Missing Aerosol Optical Depth Research Articles

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.

Consecutive retrieval of aerosol optical depth and total suspended solid concentration in turbid coastal water of Eastern China

An algorithm is proposed for consecutive retrieval of aerosol optical depth (AOD) and total suspended solid (TSS) concentration in turbid coastal water using a shortwave infrared band and a visible band. MODIS band 7 (∼2.1 µm) was used to retrieve the AOD in turbid coastal water, as the top-of-atmosphere reflectance in this band is more sensitive than in other bands and water-leaving reflectance (ρw) can be ignored. The aerosol model is determined through iteration by predetermining the proportion range of four basic aerosol modes (dust, water-soluble, ocean and soot). The retrieved AOD is used for atmospheric correction. Based on the Pearson correlation coefficient between TSS and ρw for the different bands, MODIS band 4 was used to obtain ρw. A logarithmic model associated with TSS retrieval was established between ρw and TSS products of GOCI. For the MOD09GA product, the median, moving, adaptive, and Fourier-transform filters were employed to eliminate strip noise with respect to TSS retrieval. The retrieved AOD was strongly correlated with AOD observed at the Huaniao Island site (R = 0.94; RMSE = 0.0437; mean absolute percentage error (MAPE) = 10.9%). The new AOD retrievals are more accurate than MODIS ocean retrievals and can provide missing AOD products in turbid coastal areas. Fourier-filtered TSS has the best retrieval results (RMSE = 1.3538; MAPE = 32.03%) in comparisons between retrieved and in situ TSS.

A novel big data mining framework for reconstructing large-scale daily MAIAC AOD data across China from 2000 to 2020

ABSTRACT Satellite-based aerosol optical depth (AOD) retrieval products are essential in air pollution and climate change research. Unfortunately, cloud contaminations and unfavorable surface conditions result in a considerable proportion of missing AOD data. Numerous studies have been conducted to reconstruct large-scale AOD data gaps by utilizing adjacent spatiotemporal information or modeling AOD data via various external geographical data. However, the erratic variation of AOD and the inconvenience of external data weaken the accuracy and efficiency of reconstruction. To address these issues, a novel big data based iterative variation mining framework (IVMF), utilizing multi-spatiotemporal information on AOD variations, is proposed to reconstruct large-scale AOD data over China from 2000 to 2020. Simulated and real-data experiments are carried out to validate the effectiveness and robustness of the IVMF. Results show that the spatial patterns of the reconstructed AOD are consistent with those of the original AOD in the simulated experiments. The final reconstructed AOD data strongly correlate with in-situ AOD measurements in the real-data experiments (correlation coefficient of 0.91). After reconstruction, the average daily AOD coverage increases from 30.42% to 96.69% (a 218% increase). Besides, results reveal that central China exhibits severe AOD levels, while northwest China presents low AOD levels. Overall, the proposed IVMF can largely resolve the missing AOD data problem with outstanding accuracy and efficiency, and has great potential to be generalized to other regions and remote sensing products.

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

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

High-Resolution Mapping of Aerosol Optical Depth and Ground Aerosol Coefficients for Mainland China

Aerosols play an important role in climate change, and ground aerosols (e.g., fine particulate matter, abbreviated as PM2.5) are associated with a variety of health problems. Due to clouds and high reflectance conditions, satellite-derived aerosol optical depth (AOD) products usually have large percentages of missing values (e.g., on average greater than 60% for mainland China), which limits their applicability. In this study, we generated grid maps of high-resolution, daily complete AOD and ground aerosol coefficients for the large study area of mainland China from 2015 to 2018. Based on the AOD retrieved using the recent Multi-Angle Implementation of Atmospheric Correction advanced algorithm, we added a geographic zoning factor to account for variability in meteorology, and developed an adaptive method based on the improved full residual deep network (with attention layers) to impute extensively missing AOD in the whole study area consistently and reliably. Furthermore, we generated high-resolution grid maps of complete AOD and ground aerosol coefficients. Overall, compared with the original residual model, in the independent test of 20% samples, our daily models achieved an average test R2 of 0.90 (an improvement of approximately 5%) with a range of 0.75–0.97 (average test root mean square error: 0.075). This high test performance shows the validity of AOD imputation. In the evaluation using the ground AOD data from six Aerosol Robotic Network monitoring stations, our method obtained an R2 of 0.78, which further illustrated the reliability of the dataset. In addition, ground aerosol coefficients were generated to provide an improved correlation with PM2.5. With the complete AOD data and ground coefficients, we presented and interpreted their spatiotemporal variations in mainland China. This study has important implications for using satellite-derived AOD to estimate aerosol air pollutants.

