, J.; Lee, S.; Eck, T.F.; Holben, B.N.; Garay, M.J.; Hyer, E.J.; Saide, P.E.; Liu, H. Validation, comparison, and integration of GOCI, AHI, MODIS, MISR, and VIIRS aerosol optical depth over East Asia during the 2016 KORUS-AQ campaign. *Atmos. Meas. Tech.* **2019**, *12*, 4619–4641. [CrossRef]

103. Tian, X.; Gao, Z. Validation and Accuracy Assessment of MODIS C6. 1 Aerosol Products over the Heavy Aerosol Loading Area. *Atmosphere* **2019**, *10*, 548. [CrossRef]

104. Fan, X.; Xia, X.; Chen, H. Intercomparison of Multiple Satellite Aerosol Products against AERONET over the North China Plain. *Atmosphere* **2019**, *10*, 480. [CrossRef]

105. Che, H.; Yang, L.; Liu, C.; Xia, X.; Wang, Y.; Wang, H.; Wang, H.; Lu, X.; Zhang, X. Long-term validation of MODIS C6 and C6. 1 Dark Target aerosol products over China using CARSNET and AERONET. *Chemosphere* **2019**, *236*, 124268. [CrossRef] [PubMed]

106. Mei, L.; Zhao, C.; de Leeuw, G.; Che, H.; Che, Y.; Rozanov, V.; Vountas, M.; Burrows, J.P. Understanding MODIS dark-target collection 5 and 6 aerosol data over China: Effect of surface type, aerosol loading and aerosol absorption. *Atmos. Res.* **2019**, *228*, 161–175. [CrossRef]

107. Huang, J.; Kondragunta, S.; Laszlo, I.; Liu, H.; Remer, L.A.; Zhang, H.; Superczynski, S.; Ciren, P.; Holben, B.N.; Petrenko, M. Validation and expected error estimation of Suomi-NPP VIIRS aerosol optical thickness and Ångström exponent with AERONET. *J. Geophys. Res. Atmos.* **2016**, *121*, 7139–7160. [CrossRef]

108. Huang, Y.; Zhu, B.; Zhu, Z.; Zhang, T.; Gong, W.; Ji, Y.; Xia, X.; Wang, L.; Zhou, X.; Chen, D. Evaluation and Comparison of MODIS Collection 6.1 and Collection 6 Dark Target Aerosol Optical Depth over Mainland China Under Various Conditions Including Spatiotemporal Distribution, Haze Effects, and Underlying Surface. *Earth Space Sci.* **2019**, *6*, 2575–2592. [CrossRef]

109. Li, Y.; Shi, G.; Sun, Z. Evaluation and improvement of MODIS aerosol optical depth products over China. *Atmos. Environ.* **2020**, *223*, 117251. [CrossRef]

110. You, Y.; Zhao, T.; Xie, Y.; Zheng, Y.; Zhu, J.; Xia, J.; Cao, L.; Wang, C.; Che, H.; Liao, Y.; et al. Variation of the aerosol optical properties and validation of MODIS AOD products over the eastern edge of the Tibetan Plateau based on ground-based remote sensing in 2017. *Atmos. Environ.* **2020**, *223*, 117257. [CrossRef]

111. Yang, X.; Zhao, C.; Luo, N.; Zhao, W.; Shi, W.; Yan, X. Evaluation and Comparison of Himawari-8 L2 V1. 0, V2. 1 and MODIS C6. 1 aerosol products over Asia and the oceania regions. *Atmos. Environ.* **2020**, *220*, 117068. [CrossRef]

112. Holben, B.N.; Eck, T.F.; Slutsker, I.; Smirnov, A.; Sinyuk, A.; Schafer, J.; Giles, D.; Dubovik, O. Aeronet's Version 2.0 quality assurance criteria. *Proc. SPIE Int. Soc. Opt. Eng.* **2006**, *6408*, 64080Q. [CrossRef]

113. Schmid, B.; Redemann, J.; Russell, P.B.; Hobbs, P.V.; Hlavka, D.L.; McGill, M.J.; Holben, B.N.; Welton, E.J.; Campbell, J.R.; Torres, O.; et al. Coordinated airborne, spaceborne, and ground-based measurements of massive thick aerosol layers during the dry season in southern Africa. *J. Geophys. Res.* **2003**, *108*, 8496. [CrossRef]
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117. Russell, P.B.; Livingston, J.M.; Redemann, J.; Schmid, B.; Ramirez, S.A.; Eilers, J.; Kahn, R.; Chu, D.A.; Remer, L.; Quinn, P.K.; et al. Multi-grid-cell validation of satellite aerosol property retrievals in INTEX/ITCT/ICARTT 2004. *J. Geophys. Res.* **2007**, *112*, D12S09. [CrossRef]

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206. Wang, M.; Ghan, S.; Liu, X.; L'Ecuyer, T.S.; Zhang, K.; Morrison, H.; Ovchinnikov, M.; Easter, R.; Marchand, R.; Chand, D.; et al. Constraining cloud lifetime effects of aerosols using A-Train satellite measurements. *Geophys. Res. Lett.* **2012**, *39*, L15709. [CrossRef]

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213. Yu, H.; Tan, Q.; Chin, M.; Remer, L.A.; Kahn, R.A.; Bian, H.; Kim, D.; Zhang, Z.; Yuan, T.; Omar, A.; et al. Estimates of African dust deposition along the trans-Atlantic transit using the decadelong record of aerosol measurements from CALIOP, MODIS, MISR, and IASI. *J. Geophys. Res. Atmos.* **2019**, *124*, 7975–7996. [CrossRef] [PubMed]

