Multi-Angle Implementation Of Atmospheric Correction Aerosol Optical Depth Research Articles

Evaluation and analysis of long-term MODIS MAIAC aerosol products in China

NASA has released the latest Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-Angle Implementation of Atmospheric Correction (MAIAC) Collection 6 (C6) and Collection 6.1 (C6.1) aerosol optical depth (AOD) products with 1 km spatial resolution. This study validated and compared C6 and C6.1 MAIAC AOD products with AERONET observations in terms of accuracy and stability, and analyzed the spatiotemporal characteristics of AOD at different scales in China. The results show that the overall accuracy of MAIAC products is good, with correlation coefficient (R) > 0.9, mean bias (BIAS) < 0.015, and the fraction within the expected error (EE) > 68 %. However, after the algorithm update, the accuracy of Terra MAIAC aerosol products C6.1 has significantly decreased. The accuracy of the products varies with the region. The accuracy of C6.1 in North China, Central East China, and West China, is comparable to or even exceeds that of C6, but performs poorly in South China. In addition, the stability of the updated C6.1 MAIAC aerosol products has not seen significantly improvement. The metrics of no product can all meet the stability goals of the Global Climate Observing System (GCOS, 0.02 per decade) in China. C6.1 improves the retrieval frequency in many regions and temporarily solves the problem of AOD discontinuity at the boundaries of different aerosol models in C6, but there are some fixed climatological AOD blocks (AOD = 0.014) in the eastern Tibetan Plateau region. Both C6 and C6.1 can capture similar annual variation characteristics of AOD to those observed at the AERONET sites. The study provides possible references for improving the MAIAC algorithm and building long-term stable aerosol records.

An intercomparison of SEMARA high-resolution AOD and MODIS operational AODs

SEMARA is a high-resolution aerosol optical depth (AOD) retrieval algorithm that incorporates two algorithms, Simplified and Robust Surface Reflectance Estimation Method (SREM) for estimating surface reflectance and the Simplified Aerosol Retrieval Algorithm (SARA) for retrieving AOD. This study used SEMARA approach for retrieving AOD utilizing data obtained from Aqua-MODIS in the green channel. The algorithm had good agreement with AERONET data (R = 0.98). Also it was analyzed over a wide area containing five provinces in Iran. SEMARA AOD retrievals were compared with the operational MODIS aerosol retrieval algorithms, including Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals over different land covers and low to high aerosol loadings. The results show high consistency in SEMARA AOD retrievals with MAIAC AOD retrievals, with an average correlation coefficient of 0.61, while the correlation coefficient with DB and DT was 0.50 and 0.29, respectively. During high AOD loadings and over bright surfaces, the best agreement of SEMARA AOD and DT, DB, and MAIAC AOD was achieved with correlation coefficients of 0.52, 0.75, and 0.80, respectively. SEMARA AOD shows the best spatial coverage, averaging 80%, while MAIAC, DB, and DT had 54%, 32%, and 12% spatial coverage, respectively. This study concluded that SEMARA AOD can retrieve AOD over various land covers and during low to high AOD loadings with acceptable results.

Long-term validation and error analysis of DB and MAIAC aerosol products over bright surface of China

