Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives

Wei, Xiaoli; Chang, Ni‐Bin; Bai, Kaixu; Gao, Wei

doi:10.1080/10643389.2019.1665944

Cited by 93 publications

(64 citation statements)

References 344 publications

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“…Depending on the types and characteristics of the sensors, different retrieval algorithms have been developed, including contrast reduction [13], Dark Target (DT) [14], Deep Blue (DB) [15,16], Multi-Angle Implementation of Atmospheric Correction (MA-IAC) [17,18], the Simplified Aerosol Retrieval Algorithm (SARA) [19], polarization [20] and machine learning technology [21]. For the retrieval algorithms of AOD, the complexity of the Earth's surface, along with clouds and high reflectance conditions, has important influences on their quality and uncertainty, especially when there are numerous gaps in the data [22].…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

2021

Remote Sensing

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Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

2021

Remote Sensing

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“…More common naturally occurring aerosols can be observed in the form of fog, dust, sea salt spray, biological exudates and grey smoke (biomass burning). Haze, smog and black smoke are typically a result of industrial and transportation activities (Stocker et al, 2014;Wei et al, 2020). Distinct species such as black carbon (BC), organic carbon (OC), sulphates, nitrates, trace elements, sea salt, mineral dust and biological matter suffer atmospheric alteration resulting in different combinations of compounds.…”

Section: Introductionmentioning

confidence: 99%

A novel method of identifying and analysing oil smoke plumes based on synergic satellite data

Mereuță

Ajtai

Radovici

et al. 2021

Preprint

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Abstract. Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analyzed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. One objective of this study is to highlight the importance of satellite remote sensing techniques in identifying these types of events. As opposed to ground stations, satellite data offers access to remote areas all over the globe which would otherwise be very difficult to reach. Satellite data offers access to these events which, as seen in this study, are mainly located in war prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments onboard Terra and Aqua satellites we addressed the temporal evolution of the smoke plume while assessing Lidar specific properties and plume elevation using CALIPSO data. We present several aerosol properties in the form of plume specific averaged values. The MODIS ocean algorithms were successful in retrieving aerosol properties which, on average, ranged from −0.06 to 0.16 for plume specific AOD, −0.18 to 1.25 for Ångström exponent and 0.29 to 1.73 µm for the effective radius. CALIPSO measurements showed values of plume AOD ranging from 0 to 0.14 (532 nm) and 0 to 0.13 (1064 nm) except for one event where AOD values showed 1.52 (532 nm) and 1.42 (1064 nm). AE values ranged from 0.11 to 0.33 which were in agreeance with MODIS values. A large discrepancy can be found in one event where CALIPSO measured AOD values 5 times higher than MODIS. This event also produced the largest lidar ratio at 109 sr (532 nm) and 86 (1064 nm). Other lidar ratio values ranged from 37 to 55 sr however these unconstrained solutions were obtained for the entire layer of which the plumes were a part of and thus did not reflect specific plume conditions. Particulate backscatter values ranged from 0.002 to 0.0017 km−1 sr−1 while extinction coefficient values ranged from 0.10 to 1.65 km−1. On average backscatter and extinction coefficient values were 2 to 9 times higher than local background values. Particulate depolarization ratios ranged from 0.11 to 0.15 in 4 out of 6 cases while the remaining two ranged from 0.27 to 0.32 where dust was highly dominant. The values represented in this study are in good agreement with similar studies that used ground based and flight measurements. We believe that MODIS values are a conservative estimation of plume AOD since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables which do not reflect the highly absorbing nature of these smoke plumes. CALIPSO measurements are heavily dependent on lidar ratios which are not directly measured if plumes within the planetary boundary layer. We also believe that AOD values based on CALIPSO measurements are conservative in nature since heavy absorbing smoke would yield larger lidar ratios and AOD values. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

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“…Therefore, the spatiotemporal differences between different AOD products should be minimized to ensure a more comprehensive representation of natural phenomenon [38]. However, retrieving high-resolution AOD at varying scales is still a challenging task due to the low signal-to-noise ratio in sensing, algorithmic synthesis constraints, downscaling issues, and data gaps resulting from adverse impacts such as cloud contamination [39,40].…”

Section: Introductionmentioning

confidence: 99%

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

Ahn

Chung

et al. 2021

Remote Sensing

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This study aimed to generate a near real time composite of aerosol optical depth (AOD) to improve predictive model ability and provide current conditions of aerosol spatial distribution and transportation across Northeast Asia. AOD, a proxy for aerosol loading, is estimated remotely by various spaceborne imaging sensors capturing visible and infrared spectra. Nevertheless, differences in satellite-based retrieval algorithms, spatiotemporal resolution, sampling, radiometric calibration, and cloud-screening procedures create significant variability among AOD products. Satellite products, however, can be complementary in terms of their accuracy and spatiotemporal comprehensiveness. Thus, composite AOD products were derived for Northeast Asia based on data from four sensors: Advanced Himawari Imager (AHI), Geostationary Ocean Color Imager (GOCI), Moderate Infrared Spectroradiometer (MODIS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Cumulative distribution functions were employed to estimate error statistics using measurements from the Aerosol Robotic Network (AERONET). In order to apply the AERONET point-specific error, coefficients of each satellite were calculated using inverse distance weighting. Finally, the root mean square error (RMSE) for each satellite AOD product was calculated based on the inverse composite weighting (ICW). Hourly AOD composites were generated (00:00–09:00 UTC, 2017) using the regression equation derived from the comparison of the composite AOD error statistics to AERONET measurements, and the results showed that the correlation coefficient and RMSE values of composite were close to those of the low earth orbit satellite products (MODIS and VIIRS). The methodology and the resulting dataset derived here are relevant for the demonstrated successful merging of multi-sensor retrievals to produce long-term satellite-based climate data records.

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Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives

Cited by 93 publications

References 344 publications

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

A novel method of identifying and analysing oil smoke plumes based on synergic satellite data

Composite Aerosol Optical Depth Mapping over Northeast Asia from GEO-LEO Satellite Observations

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