Aerosol Optical Depth Retrievals Research Articles

Aerosol Optical Depth over the Arctic Snow-Covered Regions Derived from Dual-Viewing Satellite Observations

Aerosol properties over the Arctic snow-covered regions are sparsely provided by temporal and spatially limited in situ measurements or active Lidar observations. This introduces large uncertainties for the understanding of aerosol effects on Arctic climate change. In this paper, aerosol optical depth (AOD) is derived using the advanced along-track scanning radiometer (AATSR) instrument. The basic idea is to utilize the dual-viewing observation capability of AATSR to reduce the impacts of AOD uncertainties introduced by the absolute wavelength-dependent error on surface reflectance estimation. AOD is derived assuming that the satellite observed surface reflectance ratio can be well characterized by a snow bidirectional reflectance distribution function (BRDF) model with a certain correction direct from satellite top of the atmosphere (TOA) observation. The aerosol types include an Arctic haze aerosol obtained from campaign measurement and Arctic background aerosol (maritime aerosol) types. The proper aerosol type is selected during the iteration step based on the minimization residual. The algorithm has been used over Spitsbergen for the spring period (April–May) and the AOD spatial distribution indicates that the retrieval AOD can capture the Arctic haze event. The comparison with AERONET observations shows promising results, with a correlation coefficient R = 0.70. The time series analysis shows no systematical biases between AATSR retrieved AOD and AERONET observed ones.

Retrieval of High Spatial Resolution Aerosol Optical Depth from HJ-1 A/B CCD Data

A high-spatial resolution aerosol optical depth (AOD) dataset is critically important for regional meteorology and climate studies. Chinese Huanjing-1 (HJ-1) A/B charge-coupled diode (CCD) data are a suitable data source for retrieving AODs. However, AOD cannot be retrieved based on the dark target method due to the absence of a shortwave infrared band. In this study, an AOD estimation method based on the relationships between visible bands of HJ-1 A/B CCDs is proposed. The Polarization and Directionality of the Earth's Reflectances (POLDER) Bidirectional Reflectance Distribution Function (BRDF) dataset was used to construct a lookup table for interband regression coefficients that varied by solar/view angle and land cover type. Finally, high-spatial resolution AODs could be retrieved with the aerosol lookup table and constraints. The results showed that the AODs retrieved from the HJ-1 A/B CCD data had the same range of distribution and trends as a visual interpretation of the images and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products did. The validation results using four sites of the Aerosol Robotic Network (AERONET) in Beijing showed that the value of the correlation coefficient R was 0.866, the root mean square error (RMSE) was 0.167, the mean absolute error (MAE) was 0.131, and the expected error (EE) was 53.9%. If the measurements of an AERONET site were used as prior knowledge, AOD retrieval results could be much more accurately obtained by this method (R is 0.989, RMSE is 0.052, MAE is 0.042, and EE is 96.7%).

VIIRS Deep Blue Aerosol Products Over Land: Extending the EOS Long‐Term Aerosol Data Records

AbstractA primary goal of the Deep Blue (DB) project is to create consistent long‐term aerosol data records, suitable for climate studies, using multiple satellite instruments. In order to continue Earth Observing System (EOS)‐era aerosol products into the Joint Polar Satellite System era, we have successfully ported the DB algorithm to process data from the Visible Infrared Imaging Radiometer Suite (VIIRS). Although the basic structure of the VIIRS algorithm is similar to that for the Moderate Resolution Imaging Spectroradiometer (MODIS), many enhancements have been made compared to the MODIS collection 6 (C6) version. Most have also been implemented in the latest MODIS Collection 6.1 (C6.1). For example, a new smoke mask was developed based on the spectral curvature of measured reflectance to distinguish biomass burning smoke from weakly absorbing urban/industrial aerosols. Consequently, a new aerosol‐type flag was added into the VIIRS DB data set. In addition, new dust models have been developed to account for the nonsphericity of mineral dust. As a result, a discontinuity in the retrieved aerosol optical depth (AOD) of Saharan dust plumes seen in MODIS C6 products near the boundary between North Africa and the Atlantic has been much reduced. We have also evaluated the VIIRS and MODIS Terra/Aqua C6.1 AOD against Aerosol Robotic Network data. VIIRS and MODIS retrievals show similar performance; around 80% of matchups agree with Aerosol Robotic Network within the expected error of ±(0.05 + 20)%, indicating that DB can provide consistent AOD through the historical EOS and present Joint Polar Satellite System eras.

