Aerosol Optical Depth Products Research Articles

Evaluation and comparison of multiangle implementation of the atmospheric correction algorithm, Dark Target, and Deep Blue aerosol products over China

Abstract. A new multiangle implementation of the atmospheric correction (MAIAC) algorithm has been applied in the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and has recently provided globally high-spatial-resolution aerosol optical depth (AOD) products at 1 km. Moreover, several improvements have been modified in the classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS Collection 6.1 products. Thus, validation and comparison of the MAIAC, DT, and DB algorithms are urgent in China. In this paper, we present a comprehensive assessment and comparison of AOD products at a 550 nm wavelength based on three aerosol retrieval algorithms in the MODIS sensor using ground-truth measurements from AErosol RObotic NETwork (AERONET) sites over China from 2000 to 2017. In general, MAIAC products achieved better accuracy than DT and DB products in the overall validation and accuracy improvement of DB products after the QA filter, demonstrating the highest values among the three products. In addition, the DT algorithms had higher aerosol retrievals in cropland, forest, and ocean land types than the other two products, and the MAIAC algorithms were more accurate in grassland, built-up, unoccupied, and mixed land types among the three products. In the geometry dependency analysis, the solar zenith angle, scattering angle, and relative azimuth angle, excluding the view zenith angle, significantly affected the performance of the three aerosol retrieval algorithms. The three products showed different accuracies with varying regions and seasons. Similar spatial patterns were found for the three products, but the MAIAC retrievals were smaller in the North China Plain and higher in Yunnan Province compared with the DT and DB retrievals before the QA filter. After the QA filter, the DB retrievals were significantly lower than the MAIAC retrievals in south China. Moreover, the spatiotemporal completeness of the MAIAC product was also better than the DT and DB products.

New Regression Method to Merge Different MODIS Aerosol Products Based on NDVI Datasets

The moderate resolution and imaging spectroradiometer (MODIS) level 2 operational aerosol products that are based on the dark target (DT) method over vegetated regions and the enhanced deep blue (DB) algorithms over bright pixels provide daily global aerosol optical depth (AOD). However, increasing the data coverage by merging the DT and DB merged AOD product has recently become the focus of research. Therefore, this study aims to improve the merged AOD performance by introducing a new regression method (DTBRG), depending on the normalized difference vegetation index values when DT and DB AOD are valid. The DTBRG AOD is validated on a global scale while using aerosol robot network AOD measurements. Merged AOD550s from the MODIS official method and Bilal’s customized methods are evaluated for the same period for comparison. The inter-comparison of merged AOD550s from different methods with an equal number of coincident observations demonstrates that the DTBRG method performs better than the MODIS official algorithm with increased expected error (83% versus 76%), R (0.92 versus 0.90), and decreased bias (−0.001 versus 0.012). Therefore, it can be operationally used for global merged aerosol retrievals.

Development and validation of improved PM2.5 models for public health applications using remotely sensed aerosol and meteorological data.

In this study, Moderate Resolution Imaging Spectrometer (MODIS) satellite measurements of aerosol optical depth (AOD) from different retrieval algorithms have been correlated with ground measurements of fine particulate matter less than 2.5μm (PM2.5). Several MODIS AOD products from different satellites (Aqua vs. Terra), retrieval algorithms (Dark Target vs. Deep Blue), collections (5.1 vs. 6), and spatial resolutions (10km vs. 3km) for cities in the Western, Midwestern, and Southeastern USA have been evaluated. We developed and validated PM2.5 prediction models using remotely sensed AOD data. These models were further improved by incorporating meteorological variables (temperature, relative humidity, precipitation, wind gust, and wind direction) from the North American Land Data Assimilation System Phase 2 (NLDAS-2). Adding these meteorological data significantly improved the simulation quality of all the PM2.5 models, especially in the Western USA. Temperature, relative humidity, and wind gust were significant meteorological variables throughout the year in the Western USA. Wind speed was the most significant meteorological variable for the cold season while for the warm season, temperature was the most prominent one in the Midwestern and Southeastern USA. Using this satellite-derived PM2.5 data can improve the spatial coverage, especially in areas where PM2.5 ground monitors are lacking, and studying the connections between PM2.5 and public health concerns including respiratory and cardiovascular diseases in the USA can be further advanced.