214. Gassó, S.; Stein, A.; Marino, F.; Castellano, E.; Udisti, R.; Ceratto, J. A combined observational and modeling approach to study modern dust transport from the Patagonia desert to East Antarctica. *Atmos. Chem. Phys.* **2010**, *10*, 8287–8303. [CrossRef]

215. Heald, C.L.; Jacob, D.J.; Park, R.J.; Alexander, B.; Fairlie, T.D.; Yantosca, R.M.; Chu, D.A. Transpacific transport of Asian anthropogenic aerosols and its impact on surface air quality in the United States. *J. Geophys. Res.* **2006**, *111*, D14310. [CrossRef]

216. Stohl, A.; Berg, T.; Burkhart, J.F.; Fjæraa, A.M.; Forster, C.; Herber, A.; Hov, Ø.; Lunder, C.; McMillan, W.W.; Oltmans, S.; et al. Arctic smoke record air pollution levels in the European Arctic during a period of abnormal warmth, due to agricultural fires in eastern Europe. *Atmos. Chem. Phys* **2007**, *7*, 511–534. [CrossRef]
198. Matsui, T.; Masunaga, H.; Kreidenweis, S.M.; Pielke Sr., R.A.; Tao, W.-K.; Chin, M.; Kaufman, Y.J. Satellite-based assessment of marine low cloud variability associated with aerosol, atmospheric stability, and the diurnal cycle. *J. Geophys. Res.* **2006**, *111*, D17204. [CrossRef]

199. Chylek, P.; Dubey, M.K.; Lohmann, U.; Ramanathan, V.; Kaufman, Y.J.; Lesins, G.; Hudson, J.; Altmann, G.; Olsen, S. Aerosol indirect effect over the Indian Ocean. *Geophys. Res. Lett.* **2006**, *33*, L06806. [CrossRef]

200. Myhre, G.; Stordal, F.; Johnsrud, M.; Kaufman, Y.J.; Rosenfeld, D.; Storelvmo, T.; Kristjansson, J.E.; Berntsen, T.K.; Myhre, A.; Isaksen, I.S.A. Aerosol-cloud interaction inferred from MODIS satellite data and global aerosol models. *Atmos. Chem. Phys.* **2007**, *7*, 3081–3101. [CrossRef]

201. Yuan, T.; Li, Z.; Zhang, R.; Fan, J. Increase of cloud droplet size with aerosol optical depth: An observation and modeling study. *J. Geophys. Res.* **2008**, *113*, D04201. [CrossRef]

202. Yuan, T.-L.; Remer, L.A.; Pickering, K.E.; Yu, H.-B. Observational evidence of aerosol enhancement of lightning activity and convective invigoration. *Geophys. Res. Lett.* **2011**, *38*, L04701. [CrossRef]

203. Yuan, T.; Remer, L.; Yu, H. Microphysical, macrophysical and radiative signatures of volcanic aerosols in trade wind cumulus observed by the A-Train. *Atmos. Chem. Phys.* **2011**, *11*, 7119–7132. [CrossRef]

204. Ten Hoeve, J.E.; Remer, L.A.; Jacobson, M.Z. Microphysical and radiative effects of aerosols on warm clouds during the Amazon biomass burning season as observed by MODIS: Impacts of water vapor and land cover. *Atmos. Chem. Phys.* **2011**, *11*, 3021–3036. [CrossRef]

205. Manoj, M.G.; Devara, P.C.S.; Safai, P.D.; Goswami, B.N. Absorbing aerosols facilitate transition of Indian monsoon breaks to active spells. *Climate Dyn.* **2011**, *37*, 2181–2198. [CrossRef]

206. Wang, M.; Ghan, S.; Liu, X.; L'Ecuyer, T.S.; Zhang, K.; Morrison, H.; Ovchinnikov, M.; Easter, R.; Marchand, R.; Chand, D.; et al. Constraining cloud lifetime effects of aerosols using A-Train satellite measurements. *Geophys. Res. Lett.* **2012**, *39*, L15709. [CrossRef]

207. Costantino, L.; Bréon, F.M. Aerosol indirect effect on warm clouds over South-East Atlantic, from co-located MODIS and CALIPSO observations. *Atmos. Chem. Phys.* **2013**, *13*, 69–88. [CrossRef]

208. Solomon, S.; Qin, D.; Manning, M.; Chen, Z.; Marquis, M.; Avery, K.B.; Tignor, M.; Miller, H.L. (Eds.) *Contribution of Working Group 1 to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change*; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2007.

209. Boucher, O.; Randall, D.; Artaxo, P.; Bretherton, C.; Feingold, G.; Forster, P.; Kerminen, V.-M.; Kondo, Y.; Liao, H.; Lohmann, U.; et al. Clouds and Aerosols. In *Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*; Stocker, T.F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S.K., Boschung, J., Nauels, A., Xia, Y., Bex, V., Midgley, P.M., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2013.

