Simplified Aerosol Retrieval Algorithm Research Articles

Retrieval of high-resolution aerosol optical depth (AOD) using Landsat 8 imageries over different LULC classes over a city along Indo-Gangetic Plain, India.

Aerosol optical depth (AOD) serves as a crucial indicator for assessing regional air quality. To address regional and urban pollution issues, there is a requirement for high-resolution AOD products, as the existing data is of very coarse resolution. To address this issue, we retrieved high-resolution AOD over Kanpur (26.4499°N, 80.3319°E), located in the Indo-Gangetic Plain (IGP) region using Landsat 8 imageries and implemented the algorithm SEMARA, which combines SARA (Simplified Aerosol Retrieval Algorithm) and SREM (Simplified and Robust Surface Reflectance Estimation). Our approach leveraged the green band of the Landsat 8, resulting in an impressive spatial resolution of 30m of AOD and rigorously validated with available AERONET observations. The retrieved AOD is in good agreement with high correlation coefficients (r) of 0.997, a low root mean squared error of 0.035, and root mean bias of - 4.91%. We evaluated the retrieved AOD with downscaled MODIS (MCD19A2) AOD products across various land classes for cropped and harvested period of agriculture cycle over the study region. It is noticed that over the built-up region of Kanpur, the SEMARA algorithm exhibits a stronger correlation with the MODIS AOD product compared to vegetation, barren areas and water bodies. The SEMARA approach proved to be more effective for AOD retrieval over the barren and built-up land categories for harvested period compared with the cropping period. This study offers a first comparative examination of SEMARA-retrieved high-resolution AOD and MODIS AOD product over a station of IGP.

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

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

Estimation of high-resolution aerosol optical depth (AOD) from Landsat and Sentinel images using SEMARA model over selected locations in South Asia

The study investigates a high-resolution retrieval of AOD over complex surfaces and various aerosol regimes of South Asian countries such as China, Bangladesh and Nepal from Landsat 7/8/9 and Sentinel 2 images. To estimate AOD from these satellite images, unlike the conventional radiative transfer-based look-up-table (LUT) approach, this study uses SEMARA coupled model which combines a simplified and robust surface reflection estimation (SREM) model and a simplified aerosol retrieval algorithm (SARA). The SEMARA model uses top-of-atmospheric reflectance (TOA) and land surface reflectance (LSR), solar and sensors viewing geometries, including the in-situ AOD data from the Aerosol Robotic Network (AERONET) station to simulate AOD at 30 m and 10 m spatial resolutions. The results are compared with AERONET and MODIS-based AOD for the validation sites in the South Asian countries. A high degree of agreement with AERONET AOD is obtained over Beijing in China (R2 > 0.9) for all sensors, while it varies for the validation AERONET stations over Nepal and Bangladesh (R2 = 0.40–0.86). The accuracy metrices suggest a low uncertainty over China Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) for Beijing: 0.07 and 0.13), while it remains higher for stations over Nepal and Bangladesh (MAE and RMSE: 0.33, and 0.41; 0.15–0.27, respectively). >85% of the modelled observations fall within expected error (EE) for China, while 20–75% of observations fall within EE for Nepal and Bangladesh. We have also obtained a proximity effect for the accuracy of the estimated AOD from the SEMARA model. The findings, in general, suggest that the complexity in surface cover and aerosol type produce a higher offset with respect to AERONET observations over Bangladesh and Nepal, and therefore further validation studies are needed for other parts of the globe to measure the consistency of the model.

