MODIS Aerosol Optical Depth Products Research Articles

Impact of satellite AOD data on top-down estimation of biomass burning particulate matter emission

Particulate matter emitted from open biomass burning affects climate, air quality and public health. In the development of remote sensing techniques, top-down methods using satellite observations have become an effective way to estimate particulate matter emissions, but different spatial resolution and coverage of satellite aerosol optical depth (AOD) products introduce great uncertainties. In this work, we assess the differences in total particulate matter (TPM) emission when calculated using different satellite AOD data. To do this, we derive top-down biomass burning TPM emission coefficients (Ce) of Australia based on 1 km and 10 km MODIS AOD products, and compare the results of emission estimation. Using high spatial resolution AOD data, a 90 % decrease of Ce mean value is found in Australia, and the quality of Ce improves by 290 %. When we use 1 km AOD data in place of 10 km AOD data, the estimation of biomass burning TPM emissions in Australia during 2012–2020 drops from 1.08Tg to 0.11Tg; the temporal trends of the two products remain the same. The TPM emission estimates for biome based on higher spatial resolution AOD data in this study are lower than Fire Energetics and Emissions Research (FEERv1) inventories by factors of 0.08–0.2. Our work shows that using satellite AOD products of higher spatial resolution avoids overestimation of biomass burning TPM emissions, and improves the quality of final estimates, providing a possible way to understand biomass burning emission more accurately.

A Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) for Geostationary Meteorological Satellite Over Heterogeneous Surfaces

High-frequency aerosol observation from new-generation geostationary meteorological satellite is capable to capture and monitor the spatiotemporal dynamic variation of aerosols, which is of vital significance to environmental research and climate studies. Due to the diversity and complexity of land cover, it is a challenge to retrieve aerosol properties with high accuracy over land especially over heterogeneous land surfaces. In this study, a Geometry-Discrete Minimum Reflectance Aerosol Retrieval Algorithm (GeoMRA) has been proposed to retrieve 10-min high temporal resolution aerosol optical depth (AOD) datasets for geostationary Himawari-8 AHI sensor, aiming at providing universal bidirectional reflectance distribution function (BRDF) descriptions for different land surfaces with different heterogeneous extent. The AOD retrievals from GeoMRA demonstrate good consistency against the ground-based AERONET measurements in the East Asia from 2015 to 2020, with a correlation coefficient (R) of 0.883 and approximately 65.6% of matchups falling within the expected error envelope of ±(0.05 + 15%). Intercomparison between the GeoMRA retrieved AOD and other operational AOD products shows that the GeoMRA AOD retrievals, which generally possess similar spatial distribution and accuracy as MODIS AOD products, have better performances than the Japan Aerospace Exploration Agency (JAXA) AOD products by providing more accurate AOD retrievals with higher spatial coverage. Moreover, the AOD bias analyses further demonstrate the robustness of GeoMRA algorithm, and an extreme haze event shows that the continuous GeoMRA AOD images illustrate smoother temporal variations than JAXA AOD products, demonstrating its efficacy and reliability in capturing the process of haze transport and monitoring the continuous spatiotemporal variation of aerosol. The above results suggest the considerable accuracy of GeoMRA algorithm for scientific application requirement, and demonstrate the robustness of proposed BRDF scheme in describing heterogeneous surfaces with diverse reflectance distribution.

A high-resolution index suitable for multi-pollutant monitoring in urban areas

In view of the problems involved in remote sensing monitoring of urban air quality, including low spatial resolution, only for a single pollutant, complex inversion algorithms, and difficultly obtaining parameter values, in this study, a new difference smog index (DSI) was developed, and then a comparison with the normal difference haze index, the difference index, and the MODIS aerosol optical depth products. The results show that the DSI model developed in this study has a higher accuracy and a better monitoring effect in urban areas, and it has a higher resolution (30 m), which greatly improves the degree of refinement of the remote sensing monitoring. The DSI model has a higher extensibility, and it is suitable for monitoring the AQI, PM2.5, NO2. The DSI model proposed in this paper is simple and easy to use, and thus, it has a high potential for application and deserves promotion in urban air quality monitoring.