Two distinct algorithms, namely Deep Blue (DB) and Multiangle Implementation of Atmospheric Correction (MAIAC), based on the Moderate Resolution Imaging Spectroradiometer (MODIS), can provide aerosol optical depth (AOD) products over bright surfaces. Recently, the newest MAIAC Collection 6.1 (C6.1) product was available, which corrected many known deficiencies of C6. Due to the significant impact of high-reflectivity surfaces on the accuracy of aerosol retrieval, long-term validation and comprehensive assessment of AOD over bright surfaces are required at a regional scale. Firstly, this study conducted a systematic evaluation of three products against ground-based Sunphotometer data obtained from 21 sites of the China Aerosol Remote Sensing Network (CARSNET) and 2 sites of the Aerosol Robotic Network (AERONET) from the year 2002 to 2014. The validation results showed a significant underestimation in DB and MAIAC AOD retrievals as indicated by the large percent of retrievals below the expected error (EE) envelope, and significant negative biases. In comparison, both versions of MAIAC products performed better than DB product, which was also revealed by the site scale validation. Besides, the performance of the C6.1 MAIAC was slightly improved over the study region compared to the C6 MAIAC. Finally, this study made an in-depth investigation of the underestimation of AOD products affected by various factors, i.e., aerosol loading, land type, NDVI, surface albedo, and scattering angle. The results indicated that the large negative bias of AOD retrievals for the three products tended to occur in the case of large aerosol loadings and high bright surfaces, which could be attributed to the inaccurate assumption of aerosol models and the insensitivity of top of atmosphere (TOA) reflectance to AOD variations caused by the high reflectivity of the underlying surface. The findings of this study are expected to provide some reference for the improvement of aerosol retrieval algorithms over bright surfaces.

Direct aerosol optical depth retrievals using MODIS reflectance data and machine learning over East Asia

Anthropogenic aerosols have rapidly increased since the industrial revolution and are harmful to human health. Moderate Resolution Imaging Spectroradiometer (MODIS) data are critical for retrieving aerosol properties worldwide. However, current MODIS aerosol optical depth (AOD) products require extensive computations and a precalculated lookup table. This study proposes assumption-free high-resolution AOD retrieval models based on the light gradient boosting machine method using MODIS data and ground-based observations over East Asia. The models were developed with three spatial resolutions: 250 m, 500 m, and 1 km. The results showed that 77.8% of the 250 m AOD values were within the MODIS expected error (EE) range, while 76.5%, 76.3%, and 70.08% of the 500 m, 1 km, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD values were within the EE range, respectively. Furthermore, an analysis of the time series and detailed spatial distribution of the proposed model-derived AOD based on data from the Korea–United States Air Quality campaign demonstrated the excellent quality of the 250 m AOD via further validation using a spatially independent dataset. The Shapley Additive exPlanations analysis identified the sensor zenith angle and top-of-atmosphere reflectance of the blue band as the key contributors to the models. In addition, while MAIAC has limited spatial coverage, the spatial frequency of the proposed direct AOD retrieval was nearly 1.5-times higher than that of the MAIAC AOD. Our findings confirmed that machine learning-based high-resolution AOD estimates can be obtained using only satellite data.

The impact of different aerosol layering conditions on the high-resolution MODIS/MAIAC AOD retrieval bias: The uncertainty analysis

The Eastern Mediterranean/Middle East (EMME) region belongs to one of the most polluted and vulnerable to climate change related areas in the world. To monitor these changes, comprehensive set of measurements need to be conducted. Ground-level monitoring sites provide continuous measurements, yet their spatial coverage in EMME is very limited. Satellite data largely expand spatial coverage, however, retrieval accuracy over arid regions with bright surface background, mixed with urban and industrial sources, remains challenging. In this study, we analyzed the agreement between aerosol optical depth (AOD) data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm at 1-km spatial resolution and corresponding ground-based Aerosol Robotic Network (AERONET) measurements, using data from September 2019 to October 2022 in Tel Aviv, Israel. We identified all overestimation and underestimation MAIAC AOD measurements, relative to AERONET, and used multiwavelength polarization lidar observations of vertical aerosol profiles to characterize these conditions. Our findings suggest that under clear atmospheric conditions (AOD¡0.12), MAIAC overestimation prevails, while under dusty (AOD¿0.3), and partially cloudy conditions, MAIAC underestimation prevails. We found that dust component (high particle depolarization ratio, and low Angstrom exponent) is often presented in cases of underestimation, while a single layer overestimation cases typically related to anthropogenic pollution. The two-layers overestimation cases tend to have marine aerosols at the bottom and mixed pollution sources at the top. Our study highlights the importance of studying the different layering conditions that largely bias the MAIAC AOD retrieval accuracy. This knowledge is highly important since AOD is widely used as input variable in numerous modeling studies and air quality applications and rarely prepossessed for such a bias.

