Estimation Of Aerosol Optical Depth Research Articles

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.

Quantitative assessment of the potential of optimal estimation for aerosol retrieval from geostationary weather satellites in the frame of the iAERUS‐GEO algorithm

AbstractSatellite remote sensing enables the study of atmospheric aerosols at large spatial scales, with geostationary platforms making this possible at sub‐daily frequencies. High‐temporal‐resolution aerosol observations can be made from geostationary data by using robust numerical inversion methods such as the widely‐used optimal estimation (OE) theory. This is the case of the instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit (iAERUS‐GEO) algorithm, which successfully retrieves aerosol optical depth (AOD) maps from the Meteosat Second Generation weather satellite based on a simple implementation of the OE approach combined with the Levenberg–Marquardt method. However, the exact gain in inversion performances that can be obtained from the multiple and more advanced possibilities offered by OE is not well documented in the current literature. Against this background, this article presents the quantitative assessment of OE for the future improvement of the iAERUS‐GEO algorithm. To this end, we use a series of comprehensive experiments based on AOD maps retrieved by iAERUS‐GEO using different OE implementations, and ground‐based observations used as reference data. First, we assess the varying importance in the inversion process of satellite observations and a priori information according to the content of satellite aerosol information. Second, we quantify the gain of AOD estimation in log space versus linear space in terms of accuracy, AOD distribution and number of successful retrievals. Finally, we evaluate the accuracy improvement of simultaneous AOD and surface reflectance retrieval as a function of the regions covered by the Meteosat Earth's disk.

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.

Integration of Landsat 8 (OLI) and MODIS images to monitor suspended particles and evaluate the spatial pattern of air pollution

Air pollution has become one of the disturbances of daily life in urban areas due to its negative and repulsive effect in current conditions. On the other hand, it has a long-term negative impact on the behavior and quantity of climate change scenarios at the local, national and global levels. In this research, the integration of Landsat 8 (OLI) satellite images and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (to estimate the ozone permeability and the water vapor permeability coefficients) using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiation transfer model was used to determine Aerosol Optical Depth (AOD). For this purpose, instead of using the look-up table and approximating the AOD values, the components of the 6S radiative transfer model were estimated with a combined approach and the AOD values were retrieved without using the look-up table approach. One of the main problems of past research is to identify a suitable conversion model to connect the refractive index to the composition of aerosols. Therefore, the main innovation of this research is focused on a simplified, high resolution AOD retrieval method for Landsat 8 (OLI) images over mixed surfaces which does not require a LUT. Considering the relationship between AOD values and various indicators of air quality measurement, in this work, AOD estimation has been done with the aim of helping to predict these indicators. In this research, 24,569 square kilometers from a part of two states of Maryland and Virginia in the United States of America were investigated in order to estimate AOD. In order to evaluate the results of AOD retrieval, 5 Aerosol Robotic Network (AERONET) ground stations were used for comparison and their accuracy was evaluated. The results show that the proposed method has been able to extract the amount of aerosol with acceptable accuracy. According to the results obtained in this research, the root mean square errors (RMSE) is equal to 0.1036, the adjusted R2 coefficient (AdjR2) is equal to 0.8032 and the coefficient of determination (R2) is equal to 0.8079. It can be concluded that the integration of Landsat 8 (OLI) images with MODIS images has been able to provide more suitable results.

Reconstructing MODIS aerosol optical depth and exploring dynamic and influential factors of AOD via random forest at the global scale

