Aerosol Optical Thickness Data Research Articles

Exploiting TERRA-AQUA MODIS Relationship in the Reflective Solar Bands for Aerosol Retrieval

Satellite remote sensing has been providing aerosol data with ever-increasing accuracy, representative of the MODerate-resolution Imaging Spectroradiometer (MODIS) Dark Target (DT) and Deep Blue (DB) aerosol retrievals. These retrievals are generally performed over spectrally dark objects and therefore may struggle over bright surfaces. This study proposed an analytical TERRA-AQUA MODIS relationship in the reflective solar bands for aerosol retrieval. For the relationship development, the bidirectional reflectance distribution function (BRDF) effects were adjusted using reflectance ratios in the MODIS 2.13 μm band and the path radiance was approximated as an analytical function of aerosol optical thickness (AOT) and scattering phase function. Comparisons with MODIS observation data, MODIS AOT data, and sun photometer measurements demonstrate the validity of the proposed relationship for aerosol retrieval. The synergetic TERRA-AQUA MODIS retrievals are highly correlated with the ground measured AOT at TERRA MODIS overpass time (R2 = 0.617; RMSE = 0.043) and AQUA overpass time (R2 = 0.737; RMSE = 0.036). Compared to our retrievals, both the MODIS DT and DB retrievals are subject to severe underestimation. Sensitivity analyses reveal that the proposed method may perform better over non-vegetated than vegetated surfaces, which can offer a complement to MODIS operational algorithms. In an analytical form, the proposed method also has advantages in computational efficiency, and therefore can be employed for fine-scale (relative to operational 10 km MODIS product) MODIS aerosol retrieval. Overall, this study provides insight into aerosol retrievals and other applications regarding TERRA-AQUA MODIS data.

Estimating and source analysis of surface PM2.5 concentration in the Beijing–Tianjin–Hebei region based on MODIS data and air trajectories

ABSTRACTParticulate matter (PM) with an aerodynamic diameter of <2.5 μm (PM2.5) has become the primary air pollutant in most major cities in China. Some studies have indicated that there is a positive correlation between the aerosol optical thickness (AOT) and surface-level PM2.5 concentration. In order to estimate PM2.5 concentration over large areas, a model relating the concentration of PM2.5 and AOT has been established. The scale height of aerosol and relative humidity as well as the effect of surface temperature and wind velocity were introduced to enhance the model. 2013 full year Moderate Resolution Imaging Spectroradiometer (MODIS) AOT data and ground measurements of the PM2.5 concentration in the Beijing–Tianjin–Hebei region were used to fit a seasonal multivariate linear equation relating PM2.5 concentration and AOT, and the accuracy of the model has been determined. When comparing MODIS-estimated PM2.5 with the measurements from ground monitoring stations during spring, summer, autumn and winter, we found the R2 values were 0.45, 0.45, 0.37, and 0.31, respectively. Based on this model, the spatial distribution of PM2.5 concentration during four typical haze events sampled by seasons was derived, and displayed with the backward air trajectories calculated using the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model. We undertook a preliminary analysis about the source of surface-level PMs and the process of its accumulation and dispersion during the haze episodes by analysing the effect of terrain and topography in the specific location of the Beijing–Tianjin–Hebei region. The spatial distribution of the PM2.5 concentration showed that the high value region was generally in the southeast of the study area, which approximately overlapped an area of lower vegetation coverage, and the temporal variation of PM2.5 concentration indicated that the air pollution was more severe during winter and spring than summer and autumn. The results of the analysis of backward air trajectories suggested that the hazy weather in the Beijing–Tianjin–Hebei region was mainly caused by unfavourable terrain and weather conditions.