GOCI-II geostationary satellite hourly aerosol optical depth obtained by data-driven methods: Validation and comparison

The Geostationary Ocean Color Imager II (GOCI-II) deployed on board GEO-KOMPSAT 2B (GK2B) satellite can observe the earth with high frequency, which is of great significance to the dynamic monitoring of regional aerosol optical depth (AOD). However, the lack of short-wave infrared bands makes the classical methods based on radiative transfer analysis difficult to achieve retrieval of aerosols. The data-driven methods based on learning from massive samples can achieve high-accuracy retrieval of aerosols on the local, regional and global scale. However, the samples required for the data-driven method are difficult to obtain due to the short operation time of the GK2B satellite and insufficient ground-based measurements. Aiming to solve such a problem, the Simplified and Robust Surface Reflectance Estimation and Simplified Aerosol Retrieval Algorithm (SEMARA) were aggregated to be applied in the study. The SEMARA method using ground-based observation can retrieve local AOD with high reliability and accuracy, which can be used to construct high-quality and high-quantity samples for training with higher efficiency compared with traditional sample construct methods. Four data-driven methods: Fully Connected Neural Network (FCNN), Random Forest (RF), Support Vector Machine (SVM) and eXtreme Gradient Boosting (XGBoost), were chosen to retrieve the AOD over South Korea in 2021 and 2022 based on GOCI-II. The results were validated and compared using ground-based measurements from the Aerosol Robotic Network (AENRONET) sites and show that the FCNN method has high accuracy and stability, followed by, the RF and XGBoost methods, while the SVM results significantly overestimate the spatial distribution of the AOD.

Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA)

Satellite Aerosol Optical Depth Retrieval Based on Fully Connected Neural Network (FCNN) and a Combine Algorithm of Simplified Aerosol Retrieval Algorithm and Simplified and Robust Surface Reflectance Estimation (SREMARA)

Integration of Surface Reflectance and Aerosol Retrieval Algorithms for Multi-Resolution Aerosol Optical Depth Retrievals over Urban Areas

The SEMARA approach, an integration of the Simplified and Robust Surface Reflectance Estimation (SREM) and Simplified Aerosol Retrieval Algorithm (SARA) methods, was used to retrieve aerosol optical depth (AOD) at 550 nm from a Landsat 8 Operational Land Imager (OLI) at 30 m spatial resolution, a Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) at 500 m resolution, and a Visible Infrared Imaging Radiometer Suite (VIIRS) at 750 m resolution over bright urban surfaces in Beijing. The SEMARA approach coupled (1) the SREM method that is used to estimate the surface reflectance, which does not require information about water vapor, ozone, and aerosol, and (2) the SARA algorithm, which uses the surface reflectance estimated by SREM and AOD measurements obtained from the Aerosol Robotic NETwork (AERONET) site (or other high-quality AOD) as the input to estimate AOD without prior information on the aerosol optical and microphysical properties usually obtained from a look-up table constructed from long-term AERONET data. In the present study, AOD measurements were obtained from the Beijing AERONET site. The SEMARA AOD retrievals were validated against AOD measurements obtained from two other AERONET sites located at urban locations in Beijing, i.e., Beijing_RADI and Beijing_CAMS, over bright surfaces. The accuracy and uncertainties/errors in the AOD retrievals were assessed using Pearson’s correlation coefficient (r), root mean squared error (RMSE), relative mean bias (RMB), and expected error (EE = ± 0.05 ± 20%). EE is the envelope encompassing both absolute and relative errors and contains 68% (±1σ) of the good quality retrievals based on global validation. Here, the EE of the MODIS Dark Target algorithm at 3 km resolution is used to report the good quality SEMARA AOD retrievals. The validation results show that AOD from SEMARA correlates well with AERONET AOD measurements with high correlation coefficients (r) of 0.988, 0.980, and 0.981; small RMSE of 0.08, 0.09, and 0.08; and small RMB of 4.33%, 1.28%, and −0.54%. High percentages of retrievals, i.e., 85.71%, 91.53%, and 90.16%, were within the EE for Landsat 8 OLI, MODIS, and VIIRS, respectively. The results suggest that the SEMARA approach is capable of retrieving AOD over urban areas with high accuracy and small errors using high to medium spatial resolution satellite remote sensing data. This approach can be used for aerosol monitoring over bright urban surfaces such as in Beijing, which is frequently affected by severe dust storms and haze pollution, to evaluate their effects on public health.