Variation of Aerosol Optical Properties over Cluj-Napoca, Romania, Based on 10 Years of AERONET Data and MODIS MAIAC AOD Product

Aerosols play an important role in Earth’s climate system, and thus long-time ground- based measurements of aerosol optical properties are useful in understanding this role. Ten years of quality-assured measurements between 2010 and 2020 are used to investigate the aerosol climatology in the Cluj-Napoca area, in North-Western Romania. In this study, we analyze the aerosol optical depth (AOD), single scattering albedo (SSA) and angstrom exponent obtained by the CIMEL sun photometer, part of the aerosol robotic network (AERONET), to extract the seasonality of aerosols in the region and investigate the aerosol climatology of the area. Higher aerosol loads are found during July and August. The angstrom exponent has the lowest values in April and May, and the highest in August. The classification of aerosols using AERONET data is performed to separate dust, biomass burning, polluted urban, marine and continental-dominant aerosol mixtures. In addition, the study presents the validation efforts of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) dataset against AERONET AOD over a 10-year period.

Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales

MODIS and VIIRS aerosol products have been used extensively by the scientific community. Products in operation include MODIS Dark Target (DT), Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC) and VIIRS DT, DB, and NOAA Environmental Data Record products. This study comprehensively validated and inter-compared aerosol optical depth (AOD) and Ångstrom exponent (AE) over land and the ocean of these six products (seven different algorithms) on regional and global scales using AErosol RObotic NETwork (AERONET) and Maritime Aerosol Network (MAN) observations. In particular, we used AERONET inversions to classify AOD and AE biases into different scenarios (depending on absorption and particle size) to obtain retrieval error characteristics. The spatial patterns of the products and their differences were also analyzed. Collectively, although six satellite AODs are in good agreement with ground observations, VIIRS DB (land and ocean) and MODIS MAIAC (land only) AODs show better validation metrics globally and better performance in 8/10 world regions. Therefore, they are more recommended for usage. Although land AE retrievals are not capable of quantitative application at both instantaneous and monthly scales, their spatial patterns show qualitative potential. Ocean AE shows a relatively high correlation coefficient with ground measurements (>0.75), meeting the fraction of expected accuracy (> 0.70). Error characteristic analyses emphasize the importance of aerosol particle size and absorption-scattering properties for land retrieval, indicating that improving the representation of aerosol types is necessary. This study is expected to facilitate the usage selection of operating VIIRS and MODIS products and their algorithm improvements.

Nesting Elterman Model and Spatiotemporal Linear Mixed-Effects Model to Predict the Daily Aerosol Optical Depth over the Southern Central Hebei Plain, China

Aerosol optical depth (AOD), an important indicator of atmospheric aerosol load, characterizes the impacts of aerosol on radiation balance and atmospheric turbidity. The nesting Elterman model and a spatiotemporal linear mixed-effects (ST-LME) model, which is referred to as the ST-Elterman retrieval model (ST-ERM), was employed to improve the temporal resolution of AOD prediction. This model produces daily AOD in the Southern Central Hebei Plain (SCHP) region, China. Results show that the ST-ERM can effectively capture the variability of correlations between daily AOD and meteorological variables. After being validated against the daily Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD, the correlation coefficient between daily retrieved AOD from ST-ERM and MAIAC observations in 2017 reached 0.823. The validated Nash–Sutcliffe efficiency (Ef) of daily MAIAC AOD and ST-ERM-retrieved AOD is greater than or equal to 0.50 at 72 of the 95 stations in 2017. The relative error (Er) is less than 14% at all the stations except for Shijiazhuang (17.5%), Fengfeng (17.8%), and Raoyang (30.1%) stations. The ST-ERM significantly outperforms the conventional meteorology–AOD prediction approaches, such as the revised Elterman retrieval model (R-ERM). Thus, the ST-ERM shows great potential for daily AOD estimation in study regions with missingness of data.