Handling Missing Data in Large-Scale MODIS AOD Products Using a Two-Step Model

Aerosol optical depth (AOD) is a key parameter that reflects the characteristics of aerosols, and is of great help in predicting the concentration of pollutants in the atmosphere. At present, remote sensing inversion has become an important method for obtaining the AOD on a large scale. However, AOD data acquired by satellites are often missing, and this has gradually become a popular topic. In recent years, a large number of AOD recovery algorithms have been proposed. Many AOD recovery methods are not application-oriented. These methods focus mainly on to the accuracy of AOD recovery and neglect the AOD recovery ratio. As a result, the AOD recovery accuracy and recovery ratio cannot be balanced. To solve these problems, a two-step model (TWS) that combines multisource AOD data and AOD spatiotemporal relationships is proposed. We used the light gradient boosting (LightGBM) model under the framework of the gradient boosting machine (GBM) to fit the multisource AOD data to fill in the missing AOD between data sources. Spatial interpolation and spatiotemporal interpolation methods are limited by buffer factors. We recovered the missing AOD in a moving window. We used TWS to recover AOD from Terra Satellite’s 2018 AOD product (MOD AOD). The results show that the MOD AOD, after a 3 × 3 moving window TWS recovery, was closely related to the AOD of the Aerosol Robotic Network (AERONET) (R = 0.87, RMSE = 0.23). In addition, the MOD AOD missing rate after a 3 × 3 window TWS recovery was greatly reduced (from 0.88 to 0.1). In addition, the spatial distribution characteristics of the monthly and annual averages of the recovered MOD AOD were consistent with the original MOD AOD. The results show that TWS is reliable. This study provides a new method for the restoration of MOD AOD, and is of great significance for studying the spatial distribution of atmospheric pollutants.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full-coverage PM2.5 predictions with the consideration of these impacts. To estimate the missing AOD, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The “out-of-bag” R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance in the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD for predicting PM2.5 levels with high resolutions and complete coverage.

Incorporating Snow and Cloud Fractions in Random Forest to Estimate High Resolution PM2.5 Exposures in New York State

Satellite aerosol optical depth (AOD) has been widely used to estimate the ground-level PM2.5 concentrations, but cloud/snow covers often lead to a large proportion of non-random missing AOD, causi...

Full-Coverage High-Resolution Daily PM2.5 Estimation using MAIAC AOD in the Yangtze River Delta of China

Background/Aim: Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM2.5); however, non-random missing AOD due to cloud/snow cover or high...

Full-coverage high-resolution daily PM2.5 estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM2.5). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction (MAIAC), provides a valuable opportunity to characterize local-scale PM2.5 at 1-km resolution. However, non-random missing AOD due to cloud/snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM2.5 measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missingness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM2.5 concentrations in 2013 and 2014 at 1km resolution with complete coverage in space and time. The daily MI models have an average R2 of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall model 10-fold cross-validation R2 (root mean square error) were 0.81 (25μg/m3) and 0.73 (18μg/m3) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy. Comparing with previous gap-filling methods, our MI method presented in this study performed better with higher coverage, higher accuracy, and the ability to fill missing PM2.5 predictions without ground PM2.5 measurements. This method can provide reliable PM2.5 predictions with complete coverage that can reduce bias in exposure assessment in air pollution and health studies.

Daily estimation of ground-level PM2.5 concentrations at 4km resolution over Beijing-Tianjin-Hebei by fusing MODIS AOD and ground observations.

