AERONET Aerosol Optical Depth Research Articles

Spatiotemporal analysis of ground and satellite-based aerosol for air quality assessment in the Southeast Asia region

Satellite observations for regional air quality assessment rely on comprehensive spatial coverage, and daily monitoring with reliable, cloud-free data quality. We investigated spatiotemporal variation and data quality of two global satellite Aerosol Optical Depth (AOD) products derived from MODIS and VIIRS imagery. AOD is considered an essential atmospheric parameter strongly related to ground Particulate Matter (PM) in Southeast Asia (SEA). We analyze seasonal variation, urban/rural area influence, and biomass burning effects on atmospheric pollution. Validation indicated a strong relationship between AERONET ground AOD and both MODIS AOD (R2 = 0.81) and VIIRS AOD (R2 = 0.68). The monthly variation of satellite AOD and AERONET AOD reflects two seasonal trends of air quality separately for mainland countries including Myanmar, Laos, Cambodia, Thailand, Vietnam, and Taiwan, Hong Kong, and for maritime countries consisting of Indonesia, Philippines, Malaysia, Brunei, Singapore, and Timor Leste. The mainland SEA has a pattern of monthly AOD variation in which AODs peak in March/April, decreasing during wet season from May–September, and increasing to the second peak in October. However, in maritime SEA, AOD concentration peaks in October. The three countries with the highest annual satellite AODs are Singapore, Hong Kong, and Vietnam. High urban population proportions in Singapore (40.7%) and Hong Kong (21.6%) were associated with high AOD concentrations as expected. AOD values in SEA urban areas were a factor of 1.4 higher than in rural areas, with respective averages of 0.477 and 0.336. The AOD values varied proportionately to the frequency of biomass burning in which both active fires and AOD peak in March/April and September/October. Peak AOD in September/October in some countries could be related to pollutant transport of Indonesia forest fires. This study analyzed satellite aerosol product quality in relation to AERONET in SEA countries and highlighted framework of air quality assessment over a large, complicated region.

On the opposite seasonality of MODIS AOD and surface PM2.5 over the Northern China plain

Aerosol Vertical Structure (AVS) retrieved from CALIOP was used to resolve contradictory seasonality between MODIS aerosol optical depth (AOD) data and surface PM2.5 over the Northern China Plain (NCP). CALIOP level 2 data was used to determine seasonally averaged day and night time AVS during 2011–2015 at wavelengths of 532 and 1064 nm. Results showed that the AVS differed markedly between seasons. It was the most complex during summer days at 532 nm, when a nose like peak was apparent between 500 and 1000 m. This structure was attributed to strong aerosol hygroscopic growth, and accounted for approximately 40% of column AOD. The lowest section of AVS at 532 nm which is important for surface PM2.5 showed the highest during winter, an improved seasonality compared to column AOD; however, discrepancies were apparent during other seasons. Conversely, AVS at 1064 nm was virtually identical to observed PM2.5, implying that coarse particulates are more representative of PM2.5 in the NCP as compared to fine particulates. Thus, MODIS AOD at 550 nm may not be a good proxy for surface PM2.5 retrieval. Using CALIOP active detection, day and night time AOD were obtained, and showed that seasonality was more reasonable at night as compared to during the day. Comparisons of reconstructed daytime CALIOP AOD with MODIS AOD and AERONET AOD implied that winter AOD over the NCP was not as low as indicated by MODIS AOD.

Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

Discrimination of aerosol types over the Tehran city using 5years (2011-2015) of MODIS collection 6 aerosol products.

