Multi-Angle Implementation Of Atmospheric Correction Research Articles

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 to 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

First retrieval of daily 160 m aerosol optical depth over urban areas using Gaofen-1/6 synergistic observations: Algorithm development and validation

The satellite-based aerosol optical depth (AOD), which can provide continuous spatial observations of aerosol loadings, is widely adopted to estimate atmospheric environmental quality and evaluate its risk for human health. However, current satellite-retrieved AOD products characterized by a comparatively coarse spatial resolution (≥1 km) can hardly analyze the structure of atmospheric pollution or its correlation with urban landscapes over populated urban areas. Existed studies have tried to address this deficiency by retrieving high-resolution AOD using Landsat images, whose long revisit period (16 days nominally), however, largely limits its applications related to urban air pollution research. To achieve both high spatial resolution and expected revisit period from satellite observation, in this study, a comprehensive AOD retrieval framework is developed for Wide-Field-of-View (WFV) satellite sensors to yield a daily AOD dataset over urban areas with 160 m spatial resolution, based on the Gaofen-1 and Gaofen-6 synergistic observations. To address the crucial challenge that the high spatial resolution and complex urban landscape both contribute a dramatic variation of land surface bidirectional reflectance, a Simulated-Annealing-coupled Semiempirical Modified Rahman-Pinty-Verstraete (SAS-MRPV) scheme is proposed to model and estimate the land surface reflectance (LSR) in the AOD retrieval framework, where the SAS-MRPV scheme is implemented using simulated annealing iteration initialized by the bidirectional reflectance distribution function (BRDF) products from the Moderate Resolution Imaging Spectrometer (MODIS) as a priori knowledge. The validation results demonstrate that the Gaofen WFV AOD retrievals exhibit good agreement with the ground-based AErosol RObotic NETwork (AERONET) and SONET (Sun-sky radiometer Observation NETwork) AOD, demonstrating the correlation coefficient and the expected error (EE)±(0.05 + 0.15AODAERONET/SONET) ratio respectively reaching up to 0.97 and over 80 %, and only slight bias fluctuations are found under different land cover types, aerosol loading, and seasonal conditions. Moreover, the validation results of Gaofen WFV AOD retrievals, with LSR estimated based on the SAS-MRPV scheme, the MODIS BRDF Products, and the Minimum Reflectance Technique (MRT) method, demonstrate better accuracy and completeness of AOD retrievals using the SAS-MPRV scheme over both natural and impervious land surfaces, indicating the stability and reliability of proposed SAS-MRPV LSR determination scheme in WFV AOD retrieval. In addition to that, the error analyses and quality control results demonstrate that the SAS-MRPV scheme is effective and necessary to guarantee the reliability and accuracy of land surface bidirectional reflectance estimation by identifying and excluding the land surface pixels unsuitable for LSR modeling, which mitigates the uncertainty and yield better accuracy in the final WFV AOD products. Furthermore, the inter-comparison between WFV-derived AOD against the operational MODIS Deep Blue (10 km), Dark Target (3 km), and Multi-Angle Implementation of Atmospheric Correction (MAIAC, 1 km) AOD products demonstrates that the 160 m WFV AOD retrievals, on the basis of obtaining similar aerosol overall descriptions as MODIS AOD retrievals, possess the capability to characterize the spatial distribution of atmospheric pollution at a finer scale with smoother variation under both clean and polluted conditions. With outstanding accuracy and reliable performance, the developed comprehensive high-resolution AOD retrieval framework exhibits substantial potential for operational AOD retrieval of Gaofen WFV satellite observations, which could be directly applied to other urban areas supporting further studies related to air pollution emission management and health risk assessment at extra-fine spatial scale.