Retrieving spatial variation of aerosol level over urban mixed land surfaces using Landsat imageries: Degree of air pollution in Dhaka Metropolitan Area

Aerosols, or suspended particles in the atmosphere, are critical in the production of cloud nuclei, climate change, poor air quality and reduced ambient visibility, as well as public health and disease. Since there are limited ground monitoring stations in the Dhaka Metropolitan Area (DMA), calculating the degree of Aerosol Optical Depth (AOD) and PM2.5 is thus a challenge. The research aimed to retrieve the AOD at 30 m spatial resolution using the Simplified Aerosol Retrieval Algorithm (SARA) based on Landsat imageries. Landsat 4–5 and 8 cloud-free images at 30 m spatial resolution were obtained from 1999 to 2019 at 5 years interval to retrieve AOD and estimate Particulate Matter 2.5 (PM2.5) concentrations. The results showed significant increase in PM2.5 concentrations and the average PM2.5 were estimated for the year of 1999, 2004, 2009, 2014, 2019 were 48.41, 55.26, 66.42, 72.9 and 90.13 μg/m3, respectively. The AOD, mostly generated from the built-up area was 2.6 in 2019, which was 3 times higher than in 1999. Besides, to find out the relationship among AOD, LST (Land Surface Temperature) and NDVI (Normalized Difference Vegetation Index), results revealed a strong positive correlation of AOD with LST but negative correlation with NDVI. Based on AERONET and NASA SEDAC data, the AOD and PM2.5 were validated using statistically significant (p value 0.05) correlation analysis. By presenting a growing pattern and coverage of air pollution over time, the study will assist urban planners, policymakers, and health sectors.

Assessment and valuation of health impacts of fine particulate matter during COVID-19 lockdown: a comprehensive study of tropical and sub tropical countries.

A novel coronavirus disease (COVID-19) continues to challenge the whole world. The disease has claimed many fatalities as it has transcended from one country to another since it was first discovered in China in late 2019. To prevent further morbidity and mortality associated with COVID-19, most of the countries initiated a countrywide lockdown. While physical distancing and lockdowns helped in curbing the spread of this novel coronavirus, it led to massive economic losses for the nations. Positive impacts have been observed due to lockdown in terms of improved air quality of the nations. In the current research, ten tropical and subtropical countries have been analysed from multiple angles, including air pollution, assessment and valuation of health impacts and economic loss of countries during COVID-19 lockdown. Countries include Brazil, India, Iran, Kenya, Malaysia, Mexico, Pakistan, Peru, Sri Lanka, and Thailand. Validated Simplified Aerosol Retrieval Algorithm (SARA) binning model is used on data collated from moderate resolution imaging spectroradiometer (MODIS) for particulate matters with a diameter of less than 2.5 μm (PM2.5) for all the countries for the month of January to May 2019 and 2020. The concentration results of PM2.5 show that air pollution has drastically reduced in 2020 post lockdown for all countries. The highest average concentration obtained by converting aerosol optical depth (AOD) for 2020 is observed for Thailand as 121.9 μg/m3 and the lowest for Mexico as 36.27 μg/m3. As air pollution is found to decrease in the April and May months of 2020 for nearly all countries, they are compared with respective previous year values for the same duration to calculate the reduced health burden due to lockdown. The present study estimates that cumulative about 100.9 Billion US$ are saved due to reduced air pollution externalities, which are about 25% of the cumulative economic loss of 435.9 Billion US$.Graphical abstract Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-021-13813-w.