Evaluation of MODIS DT, DB, and MAIAC Aerosol Products over Different Land Cover Types in the Yangtze River Delta of China

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) has been widely used in atmospheric environment and climate change research. Based on data of the Aerosol Robotic Network and Sun–Sky Radiometer Observation Network in the Yangtze River Delta, the retrieval accuracies of MODIS C6.1 Dark Target (DT), Deep Blue (DB), and C6.0 Multi-angle Implementation of Atmospheric Correction (MAIAC) products under different land cover types, aerosol types, and observation geometries were analyzed. About 65.64% of MAIAC AOD is within the expected error (Within EE), which is significantly higher than 41.43% for DT and 56.98% for DB. The DT product accuracy varies most obviously with the seasons, and the Within EE in winter is more than three times that in spring. The DB and MAIAC products have low accuracy in summer but high in other seasons. The accuracy of the DT product gradually decreases with the increase in urban and water land-cover proportion. After being corrected by bias and mean relative error, the DT accuracy is significantly improved, and the Within EE increases by 24.12% and 32.33%, respectively. The observation geometries and aerosol types were also examined to investigate their effects on AOD retrieval.

June to October Aerosol Optical Depth over the Arctic at Various Spatial and Temporal Scales in MODIS, MAIAC, CALIOP and GOES Data

The accuracy of the cloud-aerosol lidar with orthogonal polarization (CALIOP), moderate resolution imaging spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC), and Geostationary Operational Environmental Satellite (GOES) aerosol optical depth (AOD) products for the Arctic north of 59.75°N was examined by means of 35 aerosol robotic network (AERONET) AOD sites. The assessment for June to October 2006 to 2020 showed MAIAC AOD agreed the best with AERONET AOD; CALIOP AOD differed the strongest from the AERONET AOD. Cross-correlations of CALIOP AOD along the satellite path indicated that AOD-values 40 km up-and-down the path often failed to represent the AERONET AOD-values within ±30 min of the overpass in this region dominated by easterly winds. Typically, CALIOP AOD was lower than AERONET AOD and MAIAC AOD at the sites, especially, at sites with mean AOD below 0.1. Generally, MODIS AOD values exceeded those of MAIAC. Comparison of CALIOP, MAIAC, and MODIS products resampled on a 0.25° × 0.25° grid revealed differences among the products caused by their temporal and spatial resolution, sample habit and size. Typically, the MODIS AOD-product showed the most details in AOD distribution. Despite differences in AOD-values, all products provided similar temporal evolution of elevated and lower AOD.

Evaluation and Comparison of Spatio-Temporal Relationship between Multiple Satellite Aerosol Optical Depth (AOD) and Near-Surface PM2.5 Concentration over China

Given the advantages of remote sensing, an increasing number of satellite aerosol optical depths (AOD) have been utilized to evaluate near-ground PM2.5. However, the spatiotemporal relationship between AODs and PM2.5 still lacks a comprehensive investigation, especially in some regions with severe pollution within China. Here, we investigated the spatiotemporal relationships between several satellite AODs and the near-surface PM2.5 concentration across China and its 14 representative regions during 2016–2018 using the correlation coefficient (R), the PM2.5/AOD ratio (η), the geo-detector (q), and the different aerosol-dominated regimes. The results showed that the MODIS AOD from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm strongly correlates with PM2.5 (R &gt; 0.6) in China, particularly in the Chengyu (CY), Beijing-Tianjin-Hebei (BTH), and Yangtze River Delta (YRD) regions. The close correlations (R = 0.7) exist between PM2.5 and MODIS and VIIRS AOD from the deep blue (DB) algorithm in the CY, BTH, and YRD regions. Under the key aerosols affecting China (e.g., sulfate and dust), there is a strong correlation (R &gt; 0.5) between the PM2.5 and MODIS and VIIRS AODs from the MAIAC and DB algorithms, with the higher concentration of ground-level PM2.5 per unit of these AODs (η &gt; 130). The MAIAC AOD (Terra/Aqua) can better explain the spatial distribution (q &gt; 0.4) of PM2.5 than those of AODs from the dark target (DT) and DB algorithms applied to the MODIS over China and its specific regions across seasons. The performance of the Advanced Himawari Imager (AHI) AOD (R &gt; 0.5, q &gt; 0.3) was close to that of the MAIAC AOD during the spring and summer; however, it was far less than the MAIAC AOD in the autumn and winter seasons. The investigation provides instructions for estimating the near-surface PM2.5 concentration based on AOD in different regions of China.