Improved Aerosol Retrievals Over Complex Regions Using NPP Visible Infrared Imaging Radiometer Suite Observations

AbstractAs a new generation of polar‐orbiting satellites, NPP VIIRS (National Polar‐orbiting Partnership Visible Infrared Imaging Radiometer Suite) provides important supports for the studies of local and global scale climate change, environmental monitoring, and radiant energy balance. This paper proposed an improved deep blue aerosol retrieval algorithm over complex regions based on NPP VIIRS observations. Two improvements were proposed and implemented to the traditional aerosol optical depth (AOD) retrieval algorithm in order to improve the retrieval accuracy, including the construction of the surface reflectance conversion model and aerosol optical parameter acquisition. The retrieval results were extensively evaluated. First, the accuracy of the algorithm was evaluated using AErosol RObotic NETwork ground‐based observations, including conditions over different underlying surfaces, different seasons, and different AOD values. Then, the cross validation was carried out between our AOD retrievals and NPP VIIRS aerosol products. The verification results show that our AOD retrievals agree very well with the AErosol RObotic NETwork AOD with coefficient of determination (R2) ~ 0.85. The improved deep blue algorithm performs well overall under different surface conditions and seasons, while the quality of performance gradually decreases with the increase of AOD. Moreover, the improved algorithm is robust with less bias, root‐mean‐square error, and relative mean bias in the AOD retrievals and can provide more detailed aerosol information compared with the NPP VIIRS aerosol products. These results suggest that the improved deep blue algorithm can obtain high‐precision AOD information, providing better data basis for related researches on air pollution and climate change.

Aerosol Retrieval in the Autumn and Winter From the Red and $2.12~\mu\text{m}$ Bands of MODIS

In the autumn and winter, aerosol is the important atmospheric pollutant over the Beijing–Tianjin–Hebei region. For monitoring aerosol in the autumn and winter, the lack of vegetation and the aging of MODIS sensor are two problems that needed to be solved. In this paper, after analyzing the characteristics of aerosol radiance in the red and shortwave infrared ( $2.12~\mu \text{m}$ ) bands of MODIS, we develop a new algorithm for terrestrial aerosol with the assumption that the reflectance ratio between the red and $2.12~\mu \text{m}$ bands is invariant. With MODIS data over the Beijing–Tianjin–Hebei region from September 2016 to February 2017, the algorithm is applied to aerosol retrieval. The retrieved aerosol optical depth images show that our algorithm can retrieve aerosol over sparse vegetation, and the validation with the AERONET/PHOTONS Beijing site shows that the correlation is greater than 0.9% and 77% of the retrievals fall within the expected error. An error analysis shows that a 2% error in the proportion of the soot component can lead to 15% retrieval error, and over more than 60% of the surface area, the error from the changes in the ratio between the red and $2.12~\mu \text{m}$ bands can lead to retrieved errors less than 0.1.

Climate-specific and global validation of MODIS Aqua and Terra aerosol optical depth at 452 AERONET stations