Aerosols pose a significant impact on the atmospheric environment and public health. Aerosol optical depth (AOD) is one of the essential parameters for quantifying aerosols. Currently, existing AOD products suffer from severe pixel value missing issue due to clouds and other interferences globally, which severely affects the accuracy of fine particulate matter estimation and related epidemics researches. At the global scale, this study first adopted population as one of the influencing factors of AOD, and fully considered the effects of humidity, surface pressure, cloud cover, and evapotranspiration on AOD. Meanwhile, the spatiotemporal resolution of meteorological data was promoted to improve the AOD estimation precision. A random forest model was fitted to estimate the relationship between AOD and influencing factors in non-missing areas, and the missing AOD values were filled in based on this model. The spatiotemporal dynamic characteristics of AOD were revealed, and the importance of each influencing factor on AOD was elucidated. The results demonstrated that the scheme can effectively fill in missing AOD values, and the accuracy of the filled-in values was significantly improved with an R2 of 0.6683 using AERONET as the validation sites. After filling in the missing values, the global AOD spatial coverage rate approached 100%. AOD exhibited significant spatiotemporal heterogeneity, with higher values in East Asia, South Asia, Southwest Asia, West and Central Africa, and northern South America, possibly due to natural factors and human activities. The importance of each influencing factor on AOD was ranked in descending order as follows, surface temperature (0.15), population density (0.14), surface pressure (0.11), U-shaped wind (0.10), humidity (0.10), total cloud cover (0.09), V-shaped wind (0.09), boundary layer height (0.08), evapotranspiration (0.07), and total precipitation (0.03). This study can provide insights and references for reconstructing other remotely sensed datasets.

Improving the accuracy of AOD by using multi-sensors data over the Red Sea and the Persian Gulf

Aerosol Optical Depth (AOD) retrieved using Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) and Moderate Resolution Imaging Spectroradiometer (MODIS) plays a crucial role in aerosol sensing. However, the performance of these products varies across different aerosol concentrations. This research assessed the performance of CALIOP and MODIS AOD products over a three-year period (January 2017 to June 2019) under various aerosol concentrations in the Red Sea and the Persian Gulf. The products were compared with ship-based AERONET (Aerosol Robotic Network) measurements conducted in the study area. The findings reveal that MODIS AOD products exhibit greater reliability during clear days, whereas CALIOP AOD products are more accurate in regions characterized by high aerosol concentrations. However, CALIOP’s limited product resolution prevents it from providing adequate spatial-temporal coverage under heavy aerosol concentrations. To enhance the accuracy of MODIS AOD, this paper proposes a methodology based on various machine learning (ML) algorithms, including Random Forest (RF), Multi-layer Perceptron (MLP), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVR), and Bayesian network. The study explores the performance of MODIS and CALIOP AOD products during moderate and pollution days in the Persian Gulf and Red Sea regions.The ML models are ranked based on their accuracy, with the order being XGBoost > MLP >SVRrbf > RF > Bayesian >SVRlinear for the annual and two semi-annual networks. In other words, the Bayesian method demonstrates higher efficiency (R2 = 0.97, 0.91, and 0.90) in handling seasonal subsets. The proposed model’s outcomes are validated using ship-based AERONET AOD data. By comparing the estimated AOD values with the ship-based AERONET AOD values and analyzing the overall correlation results, it is evident that machine learning techniques effectively provide accurate AOD estimates for moderate and pollution days.

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.

Instantaneous aerosol and surface retrieval using satellites in geostationary orbit (iAERUS-GEO) – estimation of 15 min aerosol optical depth from MSG/SEVIRI and evaluation with reference data

Abstract. Geostationary meteorological satellites are unique tools to monitor atmospheric aerosols from space. The observation of the Earth several times per hour allows these types of imaging systems to provide high-temporal-resolution observations of these suspended particles, which are of interest for research and operational topics, including climate, air quality, numerical weather prediction, and volcanic risk management. However, some challenges need to be addressed to achieve the sub-daily retrieval of aerosol properties mainly due to the varying sensitivity of geostationary imagers to aerosols during the day. In this article we propose a new algorithm named iAERUS-GEO (instantaneous Aerosol and surfacE Retrieval Using Satellites in GEOstationary orbit) that estimates the diurnal evolution of aerosol optical depth (AOD) over land and ocean from the Meteosat Second Generation (MSG) satellite. This is achieved by the use of an optimal-estimation method combined with several aerosol models and other features, including the daily retrieval of the surface reflectance directionality using Kalman filtering. AOD estimates provided by iAERUS-GEO every 15 min – the acquisition frequency of the Spinning Enhanced Visible InfraRed Imager (SEVIRI) on MSG – are assessed with collocated reference aerosol observations. First, comparison to AERONET ground-based data proves the overall satisfactory accuracy of iAERUS-GEO over land, with the exception of some higher biases found over bright surfaces and for high scattering angles. The confidence measure provided by iAERUS-GEO is proved useful to filter these less satisfactory retrievals that generally arise due to a low information content on aerosols provided by SEVIRI. Second, comparison to the GRASP/POLDER satellite product shows similar scores for the two aerosol data sets, with a significantly larger number of retrievals for iAERUS-GEO. This added value – which we illustrate here by inspecting the sub-daily variation in AOD over selected regions – allows geostationary satellites to break the temporal barrier set by traditional aerosol remote sensing from the low Earth orbit. Furthermore, the aerosol retrievals presented in this work are expected to be improved in the near future thanks to the enhanced sensing capabilities of the upcoming Meteosat Third Generation Imager mission.