Tehran, Iran, is an interesting location for aerosol studies because it is affected by anthropogenic pollution and desert dust aerosols. The aim of this study was to discriminate the aerosol types using satellite data over the city. The study was performed using Level-2 daily Aerosol Optical Depth (AOD) and Ångström Exponent (AE) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the Terra and Aqua satellites for the years 2011 to 2015. As the Deep Blue (DB) AE retrievals are more reliable than the Dark Target (DT) AE retrievals, the study was performed using DB data. The number of granules with successful retrievals (at least in two pixels with AODs >0.2 over Tehran with high quality assurance) was 200, which indicates that aerosols could be observed in 5.47% (200 from 3652 of Terra and Aqua granules) of the overpasses during the study period. The maximum and minimum values of AOD occurred during May (0.32 ± 0.27) and August (0.18 ± 0.07), respectively. Based on the AOD vs. AE data, aerosols were classified into three different categories: urban/industry (UI), Desert Dust (DD) and Mixed (Mix). To improve the accuracy of the aerosol classification, the analysis was limited to retrievals with AOD values larger than 0.2. The DD, UI and Mix types had 48.5%, 30.5% and 21% contribution in the aerosol days, respectively. The maximum DD frequency was observed in the spring and summer seasons, while the UI type had its maximum during the cold season. The AOD of the DD type (over Tehran) correlated well with the AOD observations done at the Aerosol Robotic Network (AERONET) site in Zanjan (300km northwest from Tehran). For the UI type, no relationship with the AERONET AOD was detected. This gives confidence in our aerosol typing as the contribution of dust in the aerosol load is mainly from long-range transport, whereas the urban aerosols originate from local sources. Back trajectories ending in Tehran show that the northeast and west trajectories are two main transport routes for the dust to the study area.

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

AbstractAs a new generation of polar‐orbiting satellites, NPP VIIRS (National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite) provides important supports for the studies of local and global scale climate change, environmental monitoring, and radiant energy balance. This paper proposed an improved deep blue aerosol retrieval algorithm over complex regions based on NPP VIIRS observations. Two improvements were proposed and implemented to the traditional aerosol optical depth (AOD) retrieval algorithm in order to improve the retrieval accuracy, including the construction of the surface reflectance conversion model and aerosol optical parameter acquisition. The retrieval results were extensively evaluated. First, the accuracy of the algorithm was evaluated using AErosol RObotic NETwork ground‐based observations, including conditions over different underlying surfaces, different seasons, and different AOD values. Then, the cross validation was carried out between our AOD retrievals and NPP VIIRS aerosol products. The verification results show that our AOD retrievals agree very well with the AErosol RObotic NETwork AOD with coefficient of determination (R2) ~ 0.85. The improved deep blue algorithm performs well overall under different surface conditions and seasons, while the quality of performance gradually decreases with the increase of AOD. Moreover, the improved algorithm is robust with less bias, root‐mean‐square error, and relative mean bias in the AOD retrievals and can provide more detailed aerosol information compared with the NPP VIIRS aerosol products. These results suggest that the improved deep blue algorithm can obtain high‐precision AOD information, providing better data basis for related researches on air pollution and climate change.

Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land

This study evaluates the long-term Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 3 atmospheric aerosol products over land. For this purpose, three monthly aerosol optical depth (AOD) datasets, including Dark Target (DT), Deep Blue (DB) and combined DT and DB (DTB) during 2003–2017, are collected. Aerosol Robotic Network (AERONET) Version 2 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 431 sites around the world are selected for comparison. This study attempts to provide a better understanding of the different MODIS products for their applicability at multiple spatial scales and their suitability for representing the long-term trend of aerosol characteristics. Experiments are performed with direct comparisons between MODIS retrievals and AERONET measurements at global, local to site scales. Meanwhile, the spatial and temporal variations are also compared and discussed. Our results illustrate that C6.1 MODIS AOD retrievals are well correlated with AERONET AOD measurements globally, while the DTB product performs best at most regions, yet the DB product is superior at the site scale. In general, Terra AOD products always overestimate while Aqua AOD products are more accurate in describing the annual mean AOD loadings over land. For long-term aerosol trends, there are small differences between Terra and Aqua aerosol products. Among the three aerosol datasets, neither one can consistently outperform the others in both spatial and temporal aerosol variations over land. In general, DTB products can more accurately capture the correct aerosol changes and are strongly recommended for selection in related aerosol studies at the global scale.

Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. CapsuleIn comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage.