210. Gao, Y.; Kaufman, Y.J.; Tanré, D.; Kolber, D.; Falkowski, P.G. Seasonal distributions of aeolian iron fluxes to the global ocean. *Geophys. Res. Lett.* **2001**, *28*, 29–32. [CrossRef]

211. Lin, I.-I.; Chen, J.-P.; Wong, G.T.F.; Huang, C.-W.; Lien, C.-C. Aerosol input to the South China Sea: Results from the MODerate Resolution Imaging Spectro-radiometer, the Quick Scatterometer, and the Measurements of Pollution in the Troposphere Sensor. *Deep Sea Res. Part II Topical Stud. Oceanogr.* **2007**, *54*, 1589–1601. [CrossRef]

212. Yu, H.; Remer, L.A.; Kahn, R.A.; Chin, M.; Zhang, Y. Satellite perspective of aerosol intercontinental transport: From qualitative tracking to quantitative characterization. *Atmos. Res.* **2013**, *124*, 73–100. [CrossRef]

213. Yu, H.; Tan, Q.; Chin, M.; Remer, L.A.; Kahn, R.A.; Bian, H.; Kim, D.; Zhang, Z.; Yuan, T.; Omar, A.; et al. Estimates of African dust deposition along the trans-Atlantic transit using the decadelong record of aerosol measurements from CALIOP, MODIS, MISR, and IASI. *J. Geophys. Res. Atmos.* **2019**, *124*, 7975–7996. [CrossRef] [PubMed]

214. Gassó, S.; Stein, A.; Marino, F.; Castellano, E.; Udisti, R.; Ceratto, J. A combined observational and modeling approach to study modern dust transport from the Patagonia desert to East Antarctica. *Atmos. Chem. Phys.* **2010**, *10*, 8287–8303. [CrossRef]

215. Heald, C.L.; Jacob, D.J.; Park, R.J.; Alexander, B.; Fairlie, T.D.; Yantosca, R.M.; Chu, D.A. Transpacific transport of Asian anthropogenic aerosols and its impact on surface air quality in the United States. *J. Geophys. Res.* **2006**, *111*, D14310. [CrossRef]

216. Stohl, A.; Berg, T.; Burkhart, J.F.; Fjæraa, A.M.; Forster, C.; Herber, A.; Hov, Ø.; Lunder, C.; McMillan, W.W.; Oltmans, S.; et al. Arctic smoke record air pollution levels in the European Arctic during a period of abnormal warmth, due to agricultural fires in eastern Europe. *Atmos. Chem. Phys* **2007**, *7*, 511–534. [CrossRef]
217. Mahowald, N.M.; Engelstaedter, S.; Luo, C.; Sealy, A.; Artaxo, P.; Benitez-Nelson, C.; Bonnet, S.; Chen, Y.; Chuang, P.Y.; Cohen, D.D.; et al. Atmospheric iron deposition: Global distribution, variability, and human perturbations. *Annu. Rev. Mar. Sci.* **2009**, *1*, 245–278. [CrossRef]

218. Ridley, D.A.; Heald, C.L.; Ford, B. North African dust export and deposition: A satellite and model perspective. *J. Geophys. Res.* **2012**, *117*, D02202. [CrossRef]

219. Kim, D.; Chin, M.; Yu, H.; Diehl, T.; Tan, Q.; Kahn, R.A.; Tsigaridis, K.; Bauer, S.E.; Takemura, T.; Pozzoli, L.; et al. Sources, sinks, and transatlantic transport of North African dust aerosol: A multimodel analysis and comparison with remote sensing data. *J. Geophys. Res. Atmos.* **2014**, *119*, 6259–6277. [CrossRef]

220. Yu, H.; Yang, Y.; Wang, H.; Tan, Q.; Chin, M.; Levy, R.C.; Remer, L.A.; Smith, S.J.; Yuan, T.; Shi, Y. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017. *Atmos. Chem. Phys.* **2020**, *20*, 139–161. [CrossRef]

221. Schuerger, A.C.; Smith, D.J.; Griffin, D.W.; Jaffe, D.A.; Wawrik, B.; Burrows, S.M.; Christner, B.C.; González-Martín, C.; Lipp, E.K.; Iii, D.G.S.; et al. Science questions and knowledge gaps to study microbial transport and survival in Asian and African dust plumes reaching North America. *Aerobiologia* **2018**, *34*, 425–435. [CrossRef]

222. Engel-Cox, J.A.; Holloman, C.H.; Coutant, B.W.; Hoff, R.M. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. *Atmos. Environ.* **2004**, *38*, 2495–2509. [CrossRef]

223. Edwards, D.P.; Emmons, L.K.; Hauglustaine, D.A.; Chu, D.A.; Gille, J.C.; Kaufman, Y.J.; Pétron, G.; Yurganov, L.N.; Giglio, L.; Deeter, M.N.; et al. Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability. *J. Geophys. Res.* **2004**, *109*, D24202. [CrossRef]

224. Van Donkelaar, A.; Martin, R.V.; Park, R.J. Estimating ground-level PM₂.₅ using aerosol optical depth determined from satellite remote sensing. *J. Geophys. Res.* **2006**, *111*, D21201. [CrossRef]

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226. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Hsu, N.C.; Kahn, R.A.; Levy, R.C.; Lyapustin, A.; Sayer, A.M.; Winker, D.M. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. *Environ. Sci. Technol.* **2016**, *50*, 3762–3772. [CrossRef]

227. Koelemeijer, R.B.A.; Homan, C.D.; Matthijsen, J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. *Atmos. Environ.* **2006**, *40*, 5304–5315. [CrossRef]

228. Engel-Cox, J.A.; Hoff, R.M.; Rogers, R.; Dimmick, F.; Rush, A.C.; Szykman, J.J.; Al-Saadi, J.; Chu, D.A.; Zell, E.R. Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. *Atmos. Environ.* **2006**, *40*, 8056–8067. [CrossRef]

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230. Kumar, N.; Chu, A.; Foster, A. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. *Atmos. Environ.* **2007**, *41*, 4492–4503. [CrossRef]