Spaceborne observations of low surface aerosol concentrations in the Stockholm region

This article investigates the feasibility of using spaceborne observations of aerosol optical thickness (AOT) derived with the Moderate Resolution Imaging Spectroradiometer (MODIS) for monitoring of fine particulate matter (PM2.5) in an environment of low aerosol loading. Previous studies of the AOT-to-PM2.5 relationship benefit from the large range of observed values. The Stockholm region features a comprehensive network of ground-based monitoring stations that generally show PM2.5 values <20 µg m−3. MODIS AOT at 555 nm is usually <0.20 and in good agreement with ground-based sun photometer observations in this region. We use MODIS Collection 5 AOT data with a horizontal resolution of 10 km×10 km and ground-based in-situ PM2.5 observations to derive an AOT-to-PM2.5 relationship that can be used to estimate fields of PM2.5. This has been carried out with respect to the months from April to September of the period 2000–2013. Relative average absolute deviations of 33–55 % (mean of 45 %) are obtained between MODIS-retrieved and ground-based PM2.5. The root mean square error is 0.2159 µg m−3 between retrieved and measured PM2.5. From spaceborne lidar observations, it is found that elevated aerosol layers are generally sparse in the Stockholm region. This favours remote sensing of PM2.5 from space. The deviations found between measured and retrieved PM2.5 are mainly attributed to infrequent situations of inhomogeneous aerosol layering for which column-integrated observations cannot be connected to surface conditions. Using MODIS Collection 6 data with a resolution of 3 km×3 km in a case study actually gives far fewer results than the coarser Collection 5 product. This is explained by the complex geography of the Stockholm region with a coastline and an abundance of lakes, which seems to induce biases in the retrieval of AOT at higher spatial resolution.

Investigation of permanent aerosol source regions over Asia using multi-year MODIS observations

This work examines the permanent aerosol source regions over Asia by analyzing 7-years data of aerosol optical thickness (AOT) product from MODerate Imaging Spectroradiometer (MODIS) onboard Aqua and Terra satellites. The analysis is carried out by taking the average AOT map during the years 2002-2008 over the region in different seasons, in which the permanent source regions will appear pronounced whereas the locations influenced by transport or any emissions that last for shorter time period will be smoothened. The results show four such main permanent source regions. Angstrom Exponent (AE) aerosol product is used to infer about the possible type and size of aerosols over the source regions. The average AOT trends over the source regions in different seasons during 2002-2008 are examined and the results are discussed based on the corresponding variations of meteorological parameters derived from National Center for Environmental Prediction - National Center for Atmospheric Research (NCEP-NCAR) reanalysis data. In addition, the trends in AOT variation during the period 2002-2008 over certain selected stations over the area are also discussed.

Combining AOT, Angstrom Exponent and PM concentration data, with PSCF model, to distinguish fine and coarse aerosol intrusions in Southern France

In this paper, a cluster analysis of backward air mass trajectories, arriving in Avignon (Southern France), was combined with a Potential Source Contribution Function (PSCF) model on a 0.5°×0.5° resolution grid, in order to indicate possible aerosol intrusions. A strict triple criterion was constructed from Aerosol Optical Thickness (AOT), Angstrom Exponent (AE), and PM (PM10 and PM2.5) concentration measurements, aiming to distinguish more effectively Episodes of Fine, Coarse and Overall Aerosols (FAE, CAE and OAE respectively). Large fractions of FAE (60.0%) and CAE (40.6%) were strongly attributed to the prevalence of Eastern and South–Southwest (S–SW) airflows respectively, whereas these distinct trajectory clusters also gathered large fractions of OAE (90.2% cumulatively). According to PSCF results, FAE events were strongly associated with the influence of air masses traveling over North Italy and Southern Germany, hence the impact of urban and industrial combustion was emerged. Main sources of coarse aerosols were principally isolated over the Mediterranean, thus the import of sea spray and dust from the Sahara desert is presumed. Satellite AOT observations were used for a more detailed identification of an intense 5-day intrusion of coarse aerosols. Short range slow moving air mass trajectories, were proven to be a clear marker of atmospheric stagnation, based on a wind speed analysis, triggering the accumulation of locally emitted anthropogenic aerosols (mainly PM2.5) and lack of city ventilation.