An improved method for retrieving aerosol optical depth over Ebinur Lake Basin from Gaofen-1

Lake Ebinur is the largest saltwater lake in Xinjiang. In recent years, the exposed dry lake bottom surface has become a remarkable source of salt and dust owing to the desiccation of the lake. Given the importance of the lake basin to human livelihoods, comprehensive monitoring of basin behavior is required. Remote sensing offers numerous novel approaches to monitoring the spatial and temporal distribution of aerosols. During this study, high spatial resolution aerosol optical depth (AOD) inversion at Lake Ebinur was still in the gap stage. The wide-field-of-view (WFV) data from four cameras on the GaoFen-1 (GF-1) satellite, launched by China in April 2013, offers high spatial and temporal resolution and excellent potential for AOD estimation. Owing to the shortwave infrared band (SWIR) deficiency and the complexity of surface reflectance, the coefficient of variation (CV) was proposed as a measure of surface reflectance to develop a surface reflectance database for land cover characteristics in the Ebinur Lake Basin. The significant improvements of the algorithm include (1) the design of an accurate and universal cloud detection algorithm through the blue and red bands. (2) The CV was used to assess the robustness of surface reflectance under long time series. After dividing the surface reflectance image pixels with low robustness, a reasonable value was assigned according to the surface reflectance variation characteristics of the image pixels. (3) Aerosol models were determined from the long-term measurements of the CIMEL solar photometer (CE318), and the validation of ground-based observations showed good correlation that WFV/GF-1 AOD was comparable to CE318 AOD (R = 0.705; root mean square error (RMSE) = 0.098; mean absolute error (MAE) = 0.078), and the smaller MAE and RMSE confirmed the feasibility of inversion of AOD by this method.

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.

Application of Optimal Interpolation to Spatially and Temporally Sparse Observations of Aerosol Optical Depth

Aerosol optical depth (AOD) is one of the basic characteristics of atmospheric aerosol. A global ground-based network of sun and sky photometers, the Aerosol Robotic Network (AERONET) provides AOD data with low uncertainty. However, AERONET observations are sparse in space and time. To improve data density, we merged AERONET observations with a GEOS-Chem chemical transport model prediction using an optimal interpolation (OI) method. According to OI, we estimated AOD as a linear combination of observational data and a model forecast, with weighting coefficients chosen to minimize a mean-square error in the calculation, assuming a negligible error of AERONET AOD observations. To obtain weight coefficients, we used correlations between model errors in different grid points. In contrast with classical OI, where only spatial correlations are considered, we developed the spatial-temporal optimal interpolation (STOI) technique for atmospheric applications with the use of spatial and temporal correlation functions. Using STOI, we obtained estimates of the daily mean AOD distribution over Europe. To validate the results, we compared daily mean AOD estimated by STOI with independent AERONET observations for two months and three sites. Compared with the GEOS-Chem model results, the averaged reduction of the root-mean-square error of the AOD estimate based on the STOI method is about 25%. The study shows that STOI provides a significant improvement in AOD estimates.