Validation and Analysis of MAIAC AOD Aerosol Products in East Asia from 2011 to 2020

East Asia is one of the most important sources of aerosols in the world. The distribution of aerosols varies across time and space. Accurate aerosol data is crucial to identify its spatiotemporal dynamics; thus, it is of great significance to obtain and verify new aerosol data for this region. Based on the Aerosol Optical Depth (AOD) data of the Aerosol Robotic Network (AERONET) program for 17 stations from 2011 to 2020, this study comprehensively verified the accuracy and applicability of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD 1 km products among different seasons, elevations, and climate zones over entire East Asia. The results showed that: (1) The overall accuracy of MAIAC AOD was high in East Asia, and the accuracy of Terra was slightly better than that of Aqua. MAIAC AOD showed significant heterogeneity among sites. MAIAC AOD performed well in areas with high vegetation cover and flat terrain, while the inversion accuracy was relatively low in areas with low vegetation cover and high terrain. (2) In general, MAIAC AOD and AERONET AOD showed good agreement in different seasons, presenting as winter &gt; spring &gt; autumn &gt; summer. Yet the accuracy and consistency of Terra AOD product were better than Aqua product. (3) MAIAC AOD showed different accuracy at different elevations and climate zones. It had a high correlation and best inversion accuracy with AERONET AOD at low and medium elevations. MAIAC AOD had better inversion accuracy in the arid and warm temperate zones than that in the equatorial and cold temperate zones. (4) AOD distribution and its trend showed significant spatial differences in East Asia. The high AOD values were dominant in the Sichuan basin and the eastern plains of China, as well as in India and Bangladesh, while the relatively low AOD values were distributed in southwestern China and the areas north of 40°N. AOD in most parts of East Asia showed a negative trend, indicating a great improvement in air quality in these regions.

Full-coverage 1-km estimates and spatiotemporal trends of aerosol optical depth over Taiwan from 2003 to 2019

Satellite-derived aerosol optical depth (AOD) provides an effective way to investigate global and regional variations in atmospheric aerosols. However, due to cloud cover and surface reflectance, AOD datasets derived from satellite instruments generally have non-random missing values, which introduces additional uncertainty into AOD data and limits its downstream. To remedy this problem, this study used a two-stage approach based on spatial interpolation and a random forest model to fill the gaps in data generated by the Multiangle Implementation of Atmospheric Correction (MAIAC) aerosol retrieval algorithm, which provides the best-available AOD product to the global public. The relationship between ground-level fine particulate matter concentrations and satellite AOD was considered in the modeling. By gap-filling daily 1-km MAIAC AOD data from 2003 to 2019 over Taiwan, the two-stage model achieved comparable accuracy (coefficient of determination = 0.52, root-mean-square error = 0.22) against ground-level AOD measurements to the accuracy that has been achieved by previous studies. Furthermore, it improved daily high-spatial-resolution AOD estimates to 100% of spatial coverage. Comparisons between the full-coverage estimates and MAIAC retrievals showed that the MAIAC AOD dataset generally underestimated monthly/seasonal/annual mean AOD values in Taiwan. We also used the long-term estimates of the daily 1-km AOD dataset with full coverage to explore the spatiotemporal trends in AOD in Taiwan. The practical approach developed in this study is suitable for application in long- and short-term studies of air pollution and its effects on public health.