Influence of Spatial Resolution and Retrieval Frequency on Applicability of Satellite-Predicted PM2.5 in Northern China

Satellite aerosol optical depth (AOD) products have been widely used in estimating fine particulate matter (PM2.5) concentrations near the surface at a regional scale, and perform well compared with ground measurements. However, the influence of limitations such as retrieval frequency and the spatial resolution of satellite AODs on the applicability of predicted PM2.5 values has been rarely considered. With three widely used MODIS AOD products, including Multi-Angle Implementation of Atmospheric Correction (MAIAC), Deep Blue (DB) and Dark Target (DT), here we evaluate the influence of their spatial resolution and sampling frequency by estimating daily PM2.5 concentrations in the Beijing-Tianjin-Hebei (BTH) region of northern China during 2017 utilizing a mixed effects model. The daily concentrations of PM2.5 derived from MAIAC, DB and DT AOD all have high correlations (R2: 0.78, 0.8, and 0.78) with the observed values, but the predicted annual PM2.5 exhibits a distinct spatial distribution. DT estimation obviously underestimates annual PM2.5 in polluted areas due to lower sampling of heavy pollution events. By contrast, the retrieval frequency (~40-60%) of MAIAC and DB AOD can represent well annual PM2.5 in nearly all 83 sites tested. However, MAIAC and DB-derived PM2.5 have a larger bias compared with observed values than DT, indicating that the large spatial variation of aerosol properties can exert an influence on the reliability of the statistical AOD-PM2.5 relationship. Also, there is notable difference between MAIAC and DB PM2.5 due to their different cloud screening methods. The MAIAC PM2.5 with high spatial resolution at 1 km can capture much finer hotpots than DB and DT at 10 km. Our results suggest that it is crucial to consider the applicability of satellite-predicted PM2.5 values derived from different aerosol products according to the specific requirements besides modeling the AOD-PM2.5 relationship.

Evaluation and improvement of MODIS aerosol optical depth products over China

In this study, we use the level-1.5 and level-2.0 aerosol optical depth (AOD) from the Aerosol Robotic Network (AERONET) Version 3 dataset at 12 sites in China to evaluate the MODIS Collection 6.1 (C6.1) AOD products retrieved using three distinct algorithms: Dark Target (DT), Deep Blue (DB), and DTB merged from DT and DB from Terra and Aqua. The performance of the three algorithms is evaluated. Based on these evaluations, a simple and efficient AOD retrieval algorithm at a high spatial resolution of 1.0 km for China is proposed. AODs at this spatial resolution over China are then retrieved using the methods of DT and DB, respectively. The evaluation results showed that: (1) there is little difference in the AOD products derived from Terra and Aqua. The differences in the determination coefficient (R2) between the two platforms at most sites are less than 0.05. (2) There are relatively large differences in AODs between the AERONET level-1.5 dataset and MODIS dataset while these differences are mostly filtered out by the quality assurance process of the AERONET level-2.0 dataset. The statistical tests indicate that the MODIS DTB AOD product is generally better than those from the other two algorithms. Using the new algorithm developed in this study, the AODs at a high spatial resolution of 1 km in the whole of China are determined and the results are compared with the MODIS DTB product. The results show that the AODs determined using the new method agree reasonably well with those of the MODIS DTB dataset though the new results have slightly negative biases in the wintertime. However, these negative biases may not be a negative sign due to the fact that the MODIS AODs are subject to a positive bias relative to the AERONET AODs.

Retrieval of Aerosol Optical Depth Using Satellite Data Associated with Ground-based Observations over Urban and Rural Areas

Aerosol optical depth (AOD) can be retrieved accurately with sequential ground-based measurements of direct and diffuse solar radiance. However, spatial coverage and location frequency cause certain limitations. Hence, satellite image data are a proper tool for obtaining aerosol optical depth products with more spatial information and patterns of aerosol distribution. Currently, aerosol remote sensing may enhance our understanding of the optimal approach to AOD retrieval over urban and rural areas, and how it differs due to the characteristics of surface reflectivity. The article deals with the concepts of contrast reduction, and dark target approaches are examined using Landsat imaging and the observation of a sun photometer for integrating aerosol optical depth distribution over the city of Taipei in Taiwan. For areas with bright surfaces, such as urban areas, the above concepts were applied using the dispersion coefficient method with a sun photometer, in order to reduce errors considerably in the product. In contrast, a dark target algorithm with a relationship of surface reflectance between the blue (0.49 μm), red (0.66 μm), and infrared (2.1 μm) spectral bands is suitable for moist soils and vegetation areas. The retrieval of AOD spatial distribution is compared with MODIS AOD products and AERONET to verify the accuracy of the results. The RMSE ranged from 0.2 to 0.4, and about 50% of the data were within expected error margins (EE=± (0.05+0.15 AODsunphotometer).