Evaluation of multi-temporal and multi-sensor atmospheric correction strategies for land-cover accounting and monitoring in Ireland

Accurate atmospheric correction is an important preprocessing step for studies of multi-temporal land-cover mapping using optical satellite data. Model-based surface reflectance predictions (e.g. 6S – Second Simulation of Satellite Signal in the Solar Spectrum) are highly dependent on the adjustment of aerosol optical thickness (AOT) data. For regions with no or insufficient spatial and temporal coverage of meteorological ground measurements, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived AOT data are a valuable alternative, especially with regard to the dynamics of atmospheric conditions. In this study, atmospheric correction strategies were assessed based on the change in standard deviation (σ) compared to the raw data and also by machine learning land-cover classification accuracies. For three Landsat 8 OLI (acquired in 2013) and two RapidEye (acquired in 2010 and 2014) scenes, seven different correction strategies were tested over an agricultural area in southeast Ireland. Visibility calculated from daily spatial averaged TERRA-MODIS estimates (1° × 1° Aerosol Product) served as input for the atmospheric correction. In almost all cases the standard deviation of the raw data is reduced after incorporation of terrain correction, compared to the atmospheric-corrected data. ATCOR®-IDL-based correction decreases the standard deviation almost consistently (ranging from −0.3 to −26.7). The 6S implementation in GRASS GIS showed a tendency of increasing the variation in the data, especially for the RapidEye data. No major differences in overall accuracies (OAs) and kappa values were observed between the three machine learning classification approaches. The results indicate that the ATCOR®-IDL-based correction and MODIS parameterization methods are able to decrease the standard deviation and are therefore an appropriate approach to approximate the top-of-canopy reflectance.

A potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data

Accuracy of aerosol scattering reflectance in atmospheric correction is crucial for satellite ocean color remote sensing. A new approach is developed to estimate aerosol scattering reflectance from satellite data based on a look-up table of in situ water-leaving reflectance. A decision rule is set to determine the water-leaving component of the total satellite-measured radiance from the table under an assumption that aerosol scattering reflectance follows the Angstrom law. The epsilon spectrum, obtained from the ratio of aerosol scattering reflectance, is used to select the two closest aerosol models for determining the corrected epsilon values, which is adjusted to new values according to the reference wavelength. The actual aerosol scattering reflectance is obtained from the mean value of the reflectance and the new epsilon spectrum. The performance of the model is evaluated using the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) imagery, showing that the component of aerosol scattering reflectance can be completely separated from the total satellite-measured radiance, and effects of non-zero water-leaving reflectance can be eliminated over highly turbid waters. The accuracy is validated by in situ measured aerosol optical thickness (AOT) using a handheld multi-band sun photometer and the Aerosol Robotic Network (AERONET) measured AOT data during the 2006 winter cruise and 2007 autumn cruise over the East China Sea (ECS). The analysis gives the mean relative errors of 23.73% and 32.04%, respectively. Our analysis shows that aerosol scattering reflectance can be significantly overestimated over turbid waters when using the atmospheric correction method based on the black ocean assumption (BOA). It may also have some errors over oceanic waters because a small 1% bias of satellite-measured reflectance in the near-infrared (NIR) bands will lead to a relatively large error of ~10% in aerosol scattering reflectance in Band 1. This error will spread to water-leaving reflectance, possibly leading to negative values of water-leaving reflectance. This amplifying effect of aerosol scattering reflectance can be limited by matching aerosol models using epsilon spectrum in our model. Because the assumption of aerosol scattering reflectance following the Angstrom law can eliminate effects of different types of waters including open ocean, coastal regions, lakes and rivers, our model offers a potentially universal algorithm for estimating aerosol scattering reflectance from satellite remote sensing data.