Updated MISR over-water research aerosol retrieval algorithm – Part 2: A multi-angle aerosol retrieval algorithm for shallow, turbid, oligotrophic, and eutrophic waters

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient-rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll a concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Ångström exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD < 1.0, the root-mean-square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD > 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Ångström exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Evaluation and modification of SARA high-resolution AOD retrieval algorithm during high dust loading conditions over bright desert surfaces

Many studies have been carried out for the validation of Aerosol Optical Depth (AOD) retrieved using MODerate resolution Imaging Spectroradiometer (MODIS) satellite data at the spatial resolution of several to several tens of km. Most of the validation efforts have focused on urban areas with fewer studies focused on deserts with high amounts of dust. On the other hand, calculating AOD at a high spatial resolution over desert surfaces is of high interest to identify local and accurate dust source regions. In this study, two different high-resolution MODIS AOD retrieval algorithms were evaluated to investigate the nature of high dust loading events over the Middle East region. These algorithms include the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution based on the MOD09 Level 2 daily (original SARAMOD09) and MCD43A4 16 day (modified SARAMCD43) surface reflectance products. These algorithms were validated using AERONET AOD observations at Kuwait University, and accuracy was evaluated using descriptive statistics and Expected Error (EE) over land. Also, the results of these algorithms were evaluated with the Collection 6 (C6) Deep Blue algorithm at 10 km spatial resolution (DB10km). Results show that the SARAMCD43 and SARAMOD09 AOD retrievals are well correlated (r) with AERONET data (SARAMCD43 = 0.89 and SARAMOD09 = 0.84), with a larger number of retrievals falling within the Expected Error (SARAMCD43 = 86% and SARAMOD09 = 77%) than the DB (DB10km = 60%) AOD product. The SARAMCD43 AOD exhibited good agreement with DB10km AOD over desert surfaces areas during high aerosol loading events, with an average correlation coefficient of 0.92, RMSE of 0.09, and mean absolute error (MAE) of 0.08. While SARAMOD09 AOD over background desert areas is similar to SARAMCD43, significant differences were observed over high aerosol loading areas and SARAMOD09 dramatically underestimates AOD. The results demonstrate that: (i) the SARA algorithm (regardless of surface reflectance data used) is more robust than the MODIS AOD products (both DT and DB), and is better able to represent spatial dust aerosol conditions with an accurate AOD over desert areas over a range of low to high dust pollutant levels; (ii) SARAMCD43 is well correlated with the DB AOD observations and provides AOD with much greater accuracy and reliability than SARAMOD09 over desert areas during severe dust loading events.

Evaluation of Landsat-8 and Sentinel-2A aerosol optical depth retrievals across Chinese cities and implications for medium spatial resolution urban aerosol monitoring.

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. A year of 30 m Landsat-8 and 10 m Sentinel-2A AOD data retrieved using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities. Stringent selection criteria were used to select contemporaneous data - only satellite and AERONET data acquired within 10 minutes were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

Evaluation of MODIS and two reanalysis aerosol optical depth products over AERONET sites

This study selects 400 sites from Aerosol Robotic Network (AERONET) over 2002–2015 to develop an aerosol optical depth (AOD) evaluation dataset, which is used to evaluate two reanalysis AOD datasets (MERRAero and MERRA-2) from Modern-Era Retrospective analysis for Research and Applications (MERRA) projects, and satellite AOD datasets from Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) products. Each AOD dataset is first evaluated individually, and then, a comparison of the MODIS, MERRAero and MERRA-2 datasets is conducted. The results show that the reanalysis MERRA-2 AOD dataset has a comparable accuracy (RMSE = 0.119, MBE = −0.008) with the satellite MODIS AOD datasets (RMSE = 0.110, MBE = 0.011). The MERRA-2 AOD dataset has similar spatial distributions with MODIS data and can capture the dynamic changes of AOD temporally. Compared to MERRAero, the MERRA-2 product improves, but it still slightly underestimates AERONET AOD in general. The MODIS AOD datasets perform differently compared to AERONET AOD due to different algorithms and spatial resolutions. Compared to AERONET AOD, the MODIS AOD datasets generated by the deep blue algorithm are negative biased, while the MODIS AOD datasets from dark target algorithm and the merged dataset are positively biased. The precisions of the Aqua MODIS AOD datasets are higher than the corresponding Terra MODIS AOD datasets.

Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals

Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-global-averaged uncertainties in the DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval, we created a research algorithm and applied it to 80 appropriate MODIS granules during the event. Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5∘ grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm.