Aerosol-boundary layer dynamics and its effect on aerosol radiative forcing and atmospheric heating rate in the Indian Ocean sector of Southern Ocean

The study examines the thermodynamic structure of the marine atmospheric boundary layer (MABL) and its effect on the aerosol dynamics in the Indian Ocean sector of Southern Ocean (ISSO) between 30°S-67°S and 57°E-77°E. It includes observations of aerosols and meteorology collected during the Xth Southern Ocean Expedition conducted in December 2017. The results revealed the effect of frontal-region-specific air-sea coupling on the thermodynamic structure of MABL and its role in regulating aerosols in ISSO. The MABL over the subtropical front was unstable and formed a well-evolved mixed layer (≈2400 m) capped by low-level inversions (≈660 m). Convective activities in the Sub-Antarctic Frontal region were associated with the Agulhas Retroflection Current, which supported the formation of a well-developed mixed layer (≈1860 m). The mean estimates of aerosol optical depth (AOD) and black carbon (BC) mass concentrations were 0.095 ± 0.006 and 50 ± 14 ng m−3, respectively, and the resultant clear sky direct shortwave radiative forcing (DARF) and atmospheric heating rate (HR) were 1.32 ± 0.11 W m−2 and 0.022 ± 0.002 K day−1, respectively. In the polar front (PF) region, frequent mid-latitude cyclones led to highly stabilized MABL, supported low-level multi-layered clouds (>3-layers) and multiple high-level inversions (strength > 0.5 K m−1 > 3000 m). The clouds were mixed-phased with temperatures less than −12 °C at 3000 m altitude. Interestingly, there was higher loading of dust and BC aerosols (276 ± 24 ng m−3), maximum AOD (0.109 ± 0.009), clear sky DARF (1.73 ± 0.02 W m−2), and HR (0.029 ± 0.005 K day−1). This showed an accumulation of long-range advected anthropogenic aerosols within baroclinic-boundaries formed over the PF region. Specifically, in the region south of PF, weak convection caused weakly-unstable MABL with a single low-level inversion followed by no clouds/single-layer clouds. Predominant clean maritime air holding a small fraction of dust and BC accounted for lower estimates of AOD (0.071 ± 0.004), BC concentrations (90 ± 55 ng m−3) and associated clear sky DARF and HR were 1.16 ± 0.06 W m−2 and 0.019 ± 0.001 K day−1, respectively.

Is it possible to estimate aerosol optical depth from historic colour paintings?

Abstract. The idea of estimating stratospheric aerosol optical thickness from the twilight colours in historic paintings – particularly under conditions of volcanically enhanced stratospheric aerosol loading – is very tantalizing because it would provide information on the stratospheric aerosol loading over a period of several centuries. This idea has in fact been applied in a few studies in order to provide quantitative estimates of the aerosol optical depth after some of the major volcanic eruptions that occurred during the past 500 years. In this study we critically review this approach and come to the conclusion that the uncertainties in the estimated aerosol optical depths are so large that the values have to be considered questionable. We show that several auxiliary parameters – which are typically poorly known for historic eruptions – can have a similar effect on the red–green colour ratio as a change in optical depth typically associated with eruptions such as, for example, Tambora in 1815 or Krakatoa in 1883. Among the effects considered here, uncertainties in the aerosol particle size distribution have the largest impact on the colour ratios and hence the aerosol optical depth estimate. For solar zenith angles exceeding 80∘, uncertainties in the stratospheric ozone amount can also have a significant impact on the colour ratios. In addition, for solar zenith angles exceeding 90∘ the colour ratios exhibit a dramatic dependence on solar zenith angle, rendering the estimation of aerosol optical depth highly challenging. A quantitative determination of the aerosol optical depth may be possible for individual paintings for which all relevant parameters are sufficiently well constrained in order to reduce the related errors.

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.

A Simple Band Ratio Library (BRL) Algorithm for Retrieval of Hourly Aerosol Optical Depth Using FY-4A AGRI Geostationary Satellite Data