MODIS 에어러솔 광학두께와 지상에서 관측된 시정거리를 이용한 대기 에어러솔 연직분포 산출

본 연구에서는 MODIS 인공위성으로 분석된 에어러솔 광학두께 자료와 지상에서 관측된 시정거리 자료를 이용하여 에어러솔 연직분포 모델링을 수행하였다. 위성과 지상관측자료로부터 에어러솔의 척도 고도를 구할 수 있었으며, 그 결과는 복사전달 모델에서 사용되고 있는 표준대기 모델과 비교에서 만족할 만한 수준의 근사치를 보였다. 그리고 실제 사례로서 대기가 청명한 경우(<TEX>${\tau}_{MODIS}=0.12{\pm}0.07$</TEX>, 시정거리=<TEX>$21.13{\pm}3.31km$</TEX>)와 혼탁한 경우(<TEX>${\tau}_{MODIS}=1.71{\pm}0.85$</TEX>, 시정거리 =<TEX>$13.33{\pm}5.66km$</TEX>)에 대해서 적용하여 척도 고도를 산정한 결과는 각각 전국 평균값으로서 <TEX>$0.63{\pm}0.33km$</TEX>와 <TEX>$1.71{\pm}0.84km$</TEX>로 나타났다. 그리고 이 결과를 바탕으로 대기 에어러솔 소산계수의 연직분포를 구할 수 있었으며, 최종적으로 KML 형식으로 코딩되어 관심 영역의 대기 환경 특성 변화를 감시하는데 도움이 될 것으로 기대된다. This study presents a modeling of aerosol extinction vertical profiles in Korea by using the Moderate Resolution Imaging Spectro-radiometer(MODIS) derived aerosol optical thickness(AOT) and ground based visibility observation data. The method uses a series of physical equations for the derivation of aerosol scale height and vertical profiles from MODIS AOT and surface visibility data. The modelled results under the standard atmospheric condition showed small differences with the standard aerosol vertical profile used in the radiative transfer model. Model derived aerosol scale heights for two cases of clean(<TEX>${\tau}_{MODIS}=0.12{\pm}0.07$</TEX>, visibility=<TEX>$21.13{\pm}3.31km$</TEX>) and hazy atmosphere(<TEX>${\tau}_{MODIS}=1.71{\pm}0.85$</TEX>, visibility=<TEX>$13.33{\pm}5.66km$</TEX>) are <TEX>$0.63{\pm}0.33km$</TEX> and <TEX>$1.71{\pm}0.84km$</TEX>. Based on these results, aerosol extinction profiles can be estimated and the results are transformed into the KML code for visualization of dataset. This has implications for atmospheric environmental monitoring and environmental policies for the future.

Air quality evaluation on an urban scale based on MODIS satellite images

Earth-observing satellites have provided satellite image datasets for urban-scale air quality monitoring. Aerosol optical thickness (AOT) at 1km resolution is retrieved from MODIS on Terra Satellite by using the improved algorithm. The 1km AOT data are validated by using AOT measurements from two AERONET stations and the 10km AOT products from MOD04_L2 in the period of October 2004. Then the validated 1km AOT data are compared with ground-based PM2.5 mass concentration in Beijing. Four empirical models which are used to investigate the relationship between AOT data and PM2.5 mass concentration are obtained by regression analysis and their correlations are R2=0.818, R2=0.750, R2=0.699 and R2=0.629 respectively. In order to verify the models, we use these models to retrieve PM2.5 concentration from MODIS AOT on the 11th of October 2012 and then compare it with the PM2.5 concentration from the ground measurements in that day. 50%, 46.4%, 46.4% and 39.3% of the stations are within the expected errors respectively by the four models. Preliminary analysis of these four models indicates that the quadratic model has significant potential to enhance air quality monitoring on an urban scale. Although the limited daily MODIS AOT data, variability of terrain, weather conditions, and many other factors can limit the ability of predicting PM2.5 concentration, the models provide a cost-effective approach for obtaining the distribution of PM2.5 information from satellite image, which complement the defects of the limited ground-based monitoring station measurements.

The effect of soil moisture and wind speed on aerosol optical thickness retrieval in a desert environment using SEVIRI thermal channels