Applicability of MODIS C006 Aerosol Products in a Typical Environmental Area of the Beijing-Tianjin-Hebei Region

By fitting with the aerosol optical depth (AOD) from AERONET ground observations at sites in Beijing, Xianghe, and Xinglong with different environmental backgrounds, MODIS C051 Dark Target (DT C051), C006 Dark Target (DT C006), C006 Deep Blue (DB C006), and C006 Deep Blue/Dark Target merged AOD products were compared and evaluated to understand their applicability in the Beijing-Tianjin-Hebei region. The main conclusions are as follows:① The comparison of the C051 and C006 algorithms shows that the accuracy of the AOD at the Beijing and Xianghe sites notably improved, while an improvement was not observed at the Xinglong site; the DB C006 AOD is closest to the AERONET AOD at the Beijing site and the DT C006 AOD is closest to the AERONET AOD at the Xianghe site; the combined C006 AOD is closest to the AERONET AOD at the Xinglong site. ② The inversion error of the MODIS DT C006 at the Beijing site is caused by the improper selection of the aerosol model and surface reflectance; the inversion error of the MODIS DB C006 is mainly due to surface reflectance in spring and the aerosol model in winter. ③ Compared with the DT C051 AOD, the effective data coverage of the DT C006 is reduced, but that of DB C006 and the combined C006 increased; the combined C006 AOD data have the largest coverage. The results show that the application of the combined AOD product is best for the Beijing-Tianjin-Hebei region.

Comparative Accuracy Assessment of Combined MODIS and NAAPS Aerosol Optical Depth with AERONET Data over North Africa

Aerosol is one of the important geophysical parameters that determine the earth’s radiation budget, energy balance and hydrological cycle. The “Deep Blue” Moderate Resolution Imaging Spectro-radiometer (MODIS) Aerosol Optical Depth (AOD) retrieval algorithm was designed to complement existing “Dark Target” Ocean and Land algorithms to be able to retrieve AOD over bright land surface. Using level 2 AOD data from five Aerosol Robotic Network (AERONET) stations over the study location of North Africa (0°S - 40°N, 30°W - 60°E), comparative accuracy assessments are made for combined MODIS AOD aboard Terra and Aqua satellites and US Navy Aerosol Analysis and Prediction System (NAAPS) forecast AOD data. The aerosol transport and vertical mixing over the region are investigated at different altitudes up to 3000 m above ground level using Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT). The MODIS validation result shows highest correlation in the Sub-Sahel (0.811) followed by Sahel (0.726) and then Sahara region (0.662). Furthermore, the combined retrieval algorithm of Terra and Aqua MODIS shows statistically significant discrepancies from AERONET AOD values in term of mean, t-test value, index of agreement and fractional error. The comparison of NAAPS predicted soil dust to AERONET AOD fared best in December to February (DJF) season for the Sahel region and June to August (JJA) season for the Sahara when the dust emission and transport are at the peak. However, median ratios of NAAPS to AERONET AOD indicated bias in some island sites in the Atlantic Ocean which may be due to the presence of sea salt over the site. The analysis carried out in this study reveals that both MODIS retrieval algorithm and NAAPS model could be improved by incorporating some local aerosol sources from the study area.

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

Recently, the newest Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products were available with various refinements and improvements made to both the radiation calibration and Dark Target (DT) and Deep Blue (DB) algorithms. A combined DT and DB dataset (DTB) was also added based on piecewise fixed thresholds using the Normalized Difference Vegetation Index (NDVI) for taking advantage of one's merits. This study provides a cross-comparison and evaluation of these Terra MODIS aerosol products with reference to the enhanced ground-based AOD measurements by the Aerosol Robotic Network (AERONET) Level 3 Version 2.0 data at 384 ground stations. Their absolute and relative performance are evaluated in the period of 2013–2017 among the products, as well as between the current (C6.1) and previous (C6) releases. In general, the C6.1 aerosol products are found to be superior over the C6 products for three datasets from all scales, but the differences and improvements are rather non-uniform that varies with region. Overall, the DB AOD products show the best performance in most regions at about half of the sites, especially in Europe and North America. Meanwhile, besides bright surfaces (i.e., deserts and arid/semi-arid areas), DB products match more closely with the AERONET AODs than that of DT over medium or densely vegetated areas. The dependences of retrieval errors illustrate that the performance of three datasets deteriorates as surface reflectance, elevation and aerosol loading increase. However, the DB algorithm remains relatively more stable and less affected by changes in atmospheric and surface conditions. While the merged product using NDVI has some improvements over individual ones in general, worse performance is also shown in many cases. A more optimal method is thus wanting.