Investigating Smoke Aerosol Emission Coefficients Using MODIS Active Fire and Aerosol Products: A Case Study in the CONUS and Indonesia

AbstractSmoke aerosols released from biomass burning greatly influence air quality, weather, and climate. The total particulate matter (TPM) of smoke aerosols has been demonstrated to be a linear function of fire radiative energy (FRE) during a period of biomass burning via a smoke aerosol emission coefficient (Ce). However, it remains challenging to quantify Ce appropriately through satellite observations. In this study, an innovative approach was put forward to explore Ce by establishing the relationships between FRE and TPM in two regions, the CONtiguous United States and Indonesia. Specifically, we identified 584 isolated fire clusters and smoke plumes in the CONtiguous United States and 248 in Indonesia using Moderate Resolution Imaging Spectroradiometer natural color images, and then calculated FRE from Moderate Resolution Imaging Spectroradiometer active fire product and TPM from Moderate Resolution Imaging Spectroradiometer aerosol optical depth product for each fire‐smoke matchup during Terra and Aqua overpasses. The relationships between TPM and FRE were constructed to determine Ce using an ordinary least squares regression. The results show that FRE and TPM are significantly correlated (r2 ≥ 0.63, p < 0.001) with the Ce varying across regions and fuel types. In the CONtiguous United States, forest Ce values are 21.3 and 34.1 g/MJ and savanna Ce values are 18.2 and 22.8 g/MJ for western and eastern regions, respectively; additionally, Ce is 20.9 g/MJ for grasslands and 5.0 g/MJ for shrublands. In Indonesia, Ce is 52.4 and 30.0 g/MJ for peatlands and forests, respectively. Overall, this study improves our understanding of Ce variations with fuel types and climate regions.

Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products

Abstract. Satellite-derived aerosol products provide long-term and large-scale observations for analysing aerosol distributions and variations, climate-scale aerosol simulations, and aerosol–climate interactions. Therefore, a better understanding of the consistencies and differences among multiple aerosol products is important. The objective of this study is to compare 11 global monthly aerosol optical depth (AOD) products, which are the European Space Agency Climate Change Initiative (ESA-CCI) Advanced Along-Track Scanning Radiometer (AATSR), Advanced Very High Resolution Radiometer (AVHRR), Multi-angle Imaging SpectroRadiometer (MISR), Moderate Resolution Imaging Spectroradiometer (MODIS), Sea-viewing Wide Field-of-view Sensor (SeaWiFS), Visible Infrared Imaging Radiometer (VIIRS), and POLarization and Directionality of the Earth's Reflectance (POLDER) products. AErosol RObotic NEtwork (AERONET) Version 3 Level 2.0 monthly measurements at 308 sites around the world are selected for comparison. Our results illustrate that the spatial distributions and temporal variations of most aerosol products are highly consistent globally but exhibit certain differences on regional and site scales. In general, the AATSR Dual View (ADV) and SeaWiFS products show the lowest spatial coverage with numerous missing values, while the MODIS products can cover most areas (average of 87 %) of the world. The best performance is observed in September–October–November (SON) and the worst is in June–July–August (JJA). All the products perform unsatisfactorily over northern Africa and Middle East, southern and eastern Asia, and their coastal areas due to the influence from surface brightness and human activities. In general, the MODIS products show the best agreement with the AERONET-based AOD values on different spatial scales among all the products. Furthermore, all aerosol products can capture the correct aerosol trends at most cases, especially in areas where aerosols change significantly. The MODIS products perform best in capturing the global temporal variations in aerosols. These results provide a reference for users to select appropriate aerosol products for their particular studies.

Assessment of Himawari-8 AHI Aerosol Optical Depth Over Land

This study conducted the first comprehensive assessment of the aerosol optical depth (AOD) product retrieved from the observations by the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite. The AHI Level 3 AOD (Version 3.0) was evaluated using the collocated Aerosol Robotic Network (AERONET) level 2.0 direct sun AOD measurements over the last three years (May 2016–December 2018) at 58 selected AERONET sites. A comprehensive comparison between AHI and AERONET AOD was carried out, which yielded a correlation coefficient (R) of 0.82, a slope of 0.69, and a root mean square error (RMSE) of 0.16. The results indicate a good agreement between AHI and AERONET AOD, while revealing that the AHI aerosol retrieval algorithm tends to underestimate the atmospheric aerosol load. In addition, the expected uncertainty of AHI Level 3 AOD (Version 3.0) is ± (0.1 + 0.3 × AOD). Furthermore, the performance of the AHI aerosol retrieval algorithm exhibits regional variation. The best performance is reported over East Asia (R 0.86), followed by Southeast Asia (R 0.79) and Australia (R 0.35). The monthly and seasonal comparisons between AHI and AERONET show that the best performance is found in summer (R 0.93), followed by autumn (R 0.84), winter (R 0.82), and spring (R 0.76). The worst performance was observed in March (R 0.75), while the best performance appeared in June (R 0.94). The variation in the annual mean AHI AOD on the scale of hours demonstrates that AHI can perform continuous (no less than ten hours) aerosol monitoring.