Evaluation and modification of SARA high-resolution AOD retrieval algorithm during high dust loading conditions over bright desert surfaces

Many studies have been carried out for the validation of Aerosol Optical Depth (AOD) retrieved using MODerate resolution Imaging Spectroradiometer (MODIS) satellite data at the spatial resolution of several to several tens of km. Most of the validation efforts have focused on urban areas with fewer studies focused on deserts with high amounts of dust. On the other hand, calculating AOD at a high spatial resolution over desert surfaces is of high interest to identify local and accurate dust source regions. In this study, two different high-resolution MODIS AOD retrieval algorithms were evaluated to investigate the nature of high dust loading events over the Middle East region. These algorithms include the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution based on the MOD09 Level 2 daily (original SARAMOD09) and MCD43A4 16 day (modified SARAMCD43) surface reflectance products. These algorithms were validated using AERONET AOD observations at Kuwait University, and accuracy was evaluated using descriptive statistics and Expected Error (EE) over land. Also, the results of these algorithms were evaluated with the Collection 6 (C6) Deep Blue algorithm at 10 km spatial resolution (DB10km). Results show that the SARAMCD43 and SARAMOD09 AOD retrievals are well correlated (r) with AERONET data (SARAMCD43 = 0.89 and SARAMOD09 = 0.84), with a larger number of retrievals falling within the Expected Error (SARAMCD43 = 86% and SARAMOD09 = 77%) than the DB (DB10km = 60%) AOD product. The SARAMCD43 AOD exhibited good agreement with DB10km AOD over desert surfaces areas during high aerosol loading events, with an average correlation coefficient of 0.92, RMSE of 0.09, and mean absolute error (MAE) of 0.08. While SARAMOD09 AOD over background desert areas is similar to SARAMCD43, significant differences were observed over high aerosol loading areas and SARAMOD09 dramatically underestimates AOD. The results demonstrate that: (i) the SARA algorithm (regardless of surface reflectance data used) is more robust than the MODIS AOD products (both DT and DB), and is better able to represent spatial dust aerosol conditions with an accurate AOD over desert areas over a range of low to high dust pollutant levels; (ii) SARAMCD43 is well correlated with the DB AOD observations and provides AOD with much greater accuracy and reliability than SARAMOD09 over desert areas during severe dust loading events.

Mapping daily PM2.5 at 500 m resolution over Beijing with improved hazy day performance

The application of satellite-derived aerosol optical depth (AOD) to infer surface PM2.5 has significantly increased the spatial coverage and resolutions (1–10 km) of ground-level PM2.5 mapping as required for accurate exposure estimation. The remaining challenge is to further increase the mapping resolution to the sub-km level with improved algorithms to minimize misrepresentation of severe haze as clouds. In this study, we provide the first daily PM2.5 estimation over Beijing at a 500 m resolution using AOD from the Simplified Aerosol Retrieval Algorithm (SARA) and linear mixed effects model. A novel cloud screen method is developed which significantly improves data availability during hazy days. The cross-validation R2 for PM2.5 estimations is 0.82 with the cloud-screened SARA AOD. Based on the satellite-predicted high-resolution PM2.5 map, all-day population-weighted PM2.5 is estimated to be 81.4 μg m−3 over Beijing (2.3 times higher than China's NAAQS of 35 μg m−3). Compared to the standard MODIS Dark Target 3 km product which presents a significant percentage of missing data, the 500 m resolution PM2.5 mapping derived from SARA AOD reveals distinct pollution patterns and population exposure conditions during severe hazy days, thereby providing valuable information for pollution control and epidemiological studies.

A simplified aerosol retrieval algorithm for Himawari-8 Advanced Himawari Imager over Beijing