An Improved High‐Spatial‐Resolution Aerosol Retrieval Algorithm for MODIS Images Over Land

AbstractMODerate resolution Imaging Spectroradiometer (MODIS) data can play an important role in aerosol retrieval at the global scale due to their short revisit period and long‐term observations. The operational MODIS aerosol optical depth (AOD) products are severely limited in air quality studies at the city or local scales due to their coarse spatial resolutions. Therefore, an improved aerosol retrieval algorithm for MODIS images at 1‐km spatial resolution is proposed in this paper. This algorithm is based on the high‐resolution aerosol retrieval algorithm with a priori land surface reflectance database support (HARLS), which was developed over bright urban areas and was subsequently modified and validated over land. For this study, an eight‐day surface reflectance database and a seasonal aerosol‐type database over land are constructed using the MODIS surface reflectance and aerosol products. Four typical regions (in Europe, North America, Beijing‐Tianjin‐Hebei, and the Sahara) with different underlying surfaces and aerosol types are selected to perform the aerosol retrieval experiments. The AOD retrievals are validated against the AERosol RObotic NETwork (AERONET) version 2 level 2.0 AOD measurements and compared with the operational MODIS AOD product at 3‐km resolution (MOD04_3K). The results show that AOD retrievals adequately match with AERONET AOD measurements, with 79.56%, 72.69%, 74.71%, and 61.01% of the collections falling within the MOD04_3K expected error over land [±(0.05 + 20%)] for each of the above‐listed regions, respectively. The Improved HARLS (I‐HARLS) algorithm performs well overall under different surface conditions, but the data quality gradually decreases with the increase in surface reflectance. Moreover, the I‐HARLS algorithm is robust with less bias and can provide more detailed aerosol spatial distributions than those provided by the MOD04_3K AOD product. These results suggest that the I‐HARLS algorithm can be used for air‐pollution‐ and climate‐related studies at medium or small scales.

Aerosol retrieval algorithm based on adaptive land–atmospheric decoupling for polarized remote sensing over land surfaces

Land–atmospheric decoupling is important for the retrieval accuracy in atmospheric remote sensing. In this study, we developed an algorithm that adaptively decouples the land surface and atmosphere reflected solar radiation and retrieves the aerosol properties (adaptive land–atmospheric decoupling algorithm, ALAD). The ALAD algorithm allows precise global aerosol optical depth (AOD) retrievals. The four sub-procedures in ALAD comprise estimating a first guess for surface properties, aerosol property retrieval, atmospheric correction, and assessing the results. The ALAD algorithm was applied to PARASOL (Polarization & Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar) and the retrieved AODs were compared with the AOD products from the Moderate resolution Imaging Spectroradiometer (MODIS) and AErosol RObotic NETwork (AERONET). ALAD and MODIS retrieved AOD values were with good consistency in terms of their spatial distribution, regardless of the region or the global distribution. The correlation coefficient between the global ALAD and AERONET AOD was 0.875, and about 60.7% of the AODs retrieved by ALAD were within the expected error range of ±(15% AOD + 0.05). The results obtained using ALAD agreed well with the AERONET values. We also investigated the aerosol and land surface properties that might introduce errors into the AOD values retrieved by ALAD. It was found that the value of AOD had the greatest effect on the accuracy of the retrieval results. The errors introduced by the Angstrom exponent and Normalized Difference Vegetation Index varied slightly but they made no significant contributions.