An Estimation Method for PM2.5 Based on Aerosol Optical Depth Obtained from Remote Sensing Image Processing and Meteorological Factors

Understanding the spatiotemporal variations in the mass concentrations of particulate matter ≤2.5 µm (PM2.5) in size is important for controlling environmental pollution. Currently, ground measurement points of PM2.5 in China are relatively discrete, thereby limiting spatial coverage. Aerosol optical depth (AOD) data obtained from satellite remote sensing provide insights into spatiotemporal distributions for regional pollution sources. In this study, data from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD (1 km resolution) product from Moderate Resolution Imaging Spectroradiometer (MODIS) and hourly PM2.5 concentration ground measurements from 2015 to 2020 in Dalian, China were used. Although trends in PM2.5 and AOD were consistent over time, there were seasonal differences. Spatial distributions of AOD and PM2.5 were consistent (R2 = 0.922), with higher PM2.5 values in industrial areas. The method of cross-dividing the test set by year was adopted, with AOD and meteorological factors as the input variable and PM2.5 as the output variable. A backpropagation neural network (BPNN) model of joint cross-validation was established; the stability of the model was evaluated. The trend in the predicted values of BPNN was consistent with the monitored values; the estimation result of the BPNN with the introduction of meteorological factors is better; coefficient of determination (R2) and RMSE standard deviation (SD) between the predicted values and the monitored values in the test set were 0.663–0.752 and 0.01–0.05 μg/m3, respectively. The BPNN was simpler and the training time was shorter compared with those of a regression model and support vector regression (SVR). This study demonstrated that BPNN could be effectively applied to the MAIAC AOD data to estimate PM2.5 concentrations.

An improved method for retrieving aerosol optical depth using the ground-level meteorological data over the South-central Plain of Hebei Province, China

To retrieve the aerosol optical depth (AOD) from ground-level meteorological measurements at regional scale, a new method, the revised Elterman's retrieval model (R-ERM), was developed based on the meteorological observations to retrieve the AOD. The aerosol scale height (ASH1) algorithm might introduce significant biases into AOD retrieval. Thus, the model enhances the AOD retrieval precision by redefining the ASH1 algorithm. The model was evaluated and validated against the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD data with a 1-km spatial resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) collected over the South-central Plain of Hebei Province region, China for the period of 2016–2017. Results indicate that, with the redefinition of the ASH1 algorithm, the overall the Pearson's correlation coefficient is 0.69 in 2017 between R-ERM and MAIAC AOD, and root mean squared error and the relative error (RE) are 0.20 and 23%, respectively. The evaluation proves that the R-ERM performs previous models, such as Elterman's retrieval model (ERM) with an overall validation R of 0.11 and Qiu's retrieval model (QRM) with an overall validation R of 0.35. The spatial patterns of the retrieved AOD after ordinary Kriging interpolation are consistent with those of the MAIAC datasets. Adding the water vapor pressure parameter significantly improved the estimation accuracy of ASH1, which is a key factor to the AOD retrieval results. The findings from the study demonstrate the great potential and value of the R-ERM for regional AOD retrieval.

A National-Scale 1-km Resolution PM2.5 Estimation Model over Japan Using MAIAC AOD and a Two-Stage Random Forest Model