High temporal aerosol optical depth (AOD) is useful for inversing modelling of emissions and for comprehensively understanding the climatology of aerosol. Geostationary satellite has been used to derive AOD over land and ocean. In this study, a novel simplified aerosol retrieval algorithm for Advanced Himawari-8 imager (SARAHI) is developed to retrieve hourly AOD over Beijing from a new-generation geostationary meteorological satellite—Himawari-8, which carries the Advanced Himawari Imager (AHI). The algorithm is based on a simplified radiative transfer model and surface reflectance derived from one-month AHI apparent data. Measurements from AErosol RObotic NETwork (AERONET) sites and JAXA Himawari Monitor Version 2.0 Aerosol Retrieval product (L2ARP2.0) are used to evaluate the performance of AOD retrievals from SARAHI algorithm. There are 73.5%, 78.2%, 74.7% of AOD retrievals falling in the Expected error lines at Beijing_CAMS, Beijing_RADI, and Xianghe sites, respectively. The correlation coefficient is larger than 0.92 with small root mean square error (RMSE) of 0.134–0.164. The retrievals can also reflect the temporal variations of AOD very well. The results indicate that the SARAHI AOD algorithm can derive the regional AOD accurately and is better than the L2ARP2.0 AOD.

Estimation of High-Resolution Surface Shortwave Radiative Fluxes Using SARA AOD over the Southern Great Plains

Atmospheric aerosol optical depth (AOD) plays a determinant role in estimations of surface shortwave (SW) radiative fluxes. Therefore, this study aims to develop a hybrid scheme to produce surface SW fluxes, based on AOD at 1-km spatial resolution retrieved from the Simplified Aerosol Retrieval Algorithm (SARA) and several Terra MODIS land and atmospheric products (i.e., geolocation properties, water vapor amount, total ozone column, surface reflectance, and top-of-atmosphere (TOA) radiance). Estimations based on SARA were made over the Southern Great Plains (SGP) under cloud-free conditions in 2014 and compared with estimations based on the latest Terra MODIS AOD product at 3-km resolution. Validation against ground-based measurements showed that SARA-based fluxes obtain lower RMSE and bias values compared with MODIS-based estimations. MODIS-based downward and net fluxes are satisfactory, while the direct and diffuse components are less reliable. The results demonstrate that the SARA-based scheme produces better surface SW radiative fluxes than the MODIS-based estimates provided in this and other similar studies and that these fluxes are comparable to existing CERES data products which have been tested over the SGP.

Satellite-based PM2.5 estimation using fine-mode aerosol optical thickness over China

Accurate estimation of ground-level PM2.5 from satellite-derived aerosol optical thickness (AOT) presents various difficulties. This is because the association between AOT and surface PM2.5 can be affected by many factors, such as the contribution of fine mode AOT (FM-AOT) and the weather conditions. In this study, we compared the total AOT and FM-AOT for surface PM2.5 estimation using ground-based measurements collected in Xingtai, China from May to June 2016. The correlation between PM2.5 and FM-AOT was higher (r = 0.74) than that between PM2.5 and total AOT (r = 0.49). Based on FM-AOT, we developed a ground-level PM2.5 retrieval method that incorporated a Simplified Aerosol Retrieval Algorithm (SARA) AOT, look-up table–spectral deconvolution algorithm (LUT-SDA) fine mode fraction (FMF), and the PM2.5 remote sensing method. Due to the strong diurnal variations displayed by the particle density of PM2.5, we proposed a pseudo-density for PM2.5 retrieval based on real-time visibility data. We applied the proposed method to determine retrieval surface PM2.5 concentrations over Beijing from December 2013 to June 2015 on cloud-free days. Compared with Aerosol Robotic Network (AERONET) data, the LUT-SDA FMF was more easily available than the Moderate Resolution Imaging Spectroradiometer (MODIS) FMF. The derived PM2.5 results were compared with the ground-based monitoring values (30 stations), yielding an R2 of 0.64 and root mean square error (RMSE) = 18.9 μg/m3 (N = 921). This validation demonstrated that the developed method performed well and produced reliable results.