Ground-level PM2.5 estimation over urban agglomerations in China with high spatiotemporal resolution based on Himawari-8

High concentrations of particulate matter with diameter of <2.5 μm (PM2.5) demonstrate severe effects on human health, especially in the metropolitan agglomerations of China. Estimating PM2.5 based on satellite aerosol optical depth (AOD) is a widely used method. AOD data from Himawari-8, a geostationary satellite, enable improvement of the temporal resolution of PM2.5 estimates to the hourly level, thereby reflecting diurnal variations of pollutants compared with AOD products from polar orbit satellites, which only have one value per day. In this study, PM2.5 concentrations are estimated based on Himawari-8 AOD and other ancillary data by constructing spatiotemporal linear mixed effects model in Central China (CCH), Beijing–Tianjin–Henan (BTH), Yangtze River Delta (YRD) and Pearl River Delta (PRD) regions, respectively. The determination coefficient (R2) between the measurements and estimates of PM2.5 calculated with the tenfold cross-validation method are 0.82, 0.84, 0.80 and 0.74 in CCH, BTH, YRD and PRD, respectively. The spatial distributions of PM2.5 present large regional variation, which is highly correlated with land-use type. Heavily polluted zones are mainly located in urban or rural areas, which have dense population and high anthropogenic emissions. Comparisons among different seasons show that particle pollution during the cold seasons (autumn and winter) is relatively severe with an average PM2.5 of >60 μg/m3 in CCH, BTH and YRD, whereas the level does not greatly change throughout the year in the PRD region. During the daytime, particulate pollution levels are generally high in the morning.

Estimating ground-level PM2.5 over a coastal region of China using satellite AOD and a combined model

Studies of adverse human health effects due to exposures to particles with an aerodynamic diameter smaller than 2.5 μm (PM2.5) are often limited by sparse ground in situ measurements. Satellite remote sensing technique provides an effective tool for PM2.5 assessment in areas where surface PM2.5 monitoring network is not available. The MODIS aerosol optical depth (AOD) with spatial resolution of 10 km has been widely used in retrieving PM2.5 concentrations in a large scale, but it is insufficient for city-scale PM2.5 studies. In this study, we used the newly released AOD product with higher resolution of 3 km incorporating meteorological fields from Goddard Earth Observing System-Forward Processing (GEOS-FP) and road density for ground-level PM2.5 estimation in Fuzhou, i.e. a coastal city of China. A two-stage statistical model combing linear mixed effects model (LME) and support vector regression model (SVR) was proposed in this study, and a 10-fold cross validation approach was employed for model validation. We obtained an overall R2 of 0.81, root mean square error (RMSE) of 8.83 μg/m3 in model fitting, and R2 of 0.77, RMSE of 9.51 μg/m3 in model validation. The retrieved PM2.5 presented a spatial pattern with high concentrations in urban areas and low values in suburban or mountainous areas. We also found that the spatial distribution of PM2.5 concentrations showed a very strong correlation with the terrain features, forest cover, road density, and industrial pollution sources. The results revealed that the combined LME-SVR model using 3 km AOD along with GEOS-FP field and road density could achieve high accuracy in PM2.5 estimation, and would be helpful for air quality monitoring in Fuzhou.

MODIS AOD sampling rate and its effect on PM2.5 estimation in North China

Much attention has been paid to develop methods to estimate particulate matter with an aerodynamic diameter of 2.5 μm or less (PM2.5) from satellite aerosol optical depth (AOD). One of fundamental limitation of these methods is lack of AOD and thereby PM2.5 cannot be derived from satellites when clouds are present or when surface conditions are not favorable. This would probably result in an inherent clear-sky biased estimate of PM2.5 for air quality assessment that requires continuous 24-h measurements at all-sky conditions. Using the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD and PM2.5 data in North China, a highly polluted area with large spatiotemporal variabilities of AOD and PM2.5 values, missing MODIS AOD retrievals and its potential effect on PM2.5 estimation are studied. The MODIS dark target (DT) algorithm produces very few AODs in winter, with a regional observation rate of 4%, which limits its statistical significance for PM2.5 air quality monitoring. This limitation applies to MODIS DT AOD products at 10-km and 3-km resolutions since they are derived from the same retrieval core (Remer et al., 2013). In contrast, The MODIS deep blue (DB) AOD product complements the MODIS DT AOD coverage, which is remarkable in winter. The MODIS DT and DB merged product has comparable accuracy to that of the DT and DB products but shows a larger sampling rate, therefore, it is more suitable for estimating surface PM2.5. While the regional mean PM2.5 values in the presence and absence of AOD retrievals in spring and summer are comparable, but the former is substantially lower than the latter in autumn by 11.2 μgm−3 and winter by 8.5 μgm−3 on average. The difference in some stations even exceeds 20 μg m−3. Methods to fill missing AOD values in North China are crucial to provide an unbiased sampling and estimate of PM2.5 concentration in all-sky conditions, likely by integrating satellite, surface and modeling data.