Propagation of NO2 originated in intense fires in the Paraná River Delta analyzed from satellite observations

Wildfires in the Paraná River Delta started to be a common situation in the year 2008 and during the first years of the 2020́s decade: 2020, 2021, 2022. These fires affected natural areas, primarily the islands within the delta, situated in front of Rosario city (32.95 °S, 60.67 °W, 25 meters above sea level (m.a.s.l.)), Argentina. They mostly occur during the period between June and September each year and are more intense when a strong La Niña event is present. The aim of this study is to examine the association between the propagation of high intensity nitrogen dioxide (NO2) plumes and the significant fire events that occurred in the Paraná River Delta from 6 to 17 September 2022. Satellite data of NO2 obtained from the TROPOspheric Monitoring Instrument (TROPOMI) on board the European Space Agency (ESA) Sentinel-5 Precursor satellite, air mass forward trajectories performed with The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT), Fire Radiative Power (FRP) data provided by Visible Infrared Imaging Radiometer Suite (VIIRS) on board The National Oceanic and Atmospheric Administration - 20 (NOAA-20) and Suomi National Polar-orbiting Partnership (S-NPP) satellites, Aerosol Optical Depth (AOD) from VIIRS S-NPP and Smoke Injection Height from Multi-Angle Implementation of Atmospheric Correction (MAIAC) Thermal Technique were employed. The trajectories followed by the air masses coming from the major fire sources locations nearby Rosario city and its surrounding area, were analyzed at altitudes of 20, 500, 1000, 1500 and 2000 m above ground level (AGL) in order to evaluate the impact of the NO2 clouds present in the atmosphere and carried by the winds, at different sites of Rosario and its adjacent region. The NO2 plumes has the potential to pose a significant health risk for this region, inhabited by an approximate population of one and a half million individuals. This risk is contingent upon the altitude where the highest levels of NO2 in the tropospheric column are found. Tropospheric vertical column densities (VCDs) of NO2 in the range of 4.68×1014 to 1.66×1016 molecules/cm2 were determined. The results indicate a robust correlation between elevated tropospheric NO2 VCD values and intense fires, as characterized by their FRP. Additionally, high tropospheric NO2 VCDs were observed to impact cities and towns located in close proximity to the fires. In particular, the unusually high tropospheric NO2 VCDs determined for the towns of Fighiera and Pueblo Esther, situated along the coast and near the Paraná River Delta, are noteworthy. This is uncommon considering they are small towns in rural areas that typically have good air quality. The results regarding FRP, provided by VIIRS on board NOAA-20 and S-NPP satellites, indicate that the fire characterization of hotspots during the considered period had a mean value in the range of 20.17-21.47 MW. On specific days (12, 13 and 16 September 2022), the cumulative (total) daily values were very high, ranging from 2000 to 10000 MW. Furthermore, on 12 and 13 September 2022, the smoke aerosols did not reach significant altitudes in the atmosphere, remaining within the range of 0 to 1500 m AGL. During the fire period, AOD values close to 1 were observed within the fire plume. Notably, Puerto San Martín (0.27), San Lorenzo (0.22), and Ibarlucea (0.20) recorded higher average AOD values compared to other sites. The remaining sites generally exhibited lower average AOD values, with a slight distinction between rural locations (Pueblo Esther: 0.14 and Fighiera: 0.14) and the city center of Rosario (0.09).

Time series retrieval of Multi-wavelength Aerosol optical depth by adapting Transformer (TMAT) using Himawari-8 AHI data