231. Schaap, M.; Apituley, A.; Timmermans, R.M.A.; Koelemeijer, R.B.A.; De Leeuw, G. Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands. *Atmos. Chem. Phys.* **2009**, *9*, 909–925. [CrossRef]

232. Guo, J.P.; Zhang, X.Y.; Che, H.Z.; Gong, S.L.; An, X.; Cao, C.X.; Guang, J.; Zhang, H.; Wang, Y.Q.; Zhang, X.C.; et al. Correlation between PM concentrations and aerosol optical depth in eastern China. *Atmos. Environ.* **2009**, *43*, 5876–5886. [CrossRef]

233. Gupta, P.; Christopher, S.A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach. *J. Geophys. Res.* **2009**, *114*, D14205. [CrossRef]

234. Gupta, P.; Christopher, S.A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. *J. Geophys. Res.* **2009**, *114*, D20205. [CrossRef]

235. Zhang, H.; Hoff, R.M.; Engel-Cox, J.A. The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: A geographical comparison by US Environmental Protection Agency regions. *J. Air Waste Man. Assoc.* **2009**, *59*, 1358–1369. [CrossRef]

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218. Ridley, D.A.; Heald, C.L.; Ford, B. North African dust export and deposition: A satellite and model perspective. *J. Geophys. Res.* **2012**, *117*, D02202. [CrossRef]

219. Kim, D.; Chin, M.; Yu, H.; Diehl, T.; Tan, Q.; Kahn, R.A.; Tsigaridis, K.; Bauer, S.E.; Takemura, T.; Pozzoli, L.; et al. Sources, sinks, and transatlantic transport of North African dust aerosol: A multimodel analysis and comparison with remote sensing data. *J. Geophys. Res. Atmos.* **2014**, *119*, 6259–6277. [CrossRef]

220. Yu, H.; Yang, Y.; Wang, H.; Tan, Q.; Chin, M.; Levy, R.C.; Remer, L.A.; Smith, S.J.; Yuan, T.; Shi, Y. Interannual variability and trends of combustion aerosol and dust in major continental outflows revealed by MODIS retrievals and CAM5 simulations during 2003–2017. *Atmos. Chem. Phys.* **2020**, *20*, 139–161. [CrossRef]

221. Schuerger, A.C.; Smith, D.J.; Griffin, D.W.; Jaffe, D.A.; Wawrik, B.; Burrows, S.M.; Christner, B.C.; González-Martín, C.; Lipp, E.K.; Iii, D.G.S.; et al. Science questions and knowledge gaps to study microbial transport and survival in Asian and African dust plumes reaching North America. *Aerobiologia* **2018**, *34*, 425–435. [CrossRef]

222. Engel-Cox, J.A.; Holloman, C.H.; Coutant, B.W.; Hoff, R.M. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. *Atmos. Environ.* **2004**, *38*, 2495–2509. [CrossRef]

223. Edwards, D.P.; Emmons, L.K.; Hauglustaine, D.A.; Chu, D.A.; Gille, J.C.; Kaufman, Y.J.; Pétron, G.; Yurganov, L.N.; Giglio, L.; Deeter, M.N.; et al. Observations of carbon monoxide and aerosols from the Terra satellite: Northern Hemisphere variability. *J. Geophys. Res.* **2004**, *109*, D24202. [CrossRef]

224. Van Donkelaar, A.; Martin, R.V.; Park, R.J. Estimating ground-level PM₂.₅ using aerosol optical depth determined from satellite remote sensing. *J. Geophys. Res.* **2006**, *111*, D21201. [CrossRef]

225. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Kahn, R.; Levy, R.; Verduzco, C.; Villeneuve, P.J. Global estimates of ambient fine particulate matter concentrations from satellite-based aerosol optical depth: Development and application. *Environ. Health Perspect.* **2010**, *118*, 847–855. [CrossRef]

226. Van Donkelaar, A.; Martin, R.V.; Brauer, M.; Hsu, N.C.; Kahn, R.A.; Levy, R.C.; Lyapustin, A.; Sayer, A.M.; Winker, D.M. Global estimates of fine particulate matter using a combined geophysical-statistical method with information from satellites, models, and monitors. *Environ. Sci. Technol.* **2016**, *50*, 3762–3772. [CrossRef]

227. Koelemeijer, R.B.A.; Homan, C.D.; Matthijsen, J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. *Atmos. Environ.* **2006**, *40*, 5304–5315. [CrossRef]

228. Engel-Cox, J.A.; Hoff, R.M.; Rogers, R.; Dimmick, F.; Rush, A.C.; Szykman, J.J.; Al-Saadi, J.; Chu, D.A.; Zell, E.R. Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. *Atmos. Environ.* **2006**, *40*, 8056–8067. [CrossRef]

229. Gupta, P.; Christopher, S.A.; Wang, J.; Gehrig, R.; Lee, Y.C.; Kumar, N. Satellite remote sensing of particulate matter and air quality assessment over global cities. *Atmos. Environ.* **2006**, *40*, 5880–5892. [CrossRef]

230. Kumar, N.; Chu, A.; Foster, A. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. *Atmos. Environ.* **2007**, *41*, 4492–4503. [CrossRef]

231. Schaap, M.; Apituley, A.; Timmermans, R.M.A.; Koelemeijer, R.B.A.; De Leeuw, G. Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands. *Atmos. Chem. Phys.* **2009**, *9*, 909–925. [CrossRef]