A comparison study between AOD data from MODIS deep blue collections 51 and 06 and from AERONET over Saudi Arabia

This study compares the precision of the satellite-based Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue (DB) Collection–51 (C–51) and Collection–06 (C–06) Aerosol Optical Depth (AOD, at 550 nm) products with surface-based aerosol robotic network (AERONET) observations for the period 2002–2013 at the Solar Village, Saudi Arabia. In general, MODIS captures the patterns of AERONET AOD although C−51 tends to underestimate them while C−06 overestimates them. We found a slightly higher correlation for C–06 (0.79) than for C–51 (0.74) over the Solar Village. The C–06 retrievals are typically of better quality than those of C−51 with a smaller root mean square error (RMSE) and mean absolute error (MAE) and more AODs fall within the expected error range and relative mean bias. Overall, both the C–51 and C–06 MODIS DB algorithms show significant uncertainties and errors. The errors in AOD measurements arise due to imperfect aerosol model schemes and underestimation of surface reflectance over the Solar Village. This study suggests that further quantitative research is required to provide better estimates of satellite-based AOD over Saudi Arabia.

A spatially structured adaptive two-stage model for retrieving ground-level PM2.5 concentrations from VIIRS AOD in China

While the aerosol optical depth (AOD) product from the Visible Infrared Imaging Suite (VIIRS) instrument has proven effective for estimating regional ground-level particle concentrations with aerodynamic diameters less than 2.5 μm (PM2.5), its performance at larger spatial scales remains unclear. Despite the wide application of statistical models in building ground-level PM2.5 satellite remote sensing retrieval models, a limited number of studies have considered the spatiotemporal heterogeneities for model structures. Taking China as the study area, we used the VIIRS AOD, together with multi-source auxiliary variables, to develop a spatially structured adaptive two-stage model to estimate ground-level PM2.5 concentrations at a 6-km spatial resolution. To this end, we first defined and calculated a dual distance from the ground-level PM2.5 monitoring data. We then applied the unweighted pair-group method with arithmetic means on dual distances and obtained 13 spatial clusters. Subsequently, we combined the time fixed effects regression (TEFR) model and geographically weighted regression (GWR) model to develop the spatially structured adaptive two-stage model. For each spatial cluster, we examined all possible combinations of auxiliary variables and determined the best model structure according to multiple statistical test results. Finally, we obtained the PM2.5 estimates through regression mapping. At least seven model-fitting data records per day made a good threshold that could best overcome the model overfitting induced by the second-stage GWR model at the minimum price of losing samples. The overall model fitting and ten-fold cross validation (CV) R2 were 0.82 and 0.60, respectively, under that threshold. Model performances among different spatial clusters differed to a certain extent. High-CV R2 values always exceeded 0.6 while low-CV R2 values less than 0.5 also existed. Both the size of the model-fitting data records and the extent of urban-industrial characteristics of spatial clusters accounted for these differences. The PM2.5 estimates agreed well with the PM2.5 observations with correlation coefficients all exceeding 0.5 at the monthly, seasonal, and annual scales. East of Hu’s line and north of the Yangtze River were characterized by high PM2.5 concentrations. This study contributes to the understanding of how well VIIRS AOD can retrieve ground-level PM2.5 concentrations at the national scale and strategies for building ground-level PM2.5 satellite remote sensing retrieval models.

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.