Accurate aerosol optical depth (AOD) with high temporal resolution is required for dynamic monitoring changes of aerosol properties. The Himawari-8 Advanced Himawari imager (AHI) data with 10-min temporal resolution has been widely used for AOD retrieval but usually involves radiative transfer models and assumptions on surface reflection and aerosol properties that are hard to satisfy. This paper introduces a data-driven algorithm without any priori assumptions on surface or aerosol properties called Time series retrieval of Multi-wavelength AOD by adapting Transformer (TMAT). TMAT directly retrieves AOD at eight-wavelengths every 10-min throughout the day, using AHI top of atmosphere (TOA) reflectances in 6 wavebands and ancillary data. TMAT explores diurnal temporal change patterns of AOD and surface reflection using time series data. TMAT was trained and evaluated using samples derived by matching a year (2019) of AHI full-disk data and AERONET/SONET observations with 12,768 diurnal time series, each of which includes up to 81 10-min 6-band AHI TOA observations and 81 (time) × 8 (wavelength) AOD values per day. There are up to 273,413 (<12,768 × 81) TOA observations in 6 wavebands and up to 1,420,716 (<12,768 × 81 × 8) AOD values in all the time series samples, with filled values (−9999) to represent no data due to cloud contamination and incomplete wavelength configurations of AOD stations. The masking mechanism in Transformer is leveraged to exclude the contribution of the AHI filled values in time series AOD retrieval and exclude the contribution of AOD filled values in training. TMAT was trained with a physical constraint that the retrieved AOD decreases toward longer wavelengths. TMAT AOD was evaluated by separating training and validation samples using site-specific leave-one-station-out validation. TMAT AOD shows extremely high accuracy at all wavelengths, with RMSE = 0.101, R = 0.949 and 81.69% of retrievals within ±0.05+15%∗AODsites for the AOD at 550 nm. We also validated the accuracy of the NASA multiangle implementation of atmospheric correction (MAIAC) AHI AOD over these sites with RMSE = 0.186, R = 0.832 and 52.10% of retrievals within ±0.05+15%∗AODsites. The Ångström exponent (AE) derived from the multi-wavelength TMAT AOD also shows good agreement with AERONET/SONET AE, with a correlation coefficient of 0.620 and an RMSE of 0.291. The TMAT effectiveness to capture diurnal temporal change patterns of surface reflection and AOD was evidenced by the significant reduction of AOD accuracy when using a non-time series deep learning method. The effectiveness of multi-wavelength joint retrieval was evidenced by the significant reduction of AE accuracy when using single wavelength retrieval models. Evidence shows the training and evaluation split methods have a large impact on evaluation accuracy and this study further used ground network independent training and evaluation split to confirm the leave-one-station-out validation accuracy. Despite this, future study will focus on more spatially independent validation and more comprehensive comparison between the MAIAC and TMAT results.

Satellite-based aerosol optical depth estimates over the continental U.S. during the 2020 wildfire season: Roles of smoke and land cover

Wildfires produce smoke that can affect an area >1000 times the burn extent, with far-reaching human health, ecologic, and economic impacts. Accurately estimating aerosol load within smoke plumes is therefore crucial for understanding and mitigating these impacts. We evaluated the effectiveness of the latest Collection 6.1 MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm in estimating aerosol optical depth (AOD) across the U.S. during the historic 2020 wildfire season. We compared satellite-based MAIAC AOD to ground-based AERONET AOD measurements during no-, light-, medium-, and heavy-smoke conditions identified using the Hazard Mapping System Fire and Smoke Product. This smoke product consists of maximum extent smoke polygons digitized by analysts using visible band imagery and classified according to smoke density. We also examined the strength of the correlations between satellite- and ground-based AOD for major land cover types under various smoke density levels. MAIAC performed well in estimating AOD during smoke-affected conditions. Correlations between MAIAC and AERONET AOD were strong for medium- (r = 0.91) and heavy-smoke (r = 0.90) density, and MAIAC estimates of AOD showed little bias relative to ground-based AERONET measurements (normalized mean bias = 3 % for medium, 5 % for heavy smoke). During two high AOD, heavy smoke episodes, MAIAC underestimated ground-based AERONET AOD under mixed aerosol (i.e., smoke and dust; median bias = −0.08) and overestimated AOD under smoke-dominated (median bias = 0.02) aerosol. MAIAC most overestimated ground-based AERONET AOD over barren land (mean NMB = 48 %). Our findings indicate that MODIS MAIAC can provide robust estimates of AOD as smoke density increases in coming years. Increased frequency of mixed aerosol and expansion of developed land could affect the performance of the MAIAC algorithm in the future, however, with implications for evaluating wildfire-associated health and welfare effects and air quality standards.