A MODIS-based modeling scheme for the estimation of downward surface shortwave radiation under cloud-free conditions

Atmospheric aerosol optical depth (AOD) plays an important role in radiation modeling and partly determines the accuracy of estimated downward surface shortwave radiation (DSSR). In this study, Iqbal’s model C was used to estimate DSSR under cloud-free conditions over the Koohin and Chitgar sites in Tehran, Iran; the estimated DSSR was based on (1) our proposed hybrid modeling scheme where the AOD is retrieved using the Simplified Aerosol Retrieval Algorithm (SARA), ground-based measurements at the AERONET site in Zanjan and (2) the AOD from the Terra MODerate-resolution Imaging Spectroradiometer (MODIS) sensor. Several other Terra MODIS land and atmospheric products were also used as input data, including geolocation properties, water vapor, total ozone, surface reflectance, and top-of-atmosphere (TOA) radiance. SARA-based DSSR and MODIS-based DSSR were evaluated with ground-based DSSR measurements at the Koohin and Chitgar sites in 2011 and 2013, respectively; the averaged statistics for SARA-based DSSR [R2 ≈ 0.95, RMSE ≈ 22 W/m2 (2.5% mean value), and bias ≈ 3 W/m2] were stronger than those for MODIS-based DSSR [R2 ≈ 0.79, RMSE ≈ 51 W/m2 (5.8% mean value), and bias ≈ 34 W/m2]. These results show that the proposed hybrid scheme can be used at regional to global scales under the assumption of future access to spatially distributed AERONET sites. Additionally, the robustness of this modeling scheme was exemplified by estimating the aerosol radiative forcing (ARF) during a dust storm in Southwest Asia. The results were comparable to those of previous studies and showed the strength of our modeling scheme.

A New Approach for Estimation of Fine Particulate Concentrations Using Satellite Aerosol Optical Depth and Binning of Meteorological Variables

ABSTRACTFine particulate matter (PM2.5) has recently gained attention worldwide as being responsible for severe respiratory and cardiovascular diseases, but point based ground monitoring stations are inadequate for understanding the spatial distribution of PM2.5 over complex urban surfaces. In this study, a new approach is introduced for prediction of PM2.5 which uses satellite aerosol optical depth (AOD) and binning of meteorological variables. AOD from the MODerate resolution Imaging Spectroradiometer (MODIS) Collection 6 (C006) aerosol products, MOD04_3k Dark-Target (DT) at 3 km, MOD04 DT at 10 km, and MOD04 Deep-Blue (DB) at 10 km spatial resolution, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m resolution were obtained for Hong Kong and the industrialized Pearl River Delta (PRD) region. The SARA AOD at 500 m alone achieved a higher correlation (R = 0.72) with PM2.5 concentrations than the MODIS C6 DT AOD at 3 km (R = 0.60), the DT AOD at 10 km (R = 0.61), and the DB AOD at 10 km (R = 0.51). The SARA binning model ([PM2.5] = 110.5 [AOD] + 12.56) was developed using SARA AOD and binning of surface pressure (996–1010 hPa). This model exhibits good correlation, accurate slope, low intercept, low errors, and accurately represents the spatial distribution of PM2.5 at 500 m resolution over urban areas. Overall, the prediction power of the SARA binning model is much better than for previous models reported for Hong Kong and East Asia, and indicates the potential value of applying meteorologically-specific empirical models and incorporating boundary layer height in operational PM2.5 forecasting from satellite AOD retrievals.

Validation of MODIS and VIIRS derived aerosol optical depth over complex coastal waters