Satellite-based models for estimating concentrations of particulate matter with an aerodynamic diameter less than 2.5 μm (PM2.5) have seldom been developed in islands with complex topography over the monsoon area, where the transport of PM2.5 is influenced by both the synoptic-scale winds and local-scale circulations compared with the continental regions. We validated Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) with ground observations in Japan and developed a 1-km-resolution national-scale model between 2011 and 2016 to estimate daily PM2.5 concentrations. A two-stage random forest model integrating MAIAC AOD with meteorological variables and land use data was applied to develop the model. The first-stage random forest model was used to impute the missing AOD values. The second-stage random forest model was then utilised to estimate ground PM2.5 concentrations. Ten-fold cross-validation was performed to evaluate the model performance. There was good consistency between MAIAC AOD and ground truth in Japan (correlation coefficient = 0.82 and 74.62% of data falling within the expected error). For model training, the model showed a training coefficient of determination (R2) of 0.98 and a root mean square error (RMSE) of 1.22 μg/m3. For the 10-fold cross-validation, the cross-validation R2 and RMSE of the model were 0.86 and 3.02 μg/m3, respectively. A subsite validation was used to validate the model at the grids overlapping with the AERONET sites, and the model performance was excellent at these sites with a validation R2 (RMSE) of 0.94 (1.78 μg/m3). Additionally, the model performance increased as increased AOD coverage. The top-ten important predictors for estimating ground PM2.5 concentrations were day of the year, temperature, AOD, relative humidity, 10-m-height zonal wind, 10-m-height meridional wind, boundary layer height, precipitation, surface pressure, and population density. MAIAC AOD showed high retrieval accuracy in Japan. The performance of the satellite-based model was excellent, which showed that PM2.5 estimates derived from the model were reliable and accurate. These estimates can be used to assess both the short-term and long-term effects of PM2.5 on health outcomes in epidemiological studies.

MODIS high-resolution MAIAC aerosol product: Global validation and analysis

This paper attempt to verify the accuracy of the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol optical depth (AOD) product on a global scale using AOD measurements during 2000–2019 at 332 Aerosol Robotic Network (AERONET) sites. The results indicate that MAIAC AOD retrievals are highly correlated with ground-based AOD measurements. The correlation coefficients (R) are greater than 0.8 at more than 68% of AERONET sites. Meanwhile, the performance of MAIAC AOD retrievals in different climate zones is also investigated. The accuracy of MAIAC AOD retrievals is high (within expected error (EE) = 87.49% and 83.15%) in the regions of tropical rainforest climate and tropical open forest climate. Then, the uncertainties and estimation deviation of MAIAC AOD retrievals in various land-cover-types are analyzed. The results find that the MAIAC AOD retrievals show high correlation with aerosol measurements in the forest (within EE > 95%), while the poor accuracy is always observed in the barren areas (within EE = 62.56%, RMSE = 0.12). The uncertainty of the MAIAC AOD retrievals is not only related to the satellite and solar geometries, but also depends on the aerosol conditions, which become larger with the increase of aerosol loading and Ångstrom exponent (AE). Finally, the spatial variations of AOD values over the world are investigated. High AOD values are mainly distributed in Central Africa, Saudi Arabia, Southern Asia and China.

Impact of environmental attributes on the uncertainty in MAIAC/MODIS AOD retrievals: A comparative analysis

This work examines the impact of different environmental attributes on the uncertainty in satellite-based Aerosol Optical Depth (AOD) retrieval against the benchmark Aerosol Robotic Network (AERONET) AOD measurements at 21 sites across North Africa, California and Germany, in the years 2007–2017. As a first step, we studied the effects of spatial averaging the Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD retrievals, and of temporal averaging the AERONET AOD around the satellite (Aqua) overpass, on the agreement between the two products. AERONET AOD averaging over a time-window of ±15 min around the satellite overpass and the 1 × 1 km2 spatial grid of MAIAC were found to provide the best AOD retrieval performance. Next, MAIAC AOD were stratified according to different co-measured environmental attributes (aerosol loading, dominant particle size, vegetation cover, and prevailing particle type) and analyzed against the AERONET AOD. The envelope of the expected retrieval error varied considerably among different environmental attributes categories, with more accurate AOD retrievals obtained over highly vegetated areas (i.e. less surface reflectance) than over arid areas. Moreover, the retrieval accuracy was found to be sensitive to the aerosol loading and particle size, with a large bias between the MAIAC and AERONET AOD during high aerosol loading of coarse particles. In addition, the retrieval accuracy of MAIAC AOD was found to depend on the aerosol type due to the aerosol model assumptions regarding their optical properties.