232. Guo, J.P.; Zhang, X.Y.; Che, H.Z.; Gong, S.L.; An, X.; Cao, C.X.; Guang, J.; Zhang, H.; Wang, Y.Q.; Zhang, X.C.; et al. Correlation between PM concentrations and aerosol optical depth in eastern China. *Atmos. Environ.* **2009**, *43*, 5876–5886. [CrossRef]

233. Gupta, P.; Christopher, S.A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: Multiple regression approach. *J. Geophys. Res.* **2009**, *114*, D14205. [CrossRef]

234. Gupta, P.; Christopher, S.A. Particulate matter air quality assessment using integrated surface, satellite, and meteorological products: 2. A neural network approach. *J. Geophys. Res.* **2009**, *114*, D20205. [CrossRef]

235. Zhang, H.; Hoff, R.M.; Engel-Cox, J.A. The relation between Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth and PM2.5 over the United States: A geographical comparison by US Environmental Protection Agency regions. *J. Air Waste Man. Assoc.* **2009**, *59*, 1358–1369. [CrossRef]

236. Wang, Z.; Chen, L.; Tao, J.; Zhang, Y.; Su, L. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. *Rem. Sens. Environ.* **2010**, *114*, 50–63. [CrossRef]
237. Lee, J.; Kim, J.; Song, C.H.; Ryu, J.H.; Ahn, Y.H.; Song, C.K. Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager. *Rem. Sens. Environ.* **2010**, *114*, 1077-1088. [CrossRef]

238. Philip, S.; Martin, R.V.; van Donkelaar, A.; Lo, J.W.H.; Wang, Y.; Chen, D.; Zhang, L.; Kasibhatla, P.S.; Wang, S.; Zhang, Q.; et al. Global chemical composition of ambient fine particulate matter for exposure assessment. *Environ. Sci. Technol.* **2014**, *48*, 13060–13068. [CrossRef]

239. Xin, J.; Zhang, Q.; Wang, L.; Gong, C.; Wang, Y.; Liu, Z.; Gao, W. The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011. *Atmos. Res.* **2014**, *138*, 179–188. [CrossRef]

240. McClure, C.D.; Jaffe, D.A. US particulate matter air quality improves except in wildfire-prone areas. *Proc. Natl. Acad. Sci. USA* **2018**, *115*, 7901–7906. [CrossRef]

241. Lahoz, W.A.; Schneider, P. Data assimilation: Making sense of Earth Observation. *Front. Environ. Sci.* **2014**, *2*, 16. [CrossRef]

242. Hyer, E.J.; Reid, J.S.; Zhang, J. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals. *Atmos. Meas. Tech.* **2011**, *4*, 379–408. [CrossRef]

243. Liu, Z.; Liu, Q.; Lin, H.-C.; Schwartz, C.S.; Lee, Y.-H.; Wang, T. Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia. *J. Geophys. Res.* **2011**, *116*, D23206. [CrossRef]

244. Schutgens, N.A.J.; Miyoshi, T.; Takemura, T.; Nakajima, T. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model. *Atmos. Phys. Chem.* **2010**, *10*, 2561–2576. [CrossRef]

245. Huneeus, N.; Chevallier, F.; Boucher, O. Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model. *Atmos. Chem. Phys.* **2012**, *12*, 4585–4606. [CrossRef]

246. Lynch, P.; Reid, J.S.; Westphal, D.L.; Zhang, J.; Hogan, T.F.; Hyer, E.J.; Curtis, C.A.; Hegg, D.A.; Shi, Y.; Campbell, J.R.; et al. An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences. *Geosci. Model Dev.* **2016**, *9*, 1489–1522. [CrossRef]

247. Gelaro, R. and Coauthors. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). *J. Clim.* **2017**, *30*, 5419–5454. [CrossRef]

248. Vermote, E.F.; El Saleous, N.; Justice, C.O.; Kaufman, Y.J.; Privette, J.L.; Remer, L.; Roger, J.C.; Tanré, D. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation. *J. Geophys. Res.* **1997**, *102*, 17131–17141. [CrossRef]

249. Vermote, E.F.; El Saleous, N.Z.; Justice, C.O. Atmospheric correction of MODIS data in the visible to middle infrared: First results. *Rem. Sens. Environ.* **2002**, *83*, 97–111. [CrossRef]

250. Vermote, E.F.; Kotchenova, S. Atmospheric correction for the monitoring of land surfaces. *J. Geophys. Res.* **2008**, *113*, D23S90. [CrossRef]

251. Vermote, E.; Justice, C.; Claverie, M. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. *Rem. Sens. Environ.* **2016**, *185*, 46–56. [CrossRef] [PubMed]

252. Veefkind, J.P.; De Leeuw, G.; Durkee, P.A. Retrieval of aerosol optical depth over land using two-angle view satellite radiometry during TARFOX. *Geophys. Res. Lett.* **1998**, *25*, 3135–3138. [CrossRef]

253. Gonzalez, C.R.; Veefkind, J.P.; de Leeuw, G. Aerosol optical depth over Europe in August 1997 derived from ATSR-2 data. *Geophys. Res. Lett.* **2000**, *27*, 955–958. [CrossRef]

254. Holzer-Popp, T.; Schroedter, M.; Gesell, G. Retrieving aerosol optical depth and type in the boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR-2 radiometer measurements, 1, Method description. *J. Geophys. Res.* **2002**, *107*, 4578. [CrossRef]

255. Kim, M.; Kim, J.; Jeong, U.; Kim, W.; Hong, H.; Holben, B.; Eck, T.F.; Lim, J.H.; Song, C.K.; Lee, S.; et al. Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS). *Atmos. Chem. Phys.* **2016**, *16*, 1789–1808. [CrossRef]

256. Lee, H.J.; Liu, Y.; Coull, B.A.; Schwartz, J.; Koutrakis, P. A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations. *Atmos. Chem. Phys.* **2011**, *11*. [CrossRef]
237. Lee, J.; Kim, J.; Song, C.H.; Ryu, J.H.; Ahn, Y.H.; Song, C.K. Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager. *Rem. Sens. Environ.* **2010**, *114*, 1077-1088. [CrossRef]

238. Philip, S.; Martin, R.V.; van Donkelaar, A.; Lo, J.W.H.; Wang, Y.; Chen, D.; Zhang, L.; Kasibhatla, P.S.; Wang, S.; Zhang, Q.; et al. Global chemical composition of ambient fine particulate matter for exposure assessment. *Environ. Sci. Technol.* **2014**, *48*, 13060–13068. [CrossRef]

239. Xin, J.; Zhang, Q.; Wang, L.; Gong, C.; Wang, Y.; Liu, Z.; Gao, W. The empirical relationship between the PM2.5 concentration and aerosol optical depth over the background of North China from 2009 to 2011. *Atmos. Res.* **2014**, *138*, 179–188. [CrossRef]

240. McClure, C.D.; Jaffe, D.A. US particulate matter air quality improves except in wildfire-prone areas. *Proc. Natl. Acad. Sci. USA* **2018**, *115*, 7901–7906. [CrossRef]

241. Lahoz, W.A.; Schneider, P. Data assimilation: Making sense of Earth Observation. *Front. Environ. Sci.* **2014**, *2*, 16. [CrossRef]

242. Hyer, E.J.; Reid, J.S.; Zhang, J. An over-land aerosol optical depth data set for data assimilation by filtering, correction, and aggregation of MODIS Collection 5 optical depth retrievals. *Atmos. Meas. Tech.* **2011**, *4*, 379–408. [CrossRef]

243. Liu, Z.; Liu, Q.; Lin, H.-C.; Schwartz, C.S.; Lee, Y.-H.; Wang, T. Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia. *J. Geophys. Res.* **2011**, *116*, D23206. [CrossRef]

244. Schutgens, N.A.J.; Miyoshi, T.; Takemura, T.; Nakajima, T. Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model. *Atmos. Phys. Chem.* **2010**, *10*, 2561–2576. [CrossRef]

245. Huneeus, N.; Chevallier, F.; Boucher, O. Estimating aerosol emissions by assimilating observed aerosol optical depth in a global aerosol model. *Atmos. Chem. Phys.* **2012**, *12*, 4585–4606. [CrossRef]

246. Lynch, P.; Reid, J.S.; Westphal, D.L.; Zhang, J.; Hogan, T.F.; Hyer, E.J.; Curtis, C.A.; Hegg, D.A.; Shi, Y.; Campbell, J.R.; et al. An 11-year global gridded aerosol optical thickness reanalysis (v1.0) for atmospheric and climate sciences. *Geosci. Model Dev.* **2016**, *9*, 1489–1522. [CrossRef]

247. Gelaro, R. and Coauthors. The Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). *J. Clim.* **2017**, *30*, 5419–5454. [CrossRef]

248. Vermote, E.F.; El Saleous, N.; Justice, C.O.; Kaufman, Y.J.; Privette, J.L.; Remer, L.; Roger, J.C.; Tanré, D. Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation. *J. Geophys. Res.* **1997**, *102*, 17131–17141. [CrossRef]

249. Vermote, E.F.; El Saleous, N.Z.; Justice, C.O. Atmospheric correction of MODIS data in the visible to middle infrared: First results. *Rem. Sens. Environ.* **2002**, *83*, 97–111. [CrossRef]

250. Vermote, E.F.; Kotchenova, S. Atmospheric correction for the monitoring of land surfaces. *J. Geophys. Res.* **2008**, *113*, D23S90. [CrossRef]

251. Vermote, E.; Justice, C.; Claverie, M. Preliminary analysis of the performance of the Landsat 8/OLI land surface reflectance product. *Rem. Sens. Environ.* **2016**, *185*, 46–56. [CrossRef] [PubMed]

252. Veefkind, J.P.; De Leeuw, G.; Durkee, P.A. Retrieval of aerosol optical depth over land using two-angle view satellite radiometry during TARFOX. *Geophys. Res. Lett.* **1998**, *25*, 3135–3138. [CrossRef]

253. Gonzalez, C.R.; Veefkind, J.P.; de Leeuw, G. Aerosol optical depth over Europe in August 1997 derived from ATSR-2 data. *Geophys. Res. Lett.* **2000**, *27*, 955–958. [CrossRef]

254. Holzer-Popp, T.; Schroedter, M.; Gesell, G. Retrieving aerosol optical depth and type in the boundary layer over land and ocean from simultaneous GOME spectrometer and ATSR-2 radiometer measurements, 1, Method description. *J. Geophys. Res.* **2002**, *107*, 4578. [CrossRef]

255. Kim, M.; Kim, J.; Jeong, U.; Kim, W.; Hong, H.; Holben, B.; Eck, T.F.; Lim, J.H.; Song, C.K.; Lee, S.; et al. Aerosol optical properties derived from the DRAGON-NE Asia campaign, and implications for a single-channel algorithm to retrieve aerosol optical depth in spring from Meteorological Imager (MI) on-board the Communication, Ocean, and Meteorological Satellite (COMS). *Atmos. Chem. Phys.* **2016**, *16*, 1789–1808. [CrossRef]

