Aerosol Optical Depth Dataset Research Articles

Summarizing multiple aspects of triple collocation analysis in a single diagram

With the ongoing expansion of global observation networks, it is expected that we shall routinely analyze records of geophysical variables such as temperature from multiple collocated instruments. Validating datasets in this situation is not a trivial task because every observing system has its own bias and noise. Triple collocation is a general statistical framework to estimate the error characteristics in three or more observational-based datasets. In a triple colocation analysis, several metrics are routinely reported but traditional multiple-panel plots are not the most effective way to display information. A new formula of error variance is derived for connecting the key terms in the triple collocation theory. A diagram based on this formula is devised to facilitate triple collocation analysis of any data from observations, as illustrated using three aerosol optical depth datasets from the recent Aerosol Cloud meTeorology Interactions oVer the western ATlantic Experiment (ACTIVATE). An observational-based skill score is also derived to evaluate the quality of three datasets by taking into account both error variance and correlation coefficient. Several applications are discussed and sample plotting routines are provided.

Two decades of high-resolution aerosol product over the Sierra Nevada Mountain region (SE Spain): Spatio-temporal distribution and impact on ecosystems

Atmospheric aerosols play a pivotal role in shaping our environment, impacting climate, human health, and ecosystems. Characterizing the influence of aerosols on ecosystems, especially in mountain environments, is a challenging task due to their complex-orography and scarcity of aerosol ground stations. Satellite-based aerosol data can improve our knowledge over such complex-orography areas. Thus, we have analyzed the Aerosol Optical Depth (AOD) product from the MODerate resolution Imaging Spectrometer (MODIS) sensor produced by the inversion algorithm MultiAngle Implementation of Atmospheric Correction (MAIAC) over the last two decades for the period 2001–2022 with a spatial resolution of 1 × 1 km. Our study focuses on the Sierra Nevada Mountain region and National Park in Southeastern Spain. As a first step, we have validated the AOD from MODIS+MAIAC against three AERONET stations at different altitudes (680 m, 1800 m, and 2500 m above sea level (a.s.l.)). MODIS+MAIAC AOD showed good agreement with the ground-based AOD observations, with R values ranging from 0.75 to 0.82, RMSE values ranging from 0.047 to 0.066 and having 80% of the samples within the expected error (EE) of the product. The MODIS+MAIAC AOD product is able to characterize the fine-scale features of such complex-orography area and hence evaluate the spatio-temporal distribution of the AOD over the mountainous region. We have generated the most extended AOD dataset for a mountainous region, spanning the past two decades. We have deepened into the spatial and seasonal AOD patterns from 2001 to 2022, unveiling elevated AOD values near valleys and urban areas. In general, the AOD values decrease with increasing altitude with the exception of snow-covered areas at high altitudes (>2800 m a.s.l.), which might affect aerosol retrieval and provide bias due to higher-reflecting surfaces and pixel removal. For the first time, the relationship of aerosol loading with ecosystem type has been assessed in the protected environment of Sierra Nevada Natural Park. Monthly AOD trends across different ecosystem types and altitudinal ranges are analyzed in detail over the last two decades. In addition, Generalized Linear Models (GLM) are applied to reveal significant correlations between ecosystems and AOD, irrespective of altitude, latitude or longitude. Based on the interannual variation of AOD over the last two decades, we have analyzed the relationship of AOD with the different ecosystems of Sierra Nevada at 500 m elevation ranges. The patterns of the ecosystem's types are maintained over the elevation ranges 1200–1700 m and 1700–2200 m a.s.l., which demonstrates that land-type has an impact on the AOD product. Furthermore, it is observed that forest-like ecosystems tend to present lower AOD compared with bare-soil or low-growth vegetation ecosystems. In addition, the areas of the mountain closer to Granada city present generalized higher AOD values on the western part of the mountain, regardless of the ecosystem, showing the significant influence of the proximity of urban sites over the ecosystems and the potential impact on the environment.

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.

The Variation in Atmospheric Turbidity over a Tropical Site in Nigeria and Its Relation to Climate Drivers

Atmospheric turbidity exhibits substantial spatial–temporal variability due to factors such as aerosol emissions, seasonal changes, meteorology, and air mass transport. Investigating atmospheric turbidity is crucial for climatology, meteorology, and atmospheric pollution. This study investigates the variation in atmospheric turbidity over a tropical location in Nigeria, utilizing the Ångström exponent (α), the turbidity coefficient (β), the Linke turbidity factor (TL), the Ångström turbidity coefficient (βEST), the Unsworth–Monteith turbidity coefficient (KAUM), and the Schüepp turbidity coefficient (SCH). These parameters were estimated from a six-month uninterrupted aerosol optical depth dataset (January–June 2016) and a one-year dataset (January–December 2016) of solar radiation and meteorological data. An inverse correlation (R = −0.77) was obtained between α and β, which indicates different turbidity regimes based on particle size. TL and βEST exhibit pronounced seasonality, with higher turbidity during the dry season (TL = 9.62 and βEST = 0.60) compared to the rainy season (TL = 0.48 and βEST = 0.20) from May to October. Backward trajectories and wind patterns reveal that high-turbidity months align with north-easterly air flows from the Sahara Desert, transporting dust aerosols, while low-turbidity months coincide with humid maritime air masses originating from the Gulf of Guinea. Meteorological drivers like relative humidity and water vapor pressure are linked to turbidity levels, with an inverse exponential relationship observed between normalized turbidity coefficients and normalized water vapor pressure. This analysis provides insights into how air mass origin, wind patterns, and local climate factors impact atmospheric haze, particle characteristics, and solar attenuation variability in a tropical location across seasons. The findings can contribute to environmental studies and assist in modelling interactions between climate, weather, and atmospheric optical properties in the region.

A deep learning-based imputation method for missing gaps in satellite aerosol products by fusing numerical model data

Satellite-based aerosol optical depth (AOD) products are commonly used in various aerosol-related studies, such as aerosol pollution mapping and aerosol-climate interactions. However, these satellite AOD products often suffer from significant missing gaps due to cloud cover and limitations in the retrieval algorithm. To address this issue, some studies take advantage of real-time seamless simulation of numerical models and successfully fill in these gaps by establishing a regression relationship between satellite AOD and numerical model AOD. However, these previous studies usually use satellite AOD retrievals as the regression target, which limits the accuracy of the imputation results by the original accuracy of satellite AOD retrievals and also consumes a considerable amount of time. To overcome these limitations, this study proposes a spatiotemporal imputation model called Bi-ConvRNN, which combines convolutional neural networks (CNN) and bidirectional recurrent neural networks (Bi-RNN). The model takes both satellite AOD retrievals and numerical model AOD data as input and utilizes the weighted mean squared error (MSE) loss function of multiple AOD datasets, e.g., ground-based data, satellite retrievals, and numerical simulation, as the optimization target to improve the imputation accuracy. The proposed model is evaluated using hourly COMS GOCI AOD products. In the independent test set, the AOD results generated by the Bi-ConvRNN model in the region containing GOCI AOD retrievals can break the accuracy of original GOCI AOD products with the accuracy improved from R2 = 0.70 [RMSE = 0.15] to R2 = 0.84 [RMSE = 0.11], and the filling accuracy, e.g. R2 = 0.79, [RMSE = 0.14], in the region without GOCI AOD retrievals are still better than those of the original GOCI AOD retrievals. Additionally, the Bi-ConvRNN model demonstrates satisfactory filling efficiency, requiring only 0.12 s to fill in the missing gaps of hourly GOCI AOD products per day. These results highlight the efficiency and reliability of the proposed model in filling the gaps in satellite AOD products, and the filled AOD results have great potential for further aerosol-related research.

Spatio-temporal correspondence of aerosol optical depth between CMIP6 simulations and MODIS retrievals over India.

In the present work, the aerosol optical depth (AOD) at 550nm of the moderate resolution imaging spectroradiometer (MODIS) onboard the Terra satellite was utilised to evaluate the AOD simulations of newly emerged general circulation models (GCMs) of coupled model intercomparison project-phase 6 (CMIP6) over the Indian landmass. Further, the AOD from the CMIP6 models has been compared with its previous generation models from CMIP5 to examine the extent of uncertainties in AOD with reference to the MODIS AOD datasets. The evolution of aerosols over India using the different shared socioeconomic pathways (SSPs) has also been studied till the year 2050. The results show that the CMIP5 and CMIP6 models underestimated the mean annual AOD of the Indian region as a whole. A multi-model mean (MMM) of thirteen GCMs from CMIP6 showed an underestimation of AOD by 40 to 60% over the Indo-Gangetic plains, while an overestimation of 60 to 80% in AOD was observed over the Peninsular and Central Indian regions in comparison with MODIS for the study period of 2001 to 2014. In future simulations, the pathway SSP370 has shown a significant increasing trend of AOD whereas SSP126 and SSP585 have shown significant decreasing trends of AOD by the year 2050. In the future, the changes in the AOD will mainly be contributed by the anthropogenic aerosols (AOA, BC, and Sulphates) emissions in all SSPs. The large bias of MMM with the MODIS requires further research in terms of analysing the accuracy of emission datasets that have been used to simulate the AODs by the CMIP6 models over the Indian region.

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.

City-Scale Aerosol Loading Changes in the Sichuan Basin from 2001 to 2020 as Revealed by MODIS 1 km Aerosol Product

Long-term high-resolution monitoring of aerosol optical depth (AOD) is necessary to understand air pollution problems and climate change at regional to urban scales. Based on the 1 km AOD dataset retrieved by the MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC), the spatial-temporal evolutionary trends of AOD in the Sichuan Basin (SCB), Southwest China, and its 17 subordinate cities were analyzed from 2001 to 2020. In the past 20 years, the annual average AOD in SCB gradually decreased from south to north. The highest AOD of SCB in spring was 0.62, followed by an average AOD value of 0.60 in winter. At the city scale, Zigong, Neijiang, and Ziyang were identified as the three most polluted cities within the SCB. The average AOD in the SCB increased to 0.68 and 0.69 in February and March, respectively, and significantly decreased to 0.41 and 0.43 in June and July, respectively. The interannual AOD in the SCB presented an increasing trend from 2001 to 2010, with a range of 0.50 to 0.70, whereas it showed a decreasing trend from 2011 to 2020, with a range of 0.68 to 0.35. In spring, the annual average AOD at the district level showed significant high values from 2005 to 2012. In winter, the interannual AOD increased significantly, with high values concentrated in 2008, 2010, 2011, and 2013. The occurrence frequency of AOD in the SCB was mainly distributed between 0.2~0.5 and 1.5. There also was an increasing trend of AOD in the SCB from 2001 to 2008 and a decreasing trend from 2009 to 2020. The results of this study hold significance for further understanding the climatic characteristics and environmental effects of regional atmospheric aerosols.

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.

Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations

Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development.

Review of retrieval of aerosol optical depth to estimate particle concentration and its challenges based on spatiotemporal relationships by various spectroradiometer models

This review was carried out to understand the retrieval of aerosol optical depth (AOD) datasets for estimating particle concentration and its influence on ambient air and surroundings by various models. Several studies have evaluated particle (PM10 and PM2.5) concentration profiles present in the lowest layers of the atmosphere by using AOD datasets. This study aimed at identifying consistent and precise particle estimation by various datasets retrieved by satellite‐based models for the ground‐level PM concentration. Extremities of satellite sensor data, like specific capabilities, as well as a few drawbacks are presented. Multi‐angle imaging spectroradiometer (MISR), visible infrared imaging radiometer suite (VIIRS) datasets, mixed‐effect model (MEM), and geographically weighted regression (GWR) models outperformed to estimate AOD and PM in comparison with the moderate resolution imaging spectroradiometer (MODIS) and other datasets. The improvised algorithms with higher resolution in the upcoming research would provide an even better estimation for AOD and PM.

Estimating PM2.5 concentrations in a central region of China using a three-stage model

ABSTRACT Owing to uneven environmental monitoring site distribution, there are significant spatial data gaps for concentrations of ambient fine particles with diameters ≤ 2.5 µm (PM2.5) obtained using traditional monitoring methods. Satellite products are an alternative data source for locations where monitoring sites are unavailable. The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) product has been widely used in PM2.5 assessment for years; however, it has obvious data gaps in winter. Here, the Visible Infrared Imaging Radiometer Suite (VIIRS) AOD was applied to supplement MODIS AOD data to obtain a fused AOD dataset. A three-stage model consisting of a corrected AOD model, mixed effects model, and geographically weighted regression model was developed and used with meteorological and vegetation factors to estimate PM2.5. Results showed overall model fitting by cross-validation (CV) with an R2 of 0.92, mean absolute error of 5.72 µg/m3, and root mean square error of 7.15 µg/m3. The combination of MODIS AOD and VIIRS AOD was a suitable method for enhancing AOD coverage. The CV R2 value of the three-stage model (0.92) was higher than that of the two-stage model (0.9). Hence, the three-stage model could achieve a better fit in estimating PM2.5 on a regional scale.

Himawari-8 High Temporal Resolution AOD Products Recovery: Nested Bayesian Maximum Entropy Fusion Blending GEO With SSO Satellite Observations

High temporal resolution aerosol optical depth (AOD) observations derived from new-generation geostationary (GEO) satellite possess unique advantages in analyzing aerosol fast variation processes and thereby providing more accurate assessments on their climate effects and health risks. Unfortunately, the expected advantages and values are dramatically limited by relatively large proportion of data missing in the GEO AOD products due to cloud obscuration and intrinsic retrieval algorithm. Although several data recovery algorithms have been proposed in recent years to improve the spatial coverage for GEO AOD products, yet most of them aims at filling up the data blanks rather than reconstructing the temporally continuous variation of aerosol. Accordingly, in this study, a novel framework of nested spatiotemporal fusion blending GEO with sun-synchronous orbit (SSO) satellite observations based on Bayesian maximum entropy (BME) theorem is developed for GEO Advanced Himawari-8 Imager (AHI) AOD recovery with the sufficient excavation of complementary information from GEO and SSO satellite observations, where the minute-stage and hour-stage BME fusion are jointly employed to reconcile temporal inconsistency and data discrepancies between GEO and SSO observations. The results demonstrate that the AOD spatial coverage is dramatically increased by 240.9% (from 20.5% to 70%) with ensured accuracy after Nested-BME fusion. Additionally, two case analyses, during the development and dispersion processes of haze respectively, both demonstrate that the proposed Nested-BME fusion framework could reconstruct the reliable aerosol diurnal variation trends on the basis of recovering missing data for Himawari-8 AHI AOD datasets, while the AHI official level-2 and level-3 AOD products fail to capture these key trends. Furthermore, the developed Nested-BME AOD fusion framework is also applicable for other geostationary satellites over other regions, which could substantially enhance the availability and value of high temporal resolution AOD products for better scientific applications.

Full-coverage 250 m monthly aerosol optical depth dataset (2000–2019) amended with environmental covariates by an ensemble machine learning model over arid and semi-arid areas, NW China

Abstract. Aerosols are complex compounds that greatly affect the global radiation balance and climate system and even human health; in addition, aerosols are currently a large source of uncertainty in the numerical simulation process. The arid and semi-arid areas have fragile ecosystems with abundant dust but lack related high-accuracy aerosol data. To solve these problems, we use the bagging trees ensemble model, based on 1 km aerosol optical depth (AOD) data and multiple environmental covariates, to produce a monthly advanced-performance, full-coverage, and high-resolution (250 m) AOD product (named FEC AOD, fusing environmental covariates AOD) covering the arid and semi-arid areas. Then, based on the FEC AOD products, we analyzed the spatiotemporal AOD pattern and further discussed the interpretation of environmental covariates to AOD. The results show that the bagging trees ensemble model has a good performance, with its verification R2 values always remaining at 0.90 and the R2 being 0.79 for FEC AOD compared with AERONET AOD product. The high-AOD areas are located in the Taklimakan Desert and on the Loess Plateau, and the low-AOD areas are concentrated in southern Qinghai province. The higher the AOD, the stronger the interannual variability. Interestingly, the AOD reflected a dramatic decrease on the Loess Plateau and an evident increase in the south-eastern Taklimakan Desert, while the southern Qinghai province AODs showed almost no significant change between 2000 and 2019. The annual variation characteristics show that the AOD was largest in spring (0.267±0.200) and smallest in autumn (0.147±0.089); the annual AOD variation pattern showed different features, with two peaks in March and August over Gansu province but only one peak in April in other provinces/autonomous regions. Farmlands and construction lands have high AOD levels compared to other land cover types. Meteorological factors demonstrate the maximum interpretation ability of the AODs on all set temporal scales, followed by the terrain factors, while surface properties have the smallest explanatory abilities; the corresponding average contributions are 77.1 %, 59.1 %, and 50.4 %, respectively. The capability of the environmental covariates to explain the AOD varies seasonally in the following sequence: winter (86.6 %) > autumn (80.8 %) > spring (79.9 %) > summer (72.5 %). In this research, we provide a pathbreaking high spatial resolution (250 m) and long time series (2000–2019) FEC AOD dataset covering arid and semi-arid regions to support atmospheric and related studies in northwest China; the full dataset is available at https://doi.org/10.5281/zenodo.5727119 (Chen et al., 2021b).

Retrieval and validation of long-term aerosol optical depth from AVHRR data over China

ABSTRACT Advanced Very High Resolution Radiometer (AVHRR) onboard National Oceanic and Atmospheric Administration (NOAA) satellites can provide over 40 years of global remote sensing observations, which can be used to retrieve long-term aerosol optical depth (AOD). This is of great significance to the study of global climate change. In this paper, we proposed an algorithm to jointly calculate AOD and land surface properties from AVHRR observations. With assumptions that AOD doesn’t vary in adjacent space and earth surface property doesn’t vary in two days, the algorithm considered non-Lambertian surface reflection based on the shape of bidirectional reflectance distribution function (BRDF shape) and obtained AOD and surface property by optimal estimation (OE) method. The algorithm has been applied to NOAA-7, 9, 11, 14, 16, 18, and 19 satellites and AVHRR-retrieved AOD with 5 × 10 km over China (15°–60° N, 70°–140°E) has been obtained from 1982 to 2016. Comparisons of AVHRR-retrieved AOD against AErosol RObotic NETwork (AERONET) (in and around China) and China Aerosol Remote Sensing Network (CARSNET) AOD show good consistency with 62.62% points within the uncertainty of Δτ = ± (0.05 + 0.25τ) and root-mean-square error (RMSE) of 0.26. Further comparison of the monthly mean AOD of multiple AOD datasets in the ‘Beijing’, ‘Dalanzadgad’, ‘NCU_Taiwan’ and ‘Kanpur’ stations shows that the results of the algorithm are stable. The yearly averaged AOD data also has similar agreements with MERRA-2 (The Modern-Era Retrospective analysis for Research and Applications, Version 2) and AVHRRDB data (AVHRR ‘Deep Blue’ aerosol data set). The multi-year mean correlation coefficient is 0.70 and 0.61 and the percentages within the uncertainty are 80.01% and 67.29% compared with MERRA-2 AOD and AVHRRDB AOD respectively.

High spatiotemporal resolution estimation of AOD from Himawari-8 using an ensemble machine learning gap-filling method

The data incompleteness of aerosol optical depth (AOD) products and their lack of availability in highly urbanized areas limit their great potential of application in air quality research. In this study, we developed an ensemble machine-learning approach that integrated random forest-based Space Interpolation Model (SIM) and deep neural network-based Time Interpolation Model (TIM) to achieve high spatiotemporal resolution dataset of AOD. The spatial interpolation model first filled the spatial gaps in the Level-2 Himawari-8 hourly AOD product in 0.05° (∼5 km) spatial resolution, while the time interpolation model further improved the temporal resolution to 10 min on its basis. A full-coverage AOD dataset of Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) in 2020 was obtained as a practical implementation. The validation against in-situ AOD observations from AERONET and SONET indicated that this new dataset was satisfactory (R = 0.80), and especially in spring and summer. Overall, our ensemble machine-learning model provided an effective scheme for reconstruction of AOD with high spatiotemporal resolution of 0.05° and 10 min, which may further advance the near-real-time monitoring of air-quality in urban areas.

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.

Performance of MODIS Deep Blue Collection 6.1 Aerosol Optical Depth Products Over Indonesia: Spatiotemporal Variations and Aerosol Types

This study aims to evaluate the performance of the long-term Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection 6.1 (C6.1) in determining the spatiotemporal variation of aerosol optical depth (AOD) and aerosol types over Indonesia. For this purpose, monthly MODIS DB AOD datasets are directly compared with Aerosol Robotic Network (AERONET) Version 3 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 8 sites throughout Indonesia. The results indicate that MODIS DB AOD retrievals and AERONET AOD measurements have a high correlation in Sumatra Island (i.e., Kototabang (r = 0.88) and Jambi (r = 0.9)) and Kalimantan Island (i.e., Palangkaraya (r = 0.89) and Pontianak (r = 0.92)). However, the correlations are low in Bandung, Palu, and Sorong. In general, MODIS DB AOD tends to overestimate AERONET AOD at all sites by 16 to 61% and can detect extreme fire events in Sumatra and Kalimantan Islands quite well. Aerosol types in Indonesia mostly consist of clean continental, followed by biomass burning/urban industrial and mixed aerosols. Palu and Sorong had the highest clean continental aerosol contribution (90%), while Bandung had the highest biomass burning/urban-industrial aerosol contribution to atmospheric composition (93.7%). For mixed aerosols, the highest contribution was found in Pontianak, with a proportion of 48.4%. Spatially, the annual mean AOD in the western part of Indonesia is higher than in the eastern part. Seasonally, the highest AOD is observed during the period of September–November, which is associated with the emergence of fire events.

SPATIOTEMPORAL RECOVERY OF HIMAWARI-8 HOURLY AEROSOL OPTICAL DEPTH PRODUCTS VIA THE NESTED BAYESIAN MAXIMUM ENTROPY METHOD

Abstract. Satellite-derived aerosol optical depth (AOD) is an indispensable parameter when conducting studies related to atmospheric environment, climate change, and biogeochemical cycle. However, current satellite-derived AOD products are limited in related applications due to the large proportion of missing data, and the existed methods mainly concentrate on recovering AOD from polar-orbit satellite sensors. In order to solve these issues and take full use of the preponderance of geostationary satellite sensors in high frequency observation, we propose a spatiotemporal AOD recovery framework integrating multi-time scale AOD products based on the nested Bayesian maximum entropy methodology (NBME), aimed to obtain satellite-derived AOD datasets with low data missing and high accuracy. The experiment results show that the spatial coverage of AOD datasets increases from 20.5% to 70.0%, and the R2 and RMSE of the recovered AOD against ground-based AERONET AOD are approximately 0.62 and 0.19, respectively. Moreover, the further simulated experiments indicate that the proposed method also performs better relatively when comparing with other popular recovery methods. Therefore, the proposed NBME recovery method can obtain a more convincing product both in applicable accuracy and visual quality.