Aerosol optical depth (AOD) is a highly influential variable in solar resource assessment and clear-sky radiation modelling. Hence, the accuracy of solar energy estimates ultimately depends on the accuracy of the measured or assumed AOD. Gridded satellite information is often used for solar modelling due to its geographical coverage, and so a global validation of commonly utilised AOD products is imperative. Here, all Level-3 Moderate Resolution Imaging Spectroradiometer (MODIS) daily observations of AOD (at 470, 550 and 660 nm, noted AOD470, AOD550 and AOD660, respectively) from the Aqua and Terra satellites (of 1° × 1° spatial resolution) from 2000 to 02/2018 are compared and validated against all of NASA’s ground sensing Aerosol Robotic NETwork (AERONET) V3 Level 2 AOD daily averages from sites that reported at least one year of observations during 2000–2018 (452 sites representing at least 653,000 observations per variable). Furthermore, sub-categorisation by Köppen-Geiger climate regions enables a novel climate-specific validation to ascertain any distinct climatic influence. The results demonstrate significant climatological influences that impact the derived AOD product at all three wavelengths. It is found that blending the two Aqua and Terra products results in a higher accuracy of daily estimates of all AOD products. Each AOD product is validated similarly and separately. AOD550, which is most commonly used in solar resource assessment, is found worst in the equatorial climate (absolute root mean square error (RMSE) of 0.194) and best in the temperate climate (RMSE of 0.126). Globally, the combined Aqua + Terra AOD550 experiences an absolute RMSE of 0.106 and a mean absolute error of 0.109. The most common MODIS AOD retrievals are between 0.01 and 0.25, suggesting that the MODIS daily AOD products may introduce a significant source of uncertainty in modelled irradiance estimates, and that other sources of input data should be used instead whenever their applications demand high accuracy.

Performance of MODIS Collection 6.1 Level 3 aerosol products in spatial-temporal variations over land

This study evaluates the long-term Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) Level 3 atmospheric aerosol products over land. For this purpose, three monthly aerosol optical depth (AOD) datasets, including Dark Target (DT), Deep Blue (DB) and combined DT and DB (DTB) during 2003–2017, are collected. Aerosol Robotic Network (AERONET) Version 2 Level 2.0 (cloud-screened and quality-assured) monthly measurements at 431 sites around the world are selected for comparison. This study attempts to provide a better understanding of the different MODIS products for their applicability at multiple spatial scales and their suitability for representing the long-term trend of aerosol characteristics. Experiments are performed with direct comparisons between MODIS retrievals and AERONET measurements at global, local to site scales. Meanwhile, the spatial and temporal variations are also compared and discussed. Our results illustrate that C6.1 MODIS AOD retrievals are well correlated with AERONET AOD measurements globally, while the DTB product performs best at most regions, yet the DB product is superior at the site scale. In general, Terra AOD products always overestimate while Aqua AOD products are more accurate in describing the annual mean AOD loadings over land. For long-term aerosol trends, there are small differences between Terra and Aqua aerosol products. Among the three aerosol datasets, neither one can consistently outperform the others in both spatial and temporal aerosol variations over land. In general, DTB products can more accurately capture the correct aerosol changes and are strongly recommended for selection in related aerosol studies at the global scale.

Intercomparison and Validation of Satellite and Ground-Based Aerosol Optical Depth (AOD) Retrievals over Six AERONET Sites in West Africa

The geographical and climatic conditions of West Africa make the region an essential area for the description of atmospheric aerosol optical properties across the globe. This study provides an intercomparison of aerosol optical depth (AOD440nm) retrievals from satellite-based CALIPSO, MISR, MODIS and OMI sensors against ground-based sun photometer (AERONET) measurements between 2004 and 2014 from six West African sites, which are Agoufou, Banizoumbou, Ouagadougou, Dakar, Djougou and Ilorin during the period 2004–2014. The analysis revealed that MODISSTD performed better with a high degree of correlation for the six study sites, the CALIPSO and MISR–AERONET comparisons showed strong correlations, and the MODISDB also indicated better correlations, as did the OMI–AERONET comparisons. The root mean square error, mean absolute error and root mean bias error were also computed. The CALIPSO instrument has the lowest RMSE and MAE values over Dakar, while the highest RMSE and MAE values were indicated by the CALIPSO and MODISDB sensors, respectively, over Ilorin. The MISR instrument showed good agreement over Dakar than the other instruments, while CALIPSO AOD retrievals were better than those from the other sensors in Banizoumbou and Ouagadogou. The expected error bounds computed for both MODIS retrievals showed that MODISSTD consistently outperformed MODISDB in all the study sites. High AOD values were averagely observed by the satellite sensors during the local dry months (December–February), due to high concentrations of dust aerosols. High AOD values were observed during March–May, due to the condensation of water vapor on aerosol leading to increase in size and optical depth.

Joint Retrieval of Aerosol Optical Depth and Surface Reflectance Over Land Using Geostationary Satellite Data

The advanced Himawari imager (AHI) aboard the Himawari-8 geostationary satellite provides high-frequency observations with broad coverage, multiple spectral channels, and high spatial resolution. In this paper, AHI data were used to develop an algorithm for joint retrieval of aerosol optical depth (AOD) over land and land surface bidirectional reflectance. Instead of performing surface reflectance estimation before calculating AOD, the AOD and surface bidirectional reflectance were retrieved simultaneously using an optimal estimation method. The algorithm uses an atmospheric radiative transfer model coupled with a surface bidirectional reflectance factor (BRF) model. Based on the assumption that the surface bidirectional reflective properties are invariant during a short time period (i.e., a day), multiple temporal AHI observations were combined to calculate the AOD and surface BRF. The algorithm was tested over East Asia for year 2016, and the AOD retrieval results were validated against the aerosol robotic network (AERONET) sites observation and compared with the Moderate Resolution Imaging Spectroradiometer Collection 6.0 AOD product. The validation of the retrieved AOD with AERONET measurements using 14 713 colocation points in 2016 over East Asia shows a high correlation coefficient: $R = 0.88$ , root-mean-square error = 0.17, and approximately 69.9% AOD retrieval results within the expected error of $\pm 0.2\cdot {\mathrm {AOD}}_{\mathrm {AERONET}}\pm 0.05$ . A brief comparison between our retrieval and AOD product provided by Japan Meteorological Agency is also presented. The comparison and validation demonstrates that the algorithm has the ability to estimate AOD with considerable accuracy over land.

Toward Improving Short‐Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals

AbstractThis study develops a new approach to improve simulations of the particulate matter of aerodynamic diameter smaller than 2.5 μm (PM2.5) in the Community Multiscale Air Quality (CMAQ) model via assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals using the Gridpoint Statistical Interpolation (GSI) system. In contrast to previous studies that only consider errors due to transport, our computation of the background error covariance matrix incorporates uncertainties in anthropogenic emissions. To understand the impact of this approach, three experiments (one background and two assimilations) are performed over the contiguous United States (CONUS) from 15 July to 14 August 2014. The background CMAQ experiment significantly underestimates both the MODIS AOD and surface PM2.5 levels. MODIS AOD assimilation pushes both the CMAQ AOD and surface PM2.5 distributions toward the observed distributions, but CMAQ still underestimates the observations. Averaged over CONUS, the two assimilation experiments with and without including the anthropogenic emission uncertainties improve the correlation coefficient between the model and independent observations of PM2.5 by ~67% and ~48%, respectively, and reduces the mean bias by ~38% and ~10%, respectively. The assimilation improves the model performance everywhere over CONUS, except the New York and Wisconsin, where CMAQ overestimates the observed PM2.5 during nighttime after assimilation likely because of overcorrection of aerosol mass concentrations by the AOD assimilation. Future work should incorporate uncertainties in other processes (biomass burning and biogenic emissions, deposition, chemistry, transport, and boundary conditions) to further enhance the value of assimilating spaceborne AOD retrievals.

Evaluation of Terra-MODIS C6 and C6.1 Aerosol Products against Beijing, XiangHe, and Xinglong AERONET Sites in China during 2004-2014

In this study, Terra-MODIS (Moderate Resolution Imaging Spectroradiometer) Collections 6 and 6.1 (C6 & C6.1) aerosol optical depth (AOD) retrievals with the recommended high-quality flag (QF = 3) were retrieved from Dark-Target (DT), Deep-Blue (DB) and merged DT and DB (DTB) level–2 AOD products for verification against Aerosol Robotic Network (AERONET) Version 3 Level 2.0 AOD data obtained from 2004–2014 for three sites located in the Beijing-Tianjin-Hebei (BTH) region. These are: Beijing, located over mixed bright urban surfaces, XiangHe located over suburban surfaces, and Xinglong located over hilly and vegetated surfaces. The AOD retrievals were also validated over different land-cover types defined by static monthly NDVI (Normalized Difference Vegetation Index) values obtained from the Terra-MODIS level-3 product (MOD13A3). These include non-vegetated surfaces (NVS, NDVI < 0.2), partially vegetated surfaces (PVS, 0.2 ≤ NDVI ≤ 0.3), moderately vegetated surfaces (MVS, 0.3 < NDVI < 0.5) and densely vegetated surfaces (DVS, NDVI ≥ 0.5). Results show that the DT, DB, and DTB-collocated retrievals achieve a high correlation coefficient of ~ 0.90–0.97, 0.89–0.95, and 0.86–0.95, respectively, with AERONET AOD. The DT C6 and C6.1 collocated retrievals were comparable at XiangHe and Xinglong, whereas at Beijing, the percentage of collocated retrievals within the expected error (↔EE) increased from 21.4% to 35.5%, the root mean square error (RMSE) decreased from 0.37 to 0.24, and the relative percent mean error (RPME) decreased from 49% to 27%. These results suggest significant relative improvement in the DT C6.1 product. The percentage of DB-collocated AOD retrievals ↔EE was greater than 70% at Beijing and Xinglong, whereas less than 66% was observed at XiangHe. Similar to DT AOD, DTB AOD retrievals performed well at XiangHe and Xinglong compared with Beijing. Regionally, DB C6 and C6.1-collocated retrievals performed better than DT and DTB in terms of good quality retrievals and relatively small errors. For diverse vegetated surfaces, DT-collocated retrievals reported small errors and good quality retrievals only for NVS and DVS, whereas larger errors were reported for PVS. MVS. DB contains good quality AOD retrievals over PVS, MVS, and DVS compared with NVS. DTB C6.1 collocated retrievals were better than C6 over NVS, PVS, and DVS. C6.1 is substantially improved overall, compared with C6 at local and regional scales, and over diverse vegetated surfaces.

Comparison and evaluation of MODIS Multi-angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia

The Multiangle Implementation of Atmospheric Correction (MAIAC) is a new generic algorithm applied to collection 6 (C6) MODIS measurements to retrieve Aerosol Optical Depth (AOD) over land at high spatial resolution (1 km). This study is the first evaluation of the MAIAC AOD from MODIS Aqua (A) and Terra (T) satellites between 2006 and 2016 over South Asia. The retrieval accuracy of MAIAC was assessed by comparing it to ground-truth AErosol RObotic NETwork (AERONET) AOD, as well as to AOD retrieved by the two operational MODIS algorithms: Dark Target (DT) and Deep Blue (DB). MAIAC AOD showed higher spatial coverage and retrieval frequency than either the DT or the DB AOD retrievals. The high spatial resolution of the MAIAC retrievals enhances the capability to distinguish aerosol sources and to determine fine aerosol features, such as wildfire smoke plumes and haze over complex geographical regions, and provides more retrievals in conditions that are cloudy or when the surface is partially covered by snow. In comparison to AERONET AOD, MAIAC AOD shows a better accuracy than both DT and DB AOD. A higher number of MAIAC-AERONET AOD matchups demonstrate the capability of MAIAC to retrieve AOD over varied surfaces, different aerosol types and loadings. Our results demonstrate high retrieval accuracy in term of the Expected Error (EE) (A/T, EE: 72.22%, 73.50%), and low root mean square error (A/T, RMSE: 0.148, 0.164), root mean bias (RMB) (A/T, RMB: 0.978, 1.049) and mean absolute error (MAE) (A/T, MAE: 0.098, 0.096). Moreover, MAIAC has a lower bias as a function of the viewing geometry and the aerosol type among the three retrieval algorithms. MAIAC performed well over bright and vegetated land surfaces, showing the highest retrieval accuracy over dense vegetation and particularly well in retrieving smoke AOD, yet it underestimated dust AOD. In conclusion, MAIAC's ability to provide AOD at high spatial resolution appears promising over South Asia, thus having advantage over contemporary aerosol retrieval algorithms for epidemiological and climatological studies. CapsuleIn comparison with MODIS DT and DB AOD, and AERONET AOD, MAIAC shows improved accuracy and lower bias over South Asia, as well as with greater spatial coverage.