Can We Detect the Brownness or Greenness of the Congo Rainforest Using Satellite-Derived Surface Albedo? A Study on the Role of Aerosol Uncertainties

The ability of spatial remote sensing in the visible domain to properly detect the slow transitions in the Earth’s vegetation is often a subject of debate. The reason behind this is that the satellite products often used to calculate vegetation indices such as surface albedo or reflectance, are not always correctly decontaminated from atmospheric effects. In view of the observed decline in vegetation over the Congo during the last decade, this study investigates how effectively satellite-derived variables can contribute to the answering of this question. In this study, we use two satellite-derived surface albedo products, three satellite-derived aerosol optical depth (AOD) products, two model-derived AOD products, and synthetic observations from radiative transfer simulations. The study discusses the important discrepancies (of up to 70%) found between these satellite surface albedo products in the visible domain over this region. We conclude therefore that the analysis of trends in vegetation properties based on satellite observations in the visible domain such as NDVI (normalized difference vegetation index), calculated from reflectance or albedo variables, is still quite questionable over tropical forest regions such as the Congo. Moreover, this study demonstrates that there is a significant increase (of up to 14%) in total aerosols within the last decade over the Congo. We note that if these changes in aerosol loads are not correctly taken into account in the retrieval of surface albedo, a greenness change of the surface properties (decrease of visible albedo) of around 8% could be artificially detected. Finally, the study also shows that neglecting strong aerosol emissions due to volcano eruptions could lead to an artificial increase of greenness over the Congo of more than 25% in the year of the eruptions and up to 16% during the 2–3 years that follow.

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.

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.

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 &amp; 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 &lt; 0.2), partially vegetated surfaces (PVS, 0.2 ≤ NDVI ≤ 0.3), moderately vegetated surfaces (MVS, 0.3 &lt; NDVI &lt; 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.

Establishment of PM2.5 Prediction Model Based on MAIAC AOD Data of High Resolution Remote Sensing Images

Long-term exposure to seriously polluted atmospheric environment is closely related to a variety of human diseases. The long-term and continuous monitoring of PM[Formula: see text] (the aerodynamic particle diameter is less than or equal to 2.5[Formula: see text][Formula: see text]m) is of great significance as it is one of the major air pollutants. Because of the small number of PM[Formula: see text] monitoring stations in China, it is very difficult to accurately estimate the continuous scale of air pollution, but PM[Formula: see text] concentration can be acquired by using the aerosol products inversed by remote sensing data. However, due to the limitation of the inversion algorithm, the traditionally used moderate resolution imaging spectroradiometer (MODIS) aerosol products cannot meet the precision and practical requirements in both spatial resolution and effective numerical coverage. This paper adopts MAIAC AOD, i.e. the aerosol optical depth (AOD) products produced by using the new multi-angle implementation of atmospheric correction (MAIAC) algorithm, and we take the Beijing–Tianjin–Hebei region in China as the research area. Based on the neural network algorithm, the MAIAC AOD data is mainly used and the meteorological parameter data is supplemented to establish the PM[Formula: see text] concentration prediction model to obtain the PM[Formula: see text] concentration distribution map. The prediction accuracy, the effective numerical coverage and spatial resolution have been greatly improved.

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.

Aerosol optical depth retrieval over the city of Abu Dhabi, United Arab Emirates (UAE) using Landsat-8 OLI images

Atmospheric aerosol optical depth (AOD) products based on orbiting satellites such the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and National Oceanic and Atmospheric Administration (NOAA)'s Geostationary Operational Environmental Satellite (GOES) Aerosol Smoke Products (GASP) can provide global and regional daily AOD distributions. Their application to air quality monitoring at local scales such as urban environments is rather limited due to their coarse spatial resolution. Recently, there has been a growing interest in retrieving AOD products based on high resolution optical imagery. The primary objectives of this research study are to assess AOD retrieval over urban city areas using high spatial resolution satellite imagery. With the assumption of Lambertian surface, the principle of AOD retrieval developed is based on the radiative transfer theory using Tanré Equation. To solve the radiative transfer equation, the study adopted the radiative transfer model Second Simulation of the Satellite Signal in the Solar Spectrum (6S) and constructed a Look-Up-Table (LUT) assuming a continental aerosol model. The land surface reflectance (LSR) over the entire study period using Landsat 8 OLI for the period 2016–2017 showed very little variability. The retrieval of AOD is performed by comparing measured and simulated TOA reflectance. The validation is performed using an AERONET Masdar Institute ground based site located within the study area and the 10 km resolution MODIS Collection (C6) Deep Blue AOD (MOD04) products. The validation shows that AOD retrieval approach yields a root mean square error (RMSE) of 0.04, a good correlation with an R2 equivalent to 67% and that the 58% of the retrieved values fall within the expected error (EE). Also, results show that Landsat-OLI retrieval are better and less biased as compared to MOD04 AOD products and that the used approach can provide much more details compared to the 10 km Deep Blue MODIS AOD products.

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.