Assessing the Challenges of Surface‐Level Aerosol Mass Estimates From Remote Sensing During the SEAC4RS and SEARCH Campaigns: Baseline Surface Observations and Remote Sensing in the Southeastern United States

AbstractThe Studies of Emissions and Atmospheric Composition, Clouds and Climate Coupling by Regional Surveys (SEAC4RS) campaign conducted in the southeast United States (SEUS) during the summer of 2013 provided a singular opportunity to study local aerosol chemistry and investigate aerosol radiative properties and PM2.5 relationships, focusing on the complexities involved in simplifying the relationship into a linear regression. We utilize three Southeastern Aerosol Research and Characterization network sites and one Environmental Protection Agency Chemical Speciation Network station that afforded simultaneous Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) and aerosol mass, chemistry, and light scattering monitoring. Prediction of AERONET AOD using linear regression of daily‐mean PM2.5 during the SEAC4RS campaign yielded r2 of 0.36–0.53 and highly variable slopes across four sites. There were further reductions in PM2.5 predictive skill using Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi‐angle Imaging SpetroRadiometer (MISR) AOD data, which have shorter correlation lengths and times relative to surface PM2.5. Long‐term trends in aerosol chemistry and optical properties in the SEUS are also investigated and compared to SEAC4RS period data, establishing that the SEUS experienced significant reduction in aerosol mass, corresponding with changes in both aerosol chemistry and optical properties. These changes have substantial impact on the PM2.5‐AOD linear regression relationship and reinforce the need for long‐term aerosol observation stations in addition to concentrated field campaigns.

Accuracy Analysis of the Aerosol Backscatter Coefficient Profiles Derived from the CYY-2B Ceilometer

Ceilometers are originally designed for cloud base height monitoring. Since a few years, the number of ceilometers available worldwide is rapidly increasing, and these simple backscatter lidars are investigated to be used for aerosol research. This study presents an assessment of the potential of CYY-2B ceilometer for the quantitative retrieval of aerosol properties. The signal-to-noise ratio of the ceilometer is calculated, and the effective height of inversion is determined. It is shown that the effective height of the ceilometer for backscatter coefficient profile inversion is 3-4 km at night and about 1.5–2 km during the day, which is lower than that of the micropulse lidar (MPL) system. The accuracy of the backscatter coefficient profiles derived from the CYY-2B ceilometer is analyzed by using the Vaisala CL51 ceilometer, MPL, forward scatter visibility instrument, and aerosol optical depth (AOD) dataset from aerosol robotic network (AERONET). Spectral conversions of the ceilometer’s and lidar’s data are performed using the Ångström exponent estimated by AERONET measurements. A good agreement is found between two ceilometers and the MPL lidar in backscatter coefficient profiles inversion. The AODs agree well with the AERONET AODs during the observation period of small AODs. However, for the period of large AODs, the results are approximately 50%–60% of AERONET AODs. The limited range of extinction integration is the main cause of this problem.

Synergy of AERONET and MODIS AOD products in the estimation of PM2.5 concentrations in Beijing

Satellite aerosol optical depth (AOD) is widely used to estimate particulate matter with aerodynamic diameter ≤2.5 µm (PM2.5) mass concentrations. Polar orbiting satellite retrieval 1–2 times each day is frequently affected by cloud, snow cover or misclassification of heavy pollution. Novel methods are therefore required to improve AOD sampling. Sunphotometer provides much more AODs than satellite at a fixed point. Furthermore, much of the aerosol pollution is regional. Both factors indicate that sunphotometer has great potential for PM2.5 concentration estimation. The spatial representativeness of the Aerosol Robotic Network (AERONET) AOD at Beijing site is investigated by linear regression analysis of 13-year daily paired AODs at each grid from Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua and Beijing AERONET. The result suggests a good correlation for the whole Beijing Administrative region, with regional mean correlation coefficient exceeding 0.73. Pixel AODs are then estimated from AERONET AOD using linear equations, which are verified to have the same accuracy as that of MODIS AOD. Either AOD from MODIS retrieval or estimation from AERONET AOD in the absence of MODIS pixel AOD is finally used to predict PM2.5 concentration. Daily AOD sampling in average is enhanced by 59% in winter when MODIS AODs are very limited. More importantly, synergy of AERONET and MODIS AOD is able to improve the estimation of regional mean PM2.5 concentrations, which indicates this method would play a significant role in monitoring regional aerosol pollution.