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.

Reconstructing aerosol optical depth using spatiotemporal Long Short-Term Memory convolutional autoencoder

Aerosol Optical Depth (AOD) is a crucial atmospheric parameter in comprehending climate change, air quality, and its impacts on human health. Satellites offer exceptional spatiotemporal AOD data continuity. However, data quality is influenced by various atmospheric, landscape, and instrumental factors, resulting in data gaps. This study presents a new solution to this challenge by providing a long-term, gapless satellite-derived AOD dataset for Texas from 2010 to 2022, utilizing Moderate Resolution Imaging Spectroradiometer (MODIS) Multi-angle Implementation of Atmospheric Correction (MAIAC) products. Missing AOD data were reconstructed using a spatiotemporal Long Short-Term Memory (LSTM) convolutional autoencoder. Evaluation against an independent test dataset demonstrated the model’s effectiveness, with an average Root Mean Square Error (RMSE) of 0.017 and an R2 value of 0.941. Validation against the ground-based AERONET dataset indicated satisfactory agreement, with RMSE values ranging from 0.052 to 0.067. The reconstructed AOD data are available at daily, monthly, quarterly, and yearly scales, providing a valuable resource to advance understanding of the Earth’s atmosphere and support decision-making concerning air quality and public health.

A novel algorithm for full-coverage daily aerosol optical depth retrievals using machine learning-based reconstruction technique

The ubiquitous missing values in the satellite-derived aerosol optical depth (AOD) products have always been a challenge for spatial and temporal analysis. To address this concern, we propose a novel data-driven model to attain the full-coverage daily AOD dataset with 0.01° spatial resolution in the Guangdong-Hong Kong-Macao Greater Bay Area (hereafter GBA) from 2010 to 2021. Firstly, the missing values of top-of-atmosphere (TOA) reflectance and surface reflectance of Moderate Resolution Imaging Spectroradiometer (MODIS) caused by cloud contamination, were reconstructed using the Data Interpolating Empirical Orthogonal Functions (DINEOF). Subsequently, a new model was developed for the estimation of AOD which integrates the geographical and temporal encodings into the Light Gradient Boosting Machine (LightGBM) with the inputs of reconstructed TOA/surface reflectance and other influencing variables like meteorological and geographical factors. Results showed that the derived gap-free AOD dataset outperforms the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD product and agrees well with the ground-based observations, achieving an index of agreement (IOA) of 0.88, R of 0.84, root mean square error (RMSE) of 0.19 and mean absolute error (MAE) of 0.14. Moreover, the derived AOD dataset presents consistent temporal patterns with in-situ measurements, but with more spatial details than other gapless AOD datasets, i.e., Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2). Overall, this study has developed a promising meteorological framework for the estimation of full-coverage AOD, which can also be applied over other regions. The derived long-term full-coverage daily AOD dataset can also be used for other applications related to climate change, air quality and ecosystem assessment.

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.

Aerosol optical depth climatology from the high-resolution MAIAC product over Europe: differences between major European cities and their surrounding environments

Abstract. The aerosol optical depth (AOD) is a derived measurement useful to investigate the aerosol load and its distribution at different spatio-temporal scales. In this work we use long-term (2000–2021) MAIAC (Multi-Angle Implementation of Atmospheric Correction) retrievals with 1 km resolution to investigate the climatological AOD variability and trends at different scales in Europe: a continental (30–60∘ N, 20∘ W–40∘ E), a regional (100 × 100 km2) and an urban–local scale (3 × 3 km2). The AOD climatology at the continental scale shows the highest values during summer (JJA) and the lowest during winter (DJF) seasons. Regional and urban–local scales are investigated for 21 cities in Europe, including capitals and large urban agglomerations. Analyses show AOD average (550 nm) values between 0.06 and 0.16 at the urban–local scale while also displaying a strong north–south gradient. This gradient corresponds to a similar one in the European background, with higher AOD being located over the Po Valley, the Mediterranean Basin and eastern Europe. Average enhancements of the local with respect to regional AOD of 57 %, 55 %, 39 % and 32 % are found for large metropolitan centers such as Barcelona, Lisbon, Paris and Athens, respectively, suggesting a non-negligible enhancement of the aerosol burden through local emissions. Negative average deviations are observed for other cities, such as Amsterdam (−17 %) and Brussels (−6 %), indicating higher regional background signal and suggesting a heterogeneous aerosol spatial distribution that conceals the urban–local signal. Finally, negative statistically significant AOD trends for the entire European continent are observed. A stronger decrease rate at the regional scale with respect to the local scale occurs for most of the cities under investigation.

Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil

Brazil experiences significant aerosol loads throughout the year, particularly during the biomass-burning season in the Amazon. Thus, given the importance of aerosols for climate and health, this research aimed to validate and compare Aerosol Optical Depth (AOD) products over Brazil. This evaluation considers three algorithms that retrieve AOD by using data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor: Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC). To validate the satellite data, 17 sunphotometers from the AErosol RObotic NETwork (AERONET) were utilized. The results show a high correlation (R > 0.9) between the MODIS-AOD products and ground-based data. However, MODIS-AOD products tend to overestimate or underestimate AOD values, depending on the specific AOD value and algorithm evaluated. Additionally, it was observed that the performance of the algorithms is influenced by factors such as land cover type, view geometry, and the spatiotemporal distribution of aerosols. In particular, challenges were encountered when retrieving robust AOD data for Savanna and Urban cover classes. In conclusion, the results indicate that MAIAC and DB algorithms demonstrate greater stability in retrieving AOD values. Nevertheless, caution should be exercised when applying these products to map aerosols on specific surfaces, such as urban areas.

Spatio-temporal variations of aerosol optical depth over Ukraine under the Russia-Ukraine war

The ongoing Russia-Ukraine war has an impact on air quality in the contested region, but it is difficult to assess it during the war and to distinguish between weather conditions and anthropogenic impacts on air pollution. Aerosol optical depth (AOD) might be a way to indicate the regional air quality remotely. In this study, we analyze satellite-based MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) AOD products to compare monthly mean AOD values before and during the war. By examining the spatial-temporal distribution of AOD in 2022 and comparing it with a baseline period from 2012 to 2021, we aim to assess the impact of the conflict. Our analysis employs a time series algorithm that decomposes long-term trends and seasonal variations, enabling us to identify AOD changes associated with the war-induced artificial perturbations. Additionally, we utilize satellite-based tropospheric NO2 data and nighttime lighting data as auxiliary sources to support the analysis of abnormal AOD changes resulting from the war. Furthermore, four parameters from Aerosol Robotic Network (AERONET) measurements in the Kyiv site, including angstrom exponent (AE), single scattering albedo (SSA), refractive index (RI), fine mode fraction (FMF) were exclusively discussed to explore the possible changes of aerosol physical-optical properties over Ukraine during the war. Results showed that air quality in Ukraine has been affected by the war in contradictory ways at different levels. At the national level, atmospheric pollution has dropped across Ukraine due to a decrease in the sources of air pollution emissions as a result of the suppression of economic and agricultural activities. Meanwhile, atmospheric air quality has deteriorated at the local scale where war is intense due to the large amounts of air pollutants emitted by explosions. Specifically, a significant decrease in AOD of 22.46% compared to the baseline period was observed across Ukraine in January and February 2022 before the war. Additionally, there was a column density reduction of over 10% for tropospheric NO2 compared to the same period in the previous year. Following the outbreak of the war on February 24, significant increases in AOD were observed in March when compared to the baseline period. Eastern Ukraine experienced a significant increase of 40.14%, while northern Ukraine and central Ukraine saw rises of 34.15% and 45.78%, respectively. In April, a wider distribution of areas with significantly high AOD values across Ukraine was observed compared to March. In July, widespread areas with anomalous low AOD appeared in northern and western Ukraine, likely due to a decrease in post-harvest agricultural burning caused by the war. In August, anomalous high AOD areas were observed in central Ukraine, possibly attributed to continued Russian long-range strikes and a new round of bombardments.