256. Lee, H.J.; Liu, Y.; Coull, B.A.; Schwartz, J.; Koutrakis, P. A novel calibration approach of MODIS AOD data to predict PM 2.5 concentrations. *Atmos. Chem. Phys.* **2011**, *11*. [CrossRef]
257. Choi, M.; Kim, J.; Lee, J.; Kim, M.; Park, Y.-J.; Jeong, U.; Kim, W.; Hong, H.; Holben, B.; Eck, T.F.; et al. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign. *Atmos. Meas. Tech.* **2016**, *9*, 1377–1398. [CrossRef]

258. Choi, M.; Kim, J.; Lee, J.; Kim, M.; Park, Y.J.; Holben, B.; Song, C.H. GOCI Yonsei aerosol retrieval version 2 products: An improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. *Atmos. Meas. Tech.* **2018**, *11*, 385–408. [CrossRef]

259. Lim, H.; Choi, M.; Kim, J.; Kasai, Y.; Chan, P.W. AHI/Himawari-8 Yonsei aerosol retrieval (YAER): Algorithm, validation and merged products. *Rem. Sens.* **2018**, *10*, 699. [CrossRef]

260. Kim, J.; Yoon, J.-M.; Ahn, M.H.; Sohn, B.J.; Lim, H.S. Retrieving aerosol optical depth using visible and mid-IR channels from geostationary satellite MTSAT-1R. *Int. J. Rem. Sens.* **2008**, *29*, 6181–6192. [CrossRef]

261. Jackson, J.M.; Liu, H.Q.; Laszlo, I.; Kondragunta, S.; Remer, L.A.; Huang, J.F.; Huang, H.C. Suomi-NPP VIIRS aerosol algorithms and data products. *J. Geophys. Res. Atmos.* **2013**, *118*, 12673–12689. [CrossRef]

262. Laszlo, I.; Liu, H. EPS Aerosol Optical Depth (AOD) Algorithm Theoretical Basis Document. Version 3.0.1 NOAA NESDIS Center for Satellite Applications and Research. 2016. Available online: https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD EPS Aerosol AOD v3.0.1.pdf (accessed on 28 July 2020).

263. Hsu, N.C.; Lee, J.; Sayer, A.M.; Kim, W.; Bettenhausen, C.; Tsay, S.-C. VIIRS Deep Blue aerosol products over land: Extending the EOS long-term aerosol data records. *J. Geophys. Res. Atmos.* **2019**, *124*, 4026–4053. [CrossRef]

264. Lyapustin, A.; Wang, Y.; Laszlo, I.; Kahn, R.; Korkin, S.; Remer, L.; Levy, R.; Reid, J.S. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. *J. Geophys. Res.* **2011**, *116*, D03211. [CrossRef]

265. Holzer-Popp, T.; de Leeuw, G.; Griesfeller, J.; Martynenko, D.; Klüser, L.; Bevan, S.; Davies, W.; Ducos, F.; Deuzé, J.L.; Graigner, R.G.; et al. Aerosol retrieval experiments in the ESA aerosol cci project. *Atmos. Meas. Tech.* **2013**, *6*, 1919–1957. [CrossRef]

266. De Leeuw, G.; Holzer-Popp, T.; Bevan, S.; Davies, W.H.; Descloitres, J.; Grainger, R.G.; Griesfeller, J.; Heckel, A.; Kinne, S.; Klüser, L.; et al. Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis. *Rem. Sens. Environ.* **2015**, *162*, 295–315. [CrossRef]

267. Herman, M.; Deuze, J.-L.; Marchant, A.; Roger, B.; Lallart, P. Aerosol remote sensing from POLDER/ADEOS over the ocean: Improved retrieval using a nonspherical particle model. *J. Geophys. Res.* **2005**, *110*, D10S02. [CrossRef]

268. Tanre, D.; Breón, F.M.; Deuze, J.L.; Dubovik, O.; Ducos, F.; Francois, P.; Goloub, P.; Herman, M.; Liffermann, A.; Waquet, F. Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: The PARASOL mission. *Atmos. Meas. Tech.* **2011**, *4*, 1383–1395. [CrossRef]

269. Torres, O.; Tanskanen, A.; Veihelmann, B.; Ahn, C.; Braak, R.; Bhartia, P.K.; Veefkind, P.; Levelt, P. Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview. *J. Geophys. Res.* **2007**, *112*, D24S47. [CrossRef]

270. Huang, J.; Laszlo, I.; Remer, L.A.; Liu, H.; Zhang, H.; Ciren, P.; Kondragunta, S. Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm. *Atmos. Meas. Tech.* **2018**, *11*, 5813–5825. [CrossRef]

271. Schmit, T.J.; Gunshor, M.M.; Menzel, W.P.; Gurka, J.J.; Li, J.; Bachmeier, A.S. Introducing the next-generation Advanced Baseline Imager on GOES-R. *Bull. Amer. Meteor. Soc.* **2005**, *86*, 1079–1096. [CrossRef]

272. Werdell, P.J.; Behrenfeld, M.J.; Bontempi, P.S.; Boss, E.; Cairns, B.; Davis, G.T.; Franz, B.A.; Gliese, U.B.; Gorman, E.T.; Hasekamp, O.; et al. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: Status, science, advances. *Bull. Amer. Meteor. Soc.* **2019**, *100*, 1775–1794. [CrossRef]