The Advanced Geostationary Radiation Imager (AGRI) is one of the primary payloads aboard the FY-4A geostationary meteorological satellite, which can provide high-frequency, wide coverage, and multiple spectral channel observations for China and surrounding areas. There are currently few studies on aerosol optical depth (AOD) inversion from FY-4A AGRI data. Based on AGRI data, a new land AOD retrieval algorithm called the band ratio library (BRL) algorithm was proposed in this study. The monthly average surface reflectance band ratio library was established after obtaining the relationship of band surface reflectance ratio from the MODIS combined AOD dataset. In order to calculate the hourly AOD, look-up tables (LUT) for the various aerosol models were constructed using the 6SV model. We quantitatively compared AOD produced from AGRI data with AERONET ground observations to validate the BRL algorithm. AGRI-retrieved AOD is in good agreement with AOD measured by AERONET, which has a correlation coefficient of R is 0.84, the linear regression function is AODAGRI = 0.80 ∗ AODAERONET − 0.004, the root-mean-square error (RMSE) is 0.16, and approximately 60% of the AGRI AOD results fall within the uncertain range of AOD = ±(0.2 × AODAERONET + 0.05). A cross-comparison was made with the MODIS AOD product provided by NASA. The comparison and verification show the proposed algorithm has a good accuracy of land AOD estimation from AGRI data.

High‐Resolution Post‐Process Corrected Satellite AOD

AbstractPoor air quality poses a great threat to human health. Accurate high‐resolution satellite remote sensing of atmospheric aerosols would highly benefit satellite‐based air quality estimates. We have developed and validated a post‐process correction and downscaling approach for satellite remote sensing of aerosols. We use NASA's Moderate Resolution Imaging Spectroradiometer aerosol optical depth (AOD) over the Washington D.C.—Baltimore area during the Distributed Regional Aerosol Gridded Observation Networks campaign in 2011 to evaluate our approach. We derive and evaluate the AOD fields at high 250 m resolution. The results show that the post‐process correction approach is suitable for deriving downscaled, high‐resolution AOD estimates and significantly improves the accuracy of the AOD retrievals.

Spatiotemporal Variation and Influencing Factors of AOD in the North China Plain

A better knowledge of the spatial and temporal variation in atmospheric aerosol and its influencing factors is of great significance to controlling atmospheric pollution and improving the atmospheric environment. First, the visible infrared imaging radiometer suite (VIIRS) intermediate product (IP) aerosol optical depth (AOD) data from 2013 to 2019 were used to analyze the temporal and spatial variation in AOD in the North China Plain. Secondly, SO2, NO2, PM2.5, meteorological data, NDVI, DEM, GDP, and POPU were selected as influencing factors, and the linkage models between AOD and its influencing factors were established based on the XGBoost model for each of the five representative cities in the North China Plain to quantitatively estimate and reveal the contribution of various influencing factors behind the temporal and spatial distribution in AOD. The results showed that in terms of spatial distribution, the AOD of the North China Plain was bounded by the Taihang Mountains, showing a pattern of high AOD in the southeast and low AOD in the northwest. In terms of temporal changes, the annual average value of AOD in the five cities showed an overall decreasing trend, and the monthly average value of AOD first increased and then decreased, with the highest value appearing in July and the lowest value in December. In addition, the AOD estimation model established in this paper for the five cities in North China had high accuracy, with R2 ranging from 0.60 to 0.67. Among the factors influencing AOD in the North China Plain, NO2 and SO2 were the most influential factors contributing to AOD in the five cities. In addition, PM2.5 was another important pollutant emission factor. In terms of meteorological factors, temperature (T), relative humidity (RH), wind speed (WS), and wind direction (WD) were the other four important influencing factors. There were both commonalities and differences in the rankings of the contribution and importance of AOD influencing factors in the five representative cities in North China.

An Evaluation of Two Decades of Aerosol Optical Depth Retrievals from MODIS over Australia

We present an evaluation of Aerosol Optical Depth (AOD) retrievals from the Moderate Resolution Imaging Spectroradiometer (MODIS) over Australia covering the period 2001–2020. We focus on retrievals from the Deep Blue (DB) and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms, showing how these compare to one another in time and space. We further employ speciated AOD estimates from Copernicus Atmospheric Monitoring Service (CAMS) reanalyses to help diagnose aerosol types and hence sources. Considering Australia as a whole, monthly mean AODs show similar temporal behaviour, with a well-defined seasonal peak in the Austral summer. However, excepting periods of intense biomass burning activity, MAIAC values are systematically higher than their DB counterparts by, on average, 50%. Decomposing into seasonal maps, the patterns of behaviour show distinct differences, with DB showing a larger dynamic range in AOD, with markedly higher AODs (ΔAOD∼0.1) in northern and southeastern regions during Austral winter and summer. This is counter-balanced by typically smaller DB values across the Australian interior. Site level comparisons with all available level 2 AOD data from Australian Aerosol Robotic Network (AERONET) sites operational during the study period show that MAIAC tends to marginally outperform DB in terms of correlation (RMAIAC = 0.71, RDB = 0.65) and root-mean-square error (RMSEMAIAC = 0.065, RMSEDB = 0.072). To probe this behaviour further, we classify the sites according to the predominant surface type within a 25 km radius. This analysis shows that MAIAC’s advantage is retained across all surface types for R and all but one for RMSE. For this surface type (Bare, comprising just 1.2% of Australia) the performance of both algorithms is relatively poor, (RMAIAC = 0.403, RDB = 0.332).

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

Abstract. Black carbon aerosols are the second largest contributor to global warming while also being linked to respiratory and cardiovascular disease. These particles are generally found in smoke plumes originating from biomass burning and fossil fuel combustion. They are also heavily concentrated in smoke plumes originating from oil fires, exhibiting the largest ratio of black carbon to organic carbon. In this study, we identified and analysed oil smoke plumes derived from 30 major industrial events within a 12-year timeframe. To our knowledge, this is the first study of its kind that utilized a synergetic approach based on satellite remote sensing techniques. Satellite data offer access to these events, which, as seen in this study, are mainly located in war-prone or hazardous areas. This study focuses on the use of MODIS (Moderate Resolution Imaging Spectroradiometer) and CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) products regarding these types of aerosol while also highlighting their intrinsic limitations. By using data from both MODIS instruments on board Terra and Aqua satellites, we addressed the temporal evolution of the smoke plume while assessing lidar-specific properties and plume elevation using CALIPSO data. The analysis method in this study was developed to better differentiate between oil smoke aerosols and the local atmospheric scene. We present several aerosol properties in the form of plume-specific averaged values. We believe that MODIS values are a conservative estimation of plume aerosol optical depth (AOD) since MODIS algorithms rely on general aerosol models and various atmospheric conditions within the look-up tables, which do not reflect the highly absorbing nature of these smoke plumes. Based on this study we conclude that the MODIS land algorithms are not yet suited for retrieving aerosol properties for these types of smoke plumes due to the strong absorbing properties of these aerosols. CALIPSO retrievals rely heavily on the type of lidar solutions showing discrepancy between constrained and unconstrained retrievals. Smoke plumes identified within a larger aerosol layer were treated as unconstrained retrievals and resulted in conservative AOD estimates. Conversely, smoke plumes surrounded by clear air were identified as opaque aerosol layers and resulted in higher lidar ratios and AOD values. Measured lidar ratios and particulate depolarization ratios showed values similar to the upper ranges of biomass burning smoke. Results agree with studies that utilized ground-based retrievals, in particular for Ångström exponent (AE) and effective radius (Reff) values. MODIS and CALIPSO retrieval algorithms disagree on AOD ranges, for the most part, due to the extreme light-absorbing nature of these types of aerosols. We believe that these types of studies are a strong indicator for the need of improved aerosol models and retrieval algorithms.

Validating Aerosol Optical Depth Estimation Methods Using the National Institute of Environmental Research Operational Numerical Forecast Model

Currently, significant efforts are being made to enhance the performance of the National Institute of Environmental Research (NIER) operational model. However, the model performance concerning Aerosol Optical Depth (AOD) estimation remains uninvestigated. In this study, three different estimation methods for AOD were implemented using the NIER operational model and validated with satellite and ground observations. In the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) method, AOD exponentially increases with relative humidity owing to a hygroscopic growth factor. However, alternative methods show better performance, since AOD estimation considers the size dependency of aerosol particles and is not sensitive to high relative humidity, which reduces the high AOD in areas with large cloud fractions. Although some R values are significantly low, especially for a single observational comparison and small numerical domain analysis, one of the alternative estimation methods achieves the best performance for diagnosing AOD in the East Asia region.