In this study, the Simplified Aerosol Retrieval Algorithm (SARA) was applied to both MODIS and Visible Infrared Imaging Radiometer Suits (VIIRS) images to retrieve aerosol optical depth (AOD) over water for blue, green and red wavelengths at 500m (MODISSARA) and 750m (VIIRSSARA) resolutions, respectively. Retrievals were compared with the Terra–MODIS Dark Target (DT) AOD at 3km resolution (MODIS3K) and the VIIRS Environmental Data Record (VIIRSEDR) AOD at 6km resolution. Validation was conducted using 86 Microtops II Sun photometer AOD measurements collected over different classes of water quality (low to high sediment levels) for seven days (6, 8, 9, 13, and 15, 16 and 17 October 2014) between 10:00 to 15:00 local time. Thirty-eight to fifty percent and 44–54% of the MODIS3K and the VIIRSEDR AOD retrievals respectively, fall within the expected error (EE) with root mean square error from 0.12 to 0.14 and mean absolute error from 0.10 to 0.11. The MODISSARA and the VIIRSSARA AOD retrievals are well correlated with the ground–based measurements (R: MODISSARA=0.86–0.89 and VIIRSSARA=0.85–0.94), with a larger number of retrievals falling within the EE MODISSARA=73–77% and VIIRSSARA=71–82%) than MODIS3K and VIIRSEDR. The results indicate that the SARA algorithm is more robust than MODIS3K and VIIRSEDR global AOD products, and can retrieve accurate AOD over low to highly sedimented water surfaces, similar to good AOD retrievals over land.

Evaluation of MODIS aerosol retrieval algorithms over the Beijing‐Tianjin‐Hebei region during low to very high pollution events

AbstractThis study evaluates the performance of different MODerate resolution Imaging Spectroradiometer (MODIS) aerosol algorithms during fine particle pollution events over the Beijing‐Tianjin‐Hebei region using Aerosol Robotic Network aerosol optical depth (AOD). These algorithms include the Deep Blue (DB) Collection 5.1 (C5) and Collection 6 (C6) algorithms at 10 km resolution, the Dark Target (DT) C5 and C6 algorithms at 10 km, the DT C6 algorithm at 3 km, and the Simplified Aerosol Retrieval Algorithm (SARA) at 500 m, 3 km, and 10 km resolutions. The DB C6 retrievals have 34–39% less uncertainties, 2–3 times smaller root‐mean‐square error (RMSE), and 3–4 times smaller mean absolute error (MAE) than DB C5 retrievals. The DT C6 has 4–8% lower bias, 4–12% less overestimation, and smaller RMSE and MAE errors than DT C5. Due to underestimation of surface reflectance and the use of inappropriate aerosol schemes, 87–89% of the collocations of the DT C6 at 3 km fall above the expected error (EE), with overestimation of 64–79% which is 15–27% higher than that for the DT C6 at 10 km. The results suggest that the DT C6 at 3 km resolution is less reliable than that at 10 km. The SARA AOD has small RMSE and MAE errors with 90–96% of the collocations falling within the EE. Overall, the SARA showed 15–16% less uncertainty than the DB C6 (10 km), 69–72% less than the DT C6 (10 km), and 79–83% less than the DT C6 (3 km) retrievals.

Effect of particle shape on dust shortwave direct radiative forcing calculations based on MODIS observations for a case study

Assuming spheroidal and spherical particle shapes for mineral dust aerosols, the effect of particle shape on dust aerosol optical depth retrievals, and subsequently on instantaneous shortwave direct radiative forcing (SWDRF) at the top of the atmosphere (TOA), is assessed based on Moderate Resolution Imaging Spectroradiometer (MODIS) data for a case study. Specifically, a simplified aerosol retrieval algorithm based on the principle of the Deep Blue aerosol retrieval method is employed to retrieve dust aerosol optical depths, and the Fu-Liou radiative transfer model is used to derive the instantaneous SWDRF of dust at the TOA for cloud-free conditions. Without considering the effect of particle shape on dust aerosol optical depth retrievals, the effect of particle shape on the scattering properties of dust aerosols (e.g., extinction efficiency, single scattering albedo and asymmetry factor) is negligible, which can lead to a relative difference of at most 5% for the SWDRF at the TOA. However, the effect of particle shape on the SWDRF cannot be neglected provided that the effect of particle shape on dust aerosol optical depth retrievals is also taken into account for SWDRF calculations. The corresponding results in an instantaneous case study show that the relative differences of the SWDRF at the TOA between spheroids and spheres depend critically on the scattering angles at which dust aerosol optical depths are retrieved, and can be up to 40% for low dust-loading conditions.