The satellite-borne Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) is widely used to estimate ground-level fine ambient particulate matter (PM2.5) concentrations to evaluate their health effects. The associated estimation accuracy is often reduced by AOD missing values and by insufficiently accounting for the spatio-temporal PM2.5 variations. In this study, we aim to estimate ground-level PM2.5 concentrations at a fine resolution with improved accuracy by fusing fine-scale satellite and ground observations in the populated and polluted Beijing-Tianjin-Hebei (BTH) area of China in 2014. We employed a Bayesian-based statistical downscaler to model the spatio-temporal linear AOD-PM2.5 relationships. We used a 3km MODIS AOD product, which was resampled to a 4km resolution in a Lambert conic conformal projection, to assist comparison and fusion with predictions by atmospheric chemistry models. A two-step method was used to fill the missing AOD values to obtain a full AOD dataset with complete spatial coverage. The downscaler has a good performance in the fitting procedure (R2=0.75) and in the cross validation procedure (R2=0.58 by random method and R2=0.47 by city-specific method). The number of missing AOD values was serious and related to elevated PM2.5 concentrations. The gap-filled AOD values corresponded well with our understanding of PM2.5 pollution conditions in BTH. The prediction accuracy of PM2.5 concentrations were improved in terms of their annual and seasonal mean. As a result of its fine spatio-temporal resolution and complete spatial coverage, the daily PM2.5 estimation dataset could provide extensive and insightful benefits to related studies in the BTH area. This may include understanding the formation processes of regional PM2.5 pollution episodes, evaluating daily human exposure, and establishing pollution controlling measures.

Estimating ground-level PM2.5 concentrations over three megalopolises in China using satellite-derived aerosol optical depth measurements

Abstract Numerous previous studies have revealed that statistical models which combine satellite-derived aerosol optical depth (AOD) and PM2.5 measurements acquired at scattered monitoring sites provide an effective method for deriving continuous spatial distributions of ground-level PM2.5 concentrations. Using the national monitoring networks that have recently been established by central and local governments in China, we developed linear mixed-effects (LMEs) models that integrate Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements, meteorological parameters, and satellite-derived tropospheric NO2 column density measurements as predictors to estimate PM2.5 concentrations over three major industrialized regions in China, namely, the Beijing–Tianjin–Hebei region (BTH), the Yangtze River Delta region (YRD), and the Pearl River Delta region (PRD). The models developed for these three regions exploited different predictors to account for their varying topographies and meteorological conditions. Considering the importance of unbiased PM2.5 predictions for epidemiological studies, the correction factors calculated from the surface PM2.5 measurements were applied to correct biases in the predicted annual average PM2.5 concentrations introduced by non-stochastic missing AOD measurements. Leave-one-out cross-validation (LOOCV) was used to quantify the accuracy of our models. Cross-validation of the daily predictions yielded R2 values of 0.77, 0.8 and 0.8 and normalized mean error (NME) values of 22.4%, 17.8% and 15.2% for BTH, YRD and PRD, respectively. For the annual average PM2.5 concentrations, the LOOCV R2 values were 0.85, 0.76 and 0.71 for the three regions, respectively, whereas the LOOCV NME values were 8.0%, 6.9% and 8.4%, respectively. We found that the incorporation of satellite-based NO2 column density into the LMEs model contribute to considerable improvements in annual prediction accuracy for both BTH and YRD. The satisfactory performance of our models indicates that constructing LMEs models using various combinations of predictors for different regions would be helpful for predicting PM2.5 concentrations with high accuracy.

Monsoonal variations in aerosol optical properties and estimation of aerosol optical depth using ground-based meteorological and air quality data in Peninsular Malaysia

Abstract. Obtaining continuous aerosol-optical-depth (AOD) measurements is a difficult task due to the cloud-cover problem. With the main motivation of overcoming this problem, an AOD-predicting model is proposed. In this study, the optical properties of aerosols in Penang, Malaysia were analyzed for four monsoonal seasons (northeast monsoon, pre-monsoon, southwest monsoon, and post-monsoon) based on data from the AErosol RObotic NETwork (AERONET) from February 2012 to November 2013. The aerosol distribution patterns in Penang for each monsoonal period were quantitatively identified according to the scattering plots of the Ångström exponent against the AOD. A new empirical algorithm was proposed to predict the AOD data. Ground-based measurements (i.e., visibility and air pollutant index) were used in the model as predictor data to retrieve the missing AOD data from AERONET due to frequent cloud formation in the equatorial region. The model coefficients were determined through multiple regression analysis using selected data set from in situ data. The calibrated model coefficients have a coefficient of determination, R2, of 0.72. The predicted AOD of the model was generated based on these calibrated coefficients and compared against the measured data through standard statistical tests, yielding a R2 of 0.68 as validation accuracy. The error in weighted mean absolute percentage error (wMAPE) was less than 0.40% compared with the real data. The results revealed that the proposed model efficiently predicted the AOD data. Performance of our model was compared against selected LIDAR data to yield good correspondence. The predicted AOD can enhance measured short- and long-term AOD and provide supplementary information for climatological studies and monitoring aerosol variation.