Dust emission and deposition are associated with several factors such as surface roughness, land cover, soil properties, soil moisture (SM), and wind speed (WS). A combination of land surface and remote-sensing models has recently been investigated for dust detection and monitoring. The thermal bands of the Meteosat Second Generation Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI) satellite are widely used for qualitative detection of dust over desert because of their high spectral and temporal resolutions. In this work, the contribution of ground-measured WS data and satellite-measured SM data on aerosol optical thickness (AOT) retrieval was investigated using an artificial neural network (ANN) model. ANNs have been applied in similar applications and have shown a higher performance than simple multiple-regression models. This performance is mainly due to the ANN's ability to capture complex and non-linear relationships between inputs and outputs. A combination of MSG/SEVIRI brightness temperature (BT)/brightness temperature differences (BTDs), BTD3.9–10.8, BTD8.7–10.8, BTD10.8–12, and BT3.9, was used as input to the base ANN model while Aerosol Robotic Network (AERONET) AOT (level 2) data at 0.5 μm were used as output. These input/output sets were obtained from two stations (Hamim and Mezaira) lying in the inland desert of the United Arab Emirates (UAE). About 3800 observations were collected, of which two-thirds were used to train the ANN model and the remaining third was kept as an independent set to assess the accuracy of the trained model. Later, Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E) SM data and ground-measured WS data were used as additional inputs to the base model to investigate their contribution to the AOT retrieval. SM data consist of daytime AMSR-E-derived daily and collected from a National Snow and Ice Data Centre (NSIDC)-archived database. Hourly average WS data were also collected at 10 m height in the same AERONET sites from two stations managed by the UAE National Centre of Meteorology and Seismology. All ground and satellite measurements were extracted for the closest time to AERONET measurements. The use of these additional inputs has been shown to have a positive impact on the accuracy of simulated AOT. The addition of these inputs to the base ANN increased R 2 from 0.68 to 0.76 and reduced root mean square error from 0.113 to 0.09.

A global survey of the effect of cloud contamination on the aerosol optical thickness and its long‐term trend derived from operational AVHRR satellite observations

Subpixel cloud contamination is one of the major issues plaguing passive satellite aerosol remote sensing. Its impact on the aerosol optical thickness (AOT) retrieval has been analyzed/evaluated by many studies. However, the question of how it influences the AOT trend remains to be answered. In this paper, four long‐term advanced very high resolution radiometer (AVHRR) AOT data sets from 1981 to 2009 over global oceans for four different definitions of clear sky, respectively, are produced by applying a two‐channel aerosol retrieval algorithm to the AVHRR clear‐sky reflectances derived by combining NOAA Pathfinder Atmosphere's Extended AVHRR climate data record level‐2b all‐sky reflectances with the cloud probability parameter determined from the Bayesian probabilistic cloud detection technique. A global analysis of the effect of cloud contamination on the AVHRR AOT retrieval as well as on its long‐term trend is then performed by comparing the results from the four data sets. It was found that cloud contamination imposes not only a positive bias on AOT values but also a positive bias on its long‐term trend such that negative trends become less negative and positive trends become more positive. A cloud probability value of ≤1% has been identified as an optimal criterion for clear‐sky definition to minimize the cloud contamination in the AVHRR aerosol retrieval while still retaining strong aerosol signals. In order for a satellite aerosol product to be useful and reliable in aerosol trend detection, the cloud contamination effect on aerosol trends needs to be studied/evaluated carefully along with the effects of calibration error, surface disturbance, and aerosol model assumptions.

Analysis of Spatial and Temporal Variability of PM10 Concentrations Using MODIS Aerosol Optical Thickness in the Pearl River Delta Region, China

Characterizing spatial and temporal variations of PM pollution is critical for a thorough understanding of its formation, transport and accumulation in the atmosphere. In this study, Aerosol Optical Thickness (AOT) data retrieved from a Moderate Resolution Imaging Spectroradiometer (MODIS) were used to investigate the spatial and temporal variations of PM10 (particles with aerodynamic diameters of less than 10 μm) pollution in the Pearl River Delta (PRD) region. Seasonal linear regression models between 1-km retrieved MODIS AOT data and ground PM10 measurements were developed for the PRD region with meteorological corrections, and were subjected to a validation against observations from the regional air monitoring network in this region from 2006 to 2008, with an overall error of less than 50%. Consistent with ground observations, the estimated PM10 concentrations from the regression models appeared to be highest in winter, lower in autumn and spring, and lowest in summer. A high PM10 concentration band was detected over the inner part of the PRD region, where heavy industries and dense populations are located. The shape and concentration levels of this band exhibit significant seasonal variations, which shift with synoptic wind direction, indicating different source regions and their contributions to the PM10 pollution in the PRD region. Several discrete “hot spots” were found in the southwest of the PRD region during spring and other seasons, where no ground measurements are available. The reasons for the formation of these hot spots are unclear, and further investigations are needed. Despite the limitations of this work, the results demonstrate the effectiveness of retrieving remote sensing data for characterizing regional aerosol pollution, together with ground measurements. The combination of satellite data and ground monitoring presented in this work can help in better understanding the sources, formation mechanisms and transport process of particulate matters on a regional scale.