<figure><img src="image_18.png" /></figure>
257. Choi, M.; Kim, J.; Lee, J.; Kim, M.; Park, Y.-J.; Jeong, U.; Kim, W.; Hong, H.; Holben, B.; Eck, T.F.; et al. GOCI Yonsei Aerosol Retrieval (YAER) algorithm and validation during the DRAGON-NE Asia 2012 campaign. *Atmos. Meas. Tech.* **2016**, *9*, 1377–1398. [CrossRef]

258. Choi, M.; Kim, J.; Lee, J.; Kim, M.; Park, Y.J.; Holben, B.; Song, C.H. GOCI Yonsei aerosol retrieval version 2 products: An improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia. *Atmos. Meas. Tech.* **2018**, *11*, 385–408. [CrossRef]

259. Lim, H.; Choi, M.; Kim, J.; Kasai, Y.; Chan, P.W. AHI/Himawari-8 Yonsei aerosol retrieval (YAER): Algorithm, validation and merged products. *Rem. Sens.* **2018**, *10*, 699. [CrossRef]

260. Kim, J.; Yoon, J.-M.; Ahn, M.H.; Sohn, B.J.; Lim, H.S. Retrieving aerosol optical depth using visible and mid-IR channels from geostationary satellite MTSAT-1R. *Int. J. Rem. Sens.* **2008**, *29*, 6181–6192. [CrossRef]

261. Jackson, J.M.; Liu, H.Q.; Laszlo, I.; Kondragunta, S.; Remer, L.A.; Huang, J.F.; Huang, H.C. Suomi-NPP VIIRS aerosol algorithms and data products. *J. Geophys. Res. Atmos.* **2013**, *118*, 12673–12689. [CrossRef]

262. Laszlo, I.; Liu, H. EPS Aerosol Optical Depth (AOD) Algorithm Theoretical Basis Document. Version 3.0.1 NOAA NESDIS Center for Satellite Applications and Research. 2016. Available online: https://www.star.nesdis.noaa.gov/jpss/documents/ATBD/ATBD EPS Aerosol AOD v3.0.1.pdf (accessed on 28 July 2020).

263. Hsu, N.C.; Lee, J.; Sayer, A.M.; Kim, W.; Bettenhausen, C.; Tsay, S.-C. VIIRS Deep Blue aerosol products over land: Extending the EOS long-term aerosol data records. *J. Geophys. Res. Atmos.* **2019**, *124*, 4026–4053. [CrossRef]

264. Lyapustin, A.; Wang, Y.; Laszlo, I.; Kahn, R.; Korkin, S.; Remer, L.; Levy, R.; Reid, J.S. Multiangle implementation of atmospheric correction (MAIAC): 2. Aerosol algorithm. *J. Geophys. Res.* **2011**, *116*, D03211. [CrossRef]

265. Holzer-Popp, T.; de Leeuw, G.; Griesfeller, J.; Martynenko, D.; Klüser, L.; Bevan, S.; Davies, W.; Ducos, F.; Deuzé, J.L.; Graigner, R.G.; et al. Aerosol retrieval experiments in the ESA aerosol cci project. *Atmos. Meas. Tech.* **2013**, *6*, 1919–1957. [CrossRef]

266. De Leeuw, G.; Holzer-Popp, T.; Bevan, S.; Davies, W.H.; Descloitre, J.; Grainger, R.G.; Griesfeller, J.; Heckel, A.; Kinne, S.; Klüser, L.; et al. Evaluation of seven European aerosol optical depth retrieval algorithms for climate analysis. *Rem. Sens. Environ.* **2015**, *162*, 295–315. [CrossRef]

267. Herman, M.; Deuze, J.-L.; Marchant, A.; Roger, B.; Lallart, P. Aerosol remote sensing from POLDER/ADEOS over the ocean: Improved retrieval using a nonspherical particle model. *J. Geophys. Res.* **2005**, *110*, D10S02. [CrossRef]

268. Tanre, D.; Breón, F.M.; Deuze, J.L.; Dubovik, O.; Ducos, F.; Francois, P.; Goloub, P.; Herman, M.; Liffermann, A.; Waquet, F. Remote sensing of aerosols by using polarized, directional and spectral measurements within the A-Train: The PARASOL mission. *Atmos. Meas. Tech.* **2011**, *4*, 1383–1395. [CrossRef]

269. Torres, O.; Tanskanen, A.; Veihelmann, B.; Ahn, C.; Braak, R.; Bhartia, P.K.; Veefkind, P.; Levelt, P. Aerosols and surface UV products from Ozone Monitoring Instrument observations: An overview. *J. Geophys. Res.* **2007**, *112*, D24S47. [CrossRef]

270. Huang, J.; Laszlo, I.; Remer, L.A.; Liu, H.; Zhang, H.; Ciren, P.; Kondragunta, S. Screening for snow/snowmelt in SNPP VIIRS aerosol optical depth algorithm. *Atmos. Meas. Tech.* **2018**, *11*, 5813–5825. [CrossRef]

271. Schmit, T.J.; Gunshor, M.M.; Menzel, W.P.; Gurka, J.J.; Li, J.; Bachmeier, A.S. Introducing the next-generation Advanced Baseline Imager on GOES-R. *Bull. Amer. Meteor. Soc.* **2005**, *86*, 1079–1096. [CrossRef]

272. Werdell, P.J.; Behrenfeld, M.J.; Bontempi, P.S.; Boss, E.; Cairns, B.; Davis, G.T.; Franz, B.A.; Gliese, U.B.; Gorman, E.T.; Hasekamp, O.; et al. The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission: Status, science, advances. *Bull. Amer. Meteor. Soc.* **2019**, *100*, 1775–1794. [CrossRef]

<figure><img src="image_17.png" /></figure>