Updated MISR over-water research aerosol retrieval algorithm – Part 2: A multi-angle aerosol retrieval algorithm for shallow, turbid, oligotrophic, and eutrophic waters

Abstract. Coastal waters serve as transport pathways to the ocean for all agricultural and other runoff from terrestrial sources, and many are the sites for upwelling of nutrient-rich, deep water; they are also some of the most biologically productive on Earth. Estimating the impact coastal waters have on the global carbon budget requires relating satellite-based remote-sensing retrievals of biological productivity (e.g., chlorophyll a concentration) to in situ measurements taken in near-surface waters. The Multi-angle Imaging SpectroRadiometer (MISR) can uniquely constrain the “atmospheric correction” needed to derive ocean color from remote-sensing imagers. Here, we retrieve aerosol amount and type from MISR over all types of water. The primary limitation is an upper bound on aerosol optical depth (AOD), as the algorithm must be able to distinguish the surface. This updated MISR research aerosol retrieval algorithm (RA) also assumes that light reflection by the underlying ocean surface is Lambertian. The RA computes the ocean surface reflectance (Rrs) analytically for a given AOD, aerosol optical model, and wind speed. We provide retrieval examples over shallow, turbid, and eutrophic waters and introduce a productivity and turbidity index (PTI), calculated from retrieved spectral Rrs, that distinguished water types (similar to the the normalized difference vegetation index, NDVI, over land). We also validate the new algorithm by comparing spectral AOD and Ångström exponent (ANG) results with 2419 collocated AErosol RObotic NETwork (AERONET) observations. For AERONET 558 nm interpolated AOD < 1.0, the root-mean-square error (RMSE) is 0.04 and linear correlation coefficient is 0.95. For the 502 cloud-free MISR and AERONET collocations with an AERONET AOD > 0.20, the ANG RMSE is 0.25 and r is 0.89. Although MISR RA AOD retrieval quality does not appear to be substantially impacted by the presence of turbid water, the MISR-RA-retrieved Ångström exponent seems to suffer from increased uncertainty under such conditions. MISR supplements current ocean color sources in regions where sunglint precludes retrievals from single-view-angle instruments. MISR atmospheric correction should also be more robust than that derived from single-view instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). This is especially true in regions of shallow, turbid, and eutrophic waters, locations where biological productivity can be high, and single-view-angle retrieval algorithms struggle to separate atmospheric from oceanic features.

Comparative analysis of MODIS, MISR, and AERONET climatology over the Middle East and North Africa

Abstract. Comparative analysis of Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), and Aerosol Robotic Network (AERONET) aerosol optical depth (AOD) products is performed over seven AERONET stations located in the Middle East and North Africa for the period of 2000–2015. Sites are categorized into dust, biomass burning, and mixed aerosol conditions. MISR and MODIS AOD agree during high-dust seasons but MODIS tends to underestimate AOD during low-dust seasons. Over dust-dominated sites, MODIS/Terra AODs indicate a negative trend over time, while MODIS/Aqua, MISR, and AERONET depict a positive trend. A deviation between MODIS/Aqua and MODIS/Terra was observed regardless of the geographic location and data sampling. The performance of MODIS is similar over the entire region with ∼64 % of AOD within the Δτ=±0.05±0.15τAERO confidence range. MISR AOD retrievals fall within 84 % of the same confidence range for all sites examined here. Both MISR and MODIS capture aerosol climatology; however few cases were observed where one of the two sensors better captures the climatology over a certain location or AOD range than the other sensor. AERONET Level 2.0 version 3, MODIS Collection 6.1, and MISR V23 data have been used in analyzing the results presented in this study.

Extreme gradient boosting model to estimate PM2.5 concentrations with missing-filled satellite data in China

Several studies have attempted to predict ground PM2.5 concentrations using satellite aerosol optical depth (AOD) retrieval. However, over 70%–90% of aerosol retrievals are non-random missing, which limits and biases the estimation. To the best of our knowledge, this issue has not been well resolved to date.The aim of this study was to develop an interpolation technique to handle the missing data retrieval problem and to estimate the daily PM2.5 for a high coverage dataset with 3-km resolution in China by fitting the complex temporal and spatial variations.We developed a two-step interpolation method (i.e., the mixed-effect model and inverse distance weighting technology) to replace the missing values in AOD. Next, the extreme gradient boosting (XGBoost) technique that includes a non-linear exposure-lag-response model (NELRM) was proposed and validated to estimate the daily levels of PM2.5 across China during 2014–2015.After two steps of interpolation, the missing value rate of daily AOD data was reduced from 87.91% to 13.83%. The cross-validation (CV) R-square, root mean square error (RMSE) and mean absolute percentage prediction error (MAPE) of the interpolation were 0.76, 0.10 and 21.41%, respectively. The cross-validation for the prediction of daily PM2.5 resulted in R2 = 0.86, RMSE = 14.98, and MAPE = 23.72%.The results of this study indicate that the two-step interpolation method can largely resolve the non-random missing data problem and that the combined XGBoost methods have a good ability to estimate fine particulate matter concentrations.

Evaluation of MAIAC aerosol retrievals over China

Multiangle Implementation of Atmospheric Correction (MAIAC) is a new aerosol algorithm developed to retrieve aerosol optical depth (AOD) over land using the time series data to dynamically isolate aerosol and land contributions. However, there are still no comprehensive research on the quality of MAIAC AOD over China. In this paper, 1 km MAIAC AOD over China were examined against ground-based measurements to evaluate the performance of the data. In general, Aqua and Terra MAIAC retrievals have a high correlation coefficient (0.924 for Aqua and 0.933 for Terra) with ground-based observations and there are more than 72% of retrievals falling within the Expected Errors (EE = ±(0.05 + 0.2*AOD)). We found that the accuracy of MAIAC AOD is related with the AOD magnitude, aerosol size, seasons and surface types. In spring and summer, the AOD bias was influenced by both aerosol model and surface reflectance estimation. We also found that the aerosol model is the main source of AOD bias over desert regions. These results indicated that the MAIAC AOD could be used as a new aerosol data source for air quality and climate studies in China.

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.

Evaluation of Landsat-8 and Sentinel-2A aerosol optical depth retrievals across Chinese cities and implications for medium spatial resolution urban aerosol monitoring.

In urban environments, aerosol distributions may change rapidly due to building and transport infrastructure and human population density variations. The recent availability of medium resolution Landsat-8 and Sentinel-2 satellite data provide the opportunity for aerosol optical depth (AOD) estimation at higher spatial resolution than provided by other satellites. A year of 30 m Landsat-8 and 10 m Sentinel-2A AOD data retrieved using the Land Surface Reflectance Code (LaSRC) were compared with coincident ground-based Aerosol Robotic Network (AERONET) Version 3 AOD data for 20 Chinese cities. Stringent selection criteria were used to select contemporaneous data - only satellite and AERONET data acquired within 10 minutes were considered. The average satellite retrieved AOD over a 1470 m × 1470 m window centered on each AERONET site was derived to capture fine scale urban AOD variations. AERONET Level 1.5 (cloud-screened) and Level 2.0 (cloud-screened and also quality assured) data were considered. For the 20 urban AERONET sites in 2016 there were 106 (Level 1.5) and 67 (Level 2.0) Landsat-8 AERONET AOD contemporaneous data pairs, and 118 (Level 1.5) and 89 (Level 2.0) Sentinel-2A AOD data pairs. The greatest AOD values (>1.5) occurred in Beijing, suggesting that the Chinese capital was one of the most polluted cities in China in 2016. The LaSRC Landsat-8 and Sentinel-2A AOD retrievals agreed well with the AERONET AOD data (linear regression slopes > 0.96; coefficient of determination r2 > 0.90; root mean square deviation < 0.175) and demonstrate that the LaSRC is an effective and applicable medium resolution AOD retrieval algorithm over urban environments. The Sentinel-2A AOD retrievals had better accuracy than the Landsat-8 AOD retrievals, which is consistent with previously published research. The implications of the research and the potential for urban aerosol monitoring by combining the freely available Landsat-8 and Sentinel-2 satellite data are discussed.

Variability in atmospheric aerosols and effects of humidity, wind and InterTropical discontinuity over different ecological zones in Nigeria

The effects of humidity, wind, and InterTropical Discontinuity (ITD) movement on seasonal distribution of atmospheric aerosols were assessed over different ecological zones in Nigeria. Satellite-observed aerosol optical depth (AOD), humidity, wind speed, and precipitation/ITD data were used. The Moderate Resolution Imaging Spectroradiometer (MODIS) AOD time series of dataset were used. The MODIS AOD retrievals were at the wavelength of 550 nm and AOD data was re-scaled and sub-divided into seasonal and annual distributions to investigate the variations in Aerosol. Kriging spatial interpolation model was used to analyze the aerosol seasonal distribution. Correlations analysis was used to evaluate the degree of influence of humidity, wind speed, and precipitation/ITD on the seasonal distribution of aerosol over six ecological zones; Sahel, Sudan, Guinea, Rainforest, Freshwater, and Mangrove zones in Nigeria. The results show that the AOD over Nigeria is influenced by meteorological factors, but much more influenced by dust source outside the country. AOD appears much more during Harmattan season than Wet and Dry seasons; with AOD ≥0.46, with considerable concentration in Sahel ecological zone than other ecological zones. The results show a strong significant relationship between aerosol distribution and meteorological, principally humidity with R2 ≥ 0.65 @ p < 0.05. The study reveals that for daily variation of AOD, wind speed, humidity and precipitation as well as spatial distribution of dust source in and outside the country are important drivers.

Characterizing the 2015 Indonesia fire event using modified MODIS aerosol retrievals

Abstract. The Indonesian fire and smoke event of 2015 was an extreme episode that affected public health and caused severe economic and environmental damage. The MODIS Dark Target (DT) aerosol algorithm, developed for global applications, significantly underestimated regional aerosol optical depth (AOD) during this episode. The larger-than-global-averaged uncertainties in the DT product over this event were due to both an overly zealous set of masks that mistook heavy smoke plumes for clouds and/or inland water, and also an aerosol model developed for generic global aerosol conditions. Using Aerosol Robotic Network (AERONET) Version 3 sky inversions of local AERONET stations, we created a specific aerosol model for the extreme event. Thus, using this new less-absorbing aerosol model, cloud masking based on results of the MODIS cloud optical properties algorithm, and relaxed thresholds on both inland water tests and upper limits of the AOD retrieval, we created a research algorithm and applied it to 80 appropriate MODIS granules during the event. Collocating and comparing with AERONET AOD shows that the research algorithm doubles the number of MODIS retrievals greater than 1.0, while also significantly improving agreement with AERONET. The final results show that the operational DT algorithm had missed approximately 0.22 of the regional mean AOD, but as much as AOD = 3.0 for individual 0.5∘ grid boxes. This amount of missing AOD can skew the perception of the severity of the event, affect estimates of regional aerosol forcing, and alter aerosol modeling and forecasting that assimilate MODIS aerosol data products. These results will influence the future development of the global DT aerosol algorithm.