Spatiotemporal Spectral Variations of AOT in India’s EEZ over Arabian Sea: Validation of OCM-II

We report the results of sun-photometric measurements of Aerosol Optical Thickness (AOT) in India’s Exclusive Economic Zone (EEZ) over the Arabian Sea along with synchronous Ocean Color Monitor (OCM-II) derived AOT estimates during December 12, 2009–January 10, 2010. Relatively higher values of Angstrom exponent (α) around 1.2 near coast and 0.2–0.8 in the India’s EEZ, observed during the cruise period, indicate the presence of smaller particles near the coast due to anthropogenic activities; and larger particles in the India’s EEZ due to advection of pollutants from Indian subcontinent via long-range transport. Results related to α and its derivative reveal four different aerosol types (urban-industrial, desert-dust, clean-marine, and mixed-type) with varying fraction during the study period. Surface radiative forcing due to aerosols is found to be 20 W/m2 over India’s EEZ. OCM-derived AOTs showed good corroboration with in situ measurements with a correlation coefficient of about 0.95. A reasonably good correlation was also observed between AOT and wind speed (R = 0.6); AOT and relative humidity (R = 0.58). The concurrent MODIS AOT data also agree well with those observed by the OCEANSAT (OCM-II) satellite during the campaign period.

Influences of the window size of moderate resolution imaging spectroradiometer (MODIS) aerosol optical thickness (AOT) values on particulate matter (PM10) motoring in Klang Valley, Malaysia

Remote sensing has been shown to be a useful tool in retrieval aerosol optical thickness (AOT) in particulate matter (PM) pollution monitoring. In this study, daily AOT data retrieved from moderate resolution IMAGING spectroradiometer(MODIS) and the amount of PM10 measured at eight air quality monitoring ground stations in Klang Valley from 2004 to 2006 were used. The AOT values consisted of three different average window sizes (WS): (3 × 3) pixels, (5 ´ 5) pixels and (7 × 7) pixels. PM10 mass concentration consists of real-time (hourly) and 24 h mean. Linear correlation coefficients (LCC) were used to determine the best validation result. Best validation results were obtained between AOT WS in (5 × 5) pixels and PM10 mass in 24 h mean. The LCCs in dry season were acquired higher than those in rainy season. The validation results in rainy season were improved when nonlinear correlation coefficients (NLCC) with polynomial equation were used for analysis. This study reveals that the correlation coefficients are dependent on time and location. Key words: Particulate matter, aerosol optical thickness, real-time, 24 h mean, Klang Valley-Malaysia.

Predominant transport paths of Saharan dust over the Mediterranean Sea to Europe

We use monthly data of aerosol optical thickness (AOT) from the Moderate Resolution Imaging Spectroradiometer on board the seven NASA Terra and Aqua satellites for a 10 year period (2001–2010) in order to determine seasonal variations of Saharan dust transport over the Mediterranean toward Europe. The maxima of AOT are used to visualize the transport paths. Saharan dust reaches Europe over the Mediterranean and also by looping back over the Atlantic. In spring, aerosols are observed within a wide range of longitudes in Europe, with the highest AOT over western Europe. This may be partially explained by dust transport to western Europe via the Atlantic route, while to central and eastern Europe, dust is transported over the Mediterranean. During all seasons, dust is transported over the Mediterranean to Europe. In the summer months, aerosols are observed predominantly in central Europe. In autumn, aerosol activity is strongest in eastern Europe. We show that there are local AOT maxima over north Italy, in the Alps, in Spain, southeast of the Pyrenees and Sierra Nevada, and in the Rila Mountains in Bulgaria. We suggest that these maxima of aerosol concentration appear as the dust‐carrying airflow reaches the mountains and slows.

Comparison of contrast reduction based MODIS AOT estimates with AERONET measurements

Current satellite aerosol retrieval products could be complemented by contrast reduction methods to overcome limitations regarding highly reflective or heterogeneous surfaces such as urban, desert or snow covered areas. Algorithms based on the contrast reduction principle, define contrast loss in an image, inside a pre-determined window size, as an exponential function of the Aerosol Optical Thickness (AOT) difference between two images (a reference and a polluted) acquired under similar observation geometry conditions. This paper presents a contrast reduction algorithm designed for the MODIS sensor, based on the Differential Texture Analysis (DTA) approach. It focuses on algorithm optimization by: a) determining an optimal AOT spatial resolution; b) constraining the relative observation geometry differences between polluted and reference images; and c) assessing the influence of several land cover classes on the accuracy of the retrievals. A comparison of the results obtained for 192 images acquired for the year 2005 with data from five European AERONET stations is performed to assess overall algorithm accuracy as well as the impact of the proposed improvements. Comparative analysis of the results for the various sites showed an optimal algorithm performance for MODIS images using a 39pixel distance window, composed of only forest and urban pixels. Comparison with AERONET AOT data showed a good agreement with a correlation coefficient of 0.78. A similar correlation is found when comparing AERONET measurements and MODIS aerosol standard product. This research supports the establishment of contrast reduction methods as a potential complement to other aerosol retrieval methodologies. Future work will aim at removing the residual aerosol influence from reference images, including BRDFs to better reproduce surface heterogeneity and observation geometry influences and expanding the scope of this study to other AERONET sites so as to further test the algorithm at a global scale.

Monitoring 2.5 μm particulate matter within urbanized regions using satellite-derived aerosol optical thickness, a study in Hong Kong

This study investigates the relationship between aerosol optical thickness (AOT) derived from MODerate resolution Imaging Spectroradiometer (MODIS) satellite and in situ particulate matter (PM2.5) from Hong Kong air-quality monitoring stations. The relationship was analysed for three different AOT products, namely, MODIS collection 5 AOT data, MODIS collection 5 fine-mode fraction AOT data, both at 10 km resolution, and MODIS AOT data at 500 m resolution. In view of the predicted low accuracies obtainable for MODIS AOT products for the south China region, these AOT products were first validated against AOT measurements from an AErosol RObotic NETwork (AERONET) station near the centre of Hong Kong. Strong relationships of R 2 = 0.78 and R 2 = 0.77 for the 10 km and 500 m AOT data, respectively, were obtained, thus providing a robust AOT image database at both coarse and fine spatial resolution for comparison with PM2.5 concentrations. When a whole year (2007) of AOT images was compared with PM2.5 concentrations recorded at five ground stations, correlations of R 2 = 0.31, R 2 = 0.10 and R 2 = 0.67 were obtained for collection 5, fine-mode fraction of collection 5 (both at 10 km resolution) and 500 m AOT, respectively. Strong correlations between MODIS 500 m AOT and PM2.5 concentration were also observed for individual stations (R 2 = 0.66, 0.74, 0.76, 0.56 and 0.62, for Central, Tung Chung, Tseun Wan, Yuen Long and Tap Mun stations, respectively). The study suggests that fine particle distributions at a high level of detail over whole cities may be obtained from satellite images. Since the model has potential for further refinement, monitoring of detailed PM2.5 concentrations on a routine basis from satellite images will provide a highly useful tool for urban environmental authorities.