Validation and accuracy assessment of a Simplified Aerosol Retrieval Algorithm (SARA) over Beijing under low and high aerosol loadings and dust storms

An effective Simplified Aerosol Retrieval Algorithm (SARA) based on real viewing geometry and encompassing a wide range of aerosol conditions and types (ωo=0.30–1.0) was tested over Beijing, a city under the influence of both anthropogenic emissions and dust storms. The SARA AOD was retrieved at seven resolutions (500m, 1km, 2km, 4km, 6km, 8km, and 10km) using MODIS level 1 and 2 data products (MOD02, MOD03, and MOD09) for the years 2012 and 2013. For comparison purposes, the MODIS dark-target (DT) AOD observations at 10km resolution were obtained for the same time period. Both algorithms, SARA and MODIS DT were validated using Beijing_RADI and Beijing_CAMS AERONET AOD measurements and accuracy was evaluated using the Confidence Envelope of Expected Error (CEEE). The SARA AOD achieved very high correlation (0.972–0.994) with low Root Mean Square Error (RMSE~0.067–0.133) and very high accuracy (87–95%). In comparison, the MODIS DT algorithm overestimated AOD for both low and high AOD observations with large RMSE (0.115–0.342) and very low accuracy (20–52%). The robustness and accuracy of SARA and the MODIS DT algorithms for low (AOD<0.40) and high (AOD>0.40) aerosol loadings was evaluated using the Fraction of Expected Error (FEE). The SARA algorithm achieved 46–60% higher average accuracy than the MODIS DT algorithm indicating SARA's greater accuracy and reliability in AOD retrieval over Beijing under low and high aerosol loadings, including severe dust storms. The SARA algorithm is unique among satellite based AOD retrievals, in its ability to depict extreme dust storms (AOD~5.0) at fine spatial resolution (500m) over urban and suburban areas such as Beijing, XiangHe and the Bohai Sea, as is demonstrated by the dust storm of 17th April 2006.

A Simplified high resolution MODIS Aerosol Retrieval Algorithm (SARA) for use over mixed surfaces

Aerosol Optical Depth (AOD) is a measure of the columnar atmospheric aerosol content. Satellite remote sensing has been used to retrieve AOD over land and ocean at spatial resolutions of several to several tens of km. In most cases, a Radiative Transfer Model (RTM) is used to construct a look-up table (LUT) to act as a map between measurements and physical quantities. In the current study, MODerate resolution Imaging Spectroradiometer (MODIS) measurements were used to develop a Simplified Aerosol Retrieval Algorithm (SARA) for use over Hong Kong at high (500m) spatial resolution, without using a LUT. Instead, RTM calculations were applied directly to the MODIS data, with the aerosol properties derived from a local urban Aerosol Robotic Network (AERONET) station at the Hong Kong Polytechnic University, and surface reflectance from the MOD09GA level-2 daily surface reflectance product. For validation, the SARA AOD at 500m, along with the operational MODIS aerosol product (MOD04 C005) at 10km spatial resolution, was compared with data from a ground-based Microtops II sun photometer at the Hong Kong International Airport, a Sky-radiometer at the City University of Hong Kong, and an AERONET station at Hong Kong Hok Tsui. The 500m AOD retrieved from the SARA showed a high consistency with ground-based AOD measurements, with average correlation coefficient ~0.964, root mean square error (RMSE) ~0.077, and mean absolute error (MAE) ~0.065. The SARA AOD showed a good agreement with MOD04 C005 AOD over both urban and rural areas of Hong Kong, with average correlation coefficient ~0.917, RMSE ~0.087, and MAE ~0.072. The results demonstrate that our simplified AOD algorithm is better able to represent aerosol conditions over Hong Kong than the MOD04 C005 standard product as well as other higher resolution algorithms which have been tested over Hong Kong.