Identification of Aerosol Pollution Hotspots in Jiangsu Province of China

Aerosol optical depth (AOD) is an important atmospheric parameter for climate change assessment, human health, and for total ecological situation studies both regionally and globally. This study used 21-year (2000–2020) high-resolution (1 km) Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm-based AOD from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Terra and Aqua satellites. MAIAC AOD was evaluated against Aerosol Robotic Network (AERONET) data across three sites (Xuzhou-CUMT, NUIST, and Taihu) located in Jiangsu Province. The study also investigated the spatiotemporal distributions and variations in AOD, with associated trends, and measured the impact of meteorology on AOD in the 13 cities of Jiangsu Province. The evaluation results demonstrated a high correlation (r = 0.867~0.929) between MAIAC AOD and AERONET data, with lower root mean squared error (RMSE = 0.130~0.287) and mean absolute error (MAE = 0.091~0.198). In addition, the spatial distribution of AOD was higher (&gt;0.60) in most cities except the southeast of Nantong City (AOD &lt; 0.4). Seasonally, higher AOD was seen in summer (&gt;0.70) than in spring, autumn, and winter, whereas monthly AOD peaked in June (&gt;0.9) and had a minimum in December (&lt;0.4) for all the cities. Frequencies of 0.3 ≤ AOD &lt; 0.4 and 0.4 ≤ AOD &lt; 0.5 were relatively common, indicating a turbid atmosphere, which may be associated with anthropogenic activities, increased emissions, and changes in meteorological circumstances. Trend analysis showed significant increases in AOD during 2000–2009 for all the cities, perhaps reflecting a booming economy and industrial development, with significant emissions of sulfur dioxide (SO2), and primary aerosols. China’s strict air pollution control policies and control of vehicular emissions helped to decrease AOD from 2010 to 2019, enhancing air quality throughout the study area. A notably similar pattern was observed for AOD and meteorological parameters (LST: land surface temperature, WV: water vapor, and P: precipitation), signifying that meteorology plays a role in terms of increasing and decreasing AOD.

Spatiotemporal variations and long term trends analysis of aerosol optical depth over the United Arab Emirates

This research provides a comprehensive long term time series analysis of the spatiotemporal variability of aerosol optical depth (AOD) over the United Arab Emirates (UAE) using MODIS (Moderate-resolution imaging spectroradiometer) MAIAC (Multi-Angle Implementation of Atmospheric Correction) AOD (aerosol optical depth) product for the period 2003–2018. The UAE is located in one of the driest places on earth, the Arabian Peninsula, with two large extended desert regions that form major atmospheric dust sources in the world. The atmospheric dust emitted by these vast deserts is transported locally and regionally and it is the primary major contributor to the overall aerosol optical depth in this area. The investigation of the spatiotemporal trends of AOD over the UAE showed a significant obvious annual seasonal variability. Results showed a significant increase in AOD during summer due to high wind speed, resulting in scattered events of small to large scale haboobs within the study area, high temperature, and high relative humidity. The increased humidity levels are due to increased temperatures in the summer season leading to significant evaporation from the neighbouring water bodies (mainly the Arabian Gulf and the Sea of Oman) leading to a significant increase in water vapour levels in the atmosphere. Summar AOD values during the study period ranged between 0.42 – 0.73. The second highest AOD was observed in spring followed by winter. Spring AOD values ranged between 0.33-0.58 and winter values ranged between 0.25 – 0.41. The spatial distributions of AOD showed higher values over the coastal and desert areas. Coastal areas portrayed higher values as well likely due to the high levels of evaporation and increased anthropogenic activities such as coastal urban developments and automobile pollution. Time series analysis of the multitemporal dataset confirmed the SARIMA forecast model as the best fit which can be used to provide an accurate and reliable monthly forecast for the AOD concentrations in the UAE.