A study on the potential applications of satellite data in air quality monitoring and forecasting

In this study we explore the potential applications of MODIS (Moderate Resolution Imaging Spectroradiometer) -like satellite sensors in air quality research for some Asian regions. The MODIS aerosol optical thickness (AOT), NCEP global reanalysis meteorological data, and daily surface PM10 concentrations over China and Thailand from 2001 to 2009 were analyzed using simple and multiple regression models. The AOT–PM10 correlation demonstrates substantial seasonal and regional difference, likely reflecting variations in aerosol composition and atmospheric conditions. Meteorological factors, particularly relative humidity, were found to influence the AOT–PM10 relationship. Their inclusion in regression models leads to more accurate assessment of PM10 from spaceborne observations. We further introduced a simple method for employing the satellite data to empirically forecast surface particulate pollution. In general, AOT from the previous day (day 0) is used as a predicator variable, along with the forecasted meteorology for the following day (day 1), to predict the PM10 level for day 1. The contribution of regional transport is represented by backward trajectories combined with AOT. This method was evaluated through PM10 hindcasts for 2008–2009, using observations from 2005 to 2007 as a training data set to obtain model coefficients. For five big Chinese cities, over 50% of the hindcasts have percentage error ≤ 30%. Similar performance was achieved for cities in northern Thailand. The MODIS AOT data are responsible for at least part of the demonstrated forecasting skill. This method can be easily adapted for other regions, but is probably most useful for those having sparse ground monitoring networks or no access to sophisticated deterministic models. We also highlight several existing issues, including some inherent to a regression-based approach as exemplified by a case study for Beijing. Further studies will be necessary before satellite data can see more extensive applications in the operational air quality monitoring and forecasting.

Accuracy assessment of moderate resolution image spectroradiometer products for dust storms in semiarid environment

Dust storms are strongly and negatively associated with the annual cycle of rainfall and coincide with the west and southwesterly winds in west and south west of Iran. Accuracy assessment of particulate matter products of moderate resolution image spectroradiometer was studied in this research. Moderate resolution image spectroradiometer products consist of aerosol optical thickness, its corresponding image red, green and blue and moderate resolution image spectroradiometer/ terra calibrated radiances 5 minutes L1B swath 1 km, which shows the environmental information at terrestrial, atmospheric and ocean phenomenology. Daily aerosol optical thickness data retrieved from moderate resolution image spectroradiometer from May 2009 to May 2010 were compared with the amount of particulate matter measured at ground in Sanandaj, Iran, using non-linear correlation coefficient. Results showed that the moderate resolution image spectroradiometer image / terra calibrated radiances 5 minutes L1B swath 1 km is able to detect dust storms distribution and their blowing direction over study area clearly. The air quality conditions obtained in with dust storm period were unhealthy and correlation coefficients between moderate resolution image spectroradiometer aerosol optical thickness and particulate matter concentration in this period were higher than without dust storm period. The moderate resolution image spectroradiometer aerosol optical thickness values lower than 0.1 were acquired uncertainty level. Comparison of moderate resolution image spectroradiometer images/ terra calibrated radiances 5 minutes L1B swath 1 km and image red, green and blue showed that moderate resolution image spectroradiometer has limitation in retrieval of aerosol optical thickness from the dust storm with high concentration of particulate matter. This study reveals that the algorithm which is applied to refine the aerosol optical thickness is not able to recognize the amount of particulate matter in low and very high concentrations sensitively. No study has previously been conducted to investigate the accuracy of the moderate resolution image spectroradiometer particulate matter products.

A geostatistical data fusion technique for merging remote sensing and ground‐based observations of aerosol optical thickness

The Multiangle Imaging Spectroradiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the NASA Earth Observation System's Terra satellite have been measuring aerosol optical thickness (AOT) since early 2000. These remote‐sensing platforms complement the ground‐based Aerosol Robotic Network (AERONET) in better understanding the role of aerosols in climate and atmospheric chemistry. To date, however, there have been only limited attempts to exploit the complementary multiangle (MISR) and multispectral (MODIS) capabilities of these sensors along with the ground‐based observations in an integrated analysis. This paper describes a geostatistical data fusion technique that can take advantage of the spatial autocorrelation of the AOT distribution, while making optimal use of all available data sets. Using Level 2.0 AERONET, MISR, and MODIS AOT data for the contiguous United States, we demonstrate that this approach can successfully incorporate information from multiple sensors and provide accurate estimates of AOT with rigorous uncertainty bounds. Cross‐validation results show that the resulting AOT product is closer to the ground‐based AOT observations than either of the individual satellite measurements.