Spatiotemporal Weighted for Improving the Satellite-Based High-Resolution Ground PM2.5 Estimation Using the Light Gradient Boosting Machine

Surface fine particulate matter (PM) with a diameter of less than 2.5 microns (PM2.5) negatively impacts human health and the economy. However, due to data and model limitations, obtaining high-quality, high-spatial-resolution surface PM2.5 concentration data is a challenging task, and it is difficult to accurately assess the temporal and spatial changes in PM2.5 levels at a small regional scale. Here, we combined multi-angle implementation of atmospheric correction (MAIAC) aerosol products, ERA5 reanalysis data, etc., to construct an STW-LightGBM model that considers the spatiotemporal characteristics of air pollution and estimate the PM2.5 concentration of China’s surface at 1 km resolution from 2015 to 2020. Our model performed well, and the fitting accuracy of the 10-fold cross-validation between years was 0.877–0.917. The fitting accuracy of the model was &gt;0.85 at different time scales (month, season, and year). The average slope of the regression prediction was 0.9 annually. The results showed that PM2.5 pollution improved from 2015 to 2020. The average PM2.5 concentration decreased by 4.55 μg/m3, and the maximum decrease reached 90.51 μg/m3. The areas with high PM2.5 concentrations were predominantly in the North China Plain, Sichuan Basin, and Xinjiang in the west, and the levels in areas with elevated PM2.5 levels were consistent across most study years. The standard deviation ellipse for PM2.5 in China showed a ‘northeast–southwest’ spatial distribution. From an interannual perspective, the average values of the four seasonal stations in the country showed a downward trend from 2015 to 2020, with the most obvious decline in winter, from 70.67 μg/m3 in 2015 to 46.75 μg/m3 in 2020. Compared to earlier inversion studies, this work provides a more stable and accurate method for obtaining high-resolution PM2.5 data, which is necessary for local air governance and environmental ecological construction at a fine scale.

Calibration of the SNPP and NOAA 20 VIIRS sensors for continuity of the MODIS climate data records

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS.

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.

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

Estimation of Respiratory Disease Burden Attributed to Particulate Matter from Biomass Burning in Northern Thailand Using 1-km Resolution MAIAC-AOD

The upper northern Thailand suffers from air pollution due to open burning, which has been known for a long time. It was also found that different respiratory diseases were attributed to air pollution, especially particulate matter. This study estimated the health impacts attributed to PM10 between 2014 and 2016 using the burden of disease in terms of the disability adjusted life year (DALYs). The spatial correlation was evaluated based on applicable remote sensing data using the geographically weighted regression (GWR) model. The average measured PM10 concentrations for the summer and annual periods between 2014 and 2016 were 73 and 89 µg m-3, respectively, exceeded the national standard (50 µg m-3). In the months of March and April, when PM10 concentrations were at their highest, the maximum values of the Multi-Angle Implementation of Atmospheric Correction (MAIAC-AOD), 2.70 and 3.48, were recorded. There was a strong correlation between the MAIAC-AOD and the ground-based AOD measurements (AERONET stations), with R of 0.8468, 0.8396, and 0.8334 between 2014–2016. The correlation coefficients for the 3,208 co-located gridded of PM10 emissions vs. measured PM10, measured PM10 vs. MAIAC-AOD, and MAIAC-AOD vs. PM10 emissions were 0.6656, 0.6446, and 0.5580, respectively. The spatial correlation between the interpolated measured PM10 and 1-km MAIAC-AOD was 0.5979, 0.3741, and 0.7584 as an outcome of GWR. The total DALYs of chronic obstructive pulmonary disease (COPD) attributable to PM10 in 2014–2016 were 115,930 years per 100,000 population, with the relative risk of COPD related to PM10 at a 95% confidence interval of 1.2045–1.2107.

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.