Dust Detection and Intensity Estimation Using Himawari-8/AHI Observation

In this study, simple dust detection and intensity estimation methods using Himawari-8 Advanced Himawari Imager (AHI) data are developed. Based on the differences of thermal radiation characteristics between dust and other typical objects, brightness temperature difference (BTD) among four channels (BT11–BT12, BT8–BT11, and BT3–BT11) are used together for dust detection. When considering the thermal radiation variation of dust particles over different land cover types, a dynamic threshold scheme for dust detection is adopted. An enhanced dust intensity index (EDII) is developed based on the reflectance of visible/near-infrared bands, BT of thermal-infrared bands, and aerosol optical depth (AOD), and is applied to the detected dust area. The AOD is retrieved using multiple temporal AHI observations by assuming little surface change in a short time period (i.e., 1–2 days) and proved with high accuracy using the Aerosol Robotic Network (AERONET) and cross-compared with MODIS AOD products. The dust detection results agree qualitatively with the dust locations that were revealed by AHI true color images. The results were also compared quantitatively with dust identification results from the AERONET AOD and Ångström exponent, achieving a total dust detection accuracy of 84%. A good agreement is obtained between EDII and the visibility data from National Climatic Data Center ground measurements, with a correlation coefficient of 0.81, indicating the effectiveness of EDII in dust monitoring.

Bayesian Merging of MISR and MODIS Aerosol Optical Depth Products Using Error Distributions From AERONET

Aerosol Optical depth (AOD) routinely retrieved by spaceborne sensors (e.g., MODIS and MISR) is widely used in studies focused on atmospheric aerosol characterization, their variability/trends, and environmental/climate impacts. Despite significant advancement in the understanding of aerosol loading patterns, there exist global/regional differences among AOD products from various satellite sensors; owing to differences in retrieval algorithms, spatial/temporal resolution and sampling, and cloud-screening schemes. A merged AOD dataset combining multiple satellite retrievals is therefore desirable, which utilizes the strengths of individual products, and at the same time reduces biases relative to ground-based measurements (e.g., from aerosol robotic network (AERONET)). In view of this, a Level-2 ‘‘merged’’ AOD dataset based on MODIS and MISR retrievals is developed in this study using Bayesian principles, which takes into account the error distribution of AOD from AERONET data. The merged AOD dataset is demonstrated over the Indo-Gangetic Plains, in southern Asia, and is intercompared with existing satellite and AERONET data. The RMSE of the merged AOD data (0.08–0.13) is lower than the MISR and MODIS retrievals. Additionally, the merged AOD data have higher correlation with AERONET data ( r within 0.89–0.93), compared to MISR (0.82–0.89) and MODIS (0.76–0.77) data. In terms of the expected error (EE), the accuracy of merged data is found to be higher, with larger percent of merged AOD within the EE envelope (74.7%–88.7%) compared to MISR (62.7%–84.9%) and MODIS (67.9%–69.8%) data. The merging methodology and resulting dataset are especially relevant in the scenario of fusing multisensor retrievals for producing long-term satellite-based climate data records.

Analysis of Spatio-temporal Distribution and Variation Characteristics of Aerosol Optical Depth over the Northwest of China by MODIS C6 Product

This work compared the collection 6 MODIS aerosol optical depth products with sun photometer data from AERONET, and evaluated the applicability of C6 merged data in the northwest of China. The spatio-temporal variation characteristics and influencing factors of AOD over northwest of China were analyzed by MODIS/AQUA C6 MYD08-M3 products from 2006 to 2015, and the results showed that Southern Xinjiang and Guanzhong region of Shanxi province had relatively high AOD, whereas the south of Qinghai and Gansu province had low values; Junggar Basin was the incremental area of AOD for the first 5 years and second 5 years, whereas converse result existed in Qaidam Basin and Hetao area. Temporally, the yearly mean AOD ranged from 0.18 to 0.22, and the declined trend occurred in 2011, and the annual decrease rate was about 0.32%; Because of different intensity and frequency of dust weather, the south of Xinjiang showed a significant interannual variation characteristic; East of the northwest displayed an obviously declining trend from 2011 to 2015, and the yearly mean decrease was about 1.1%. The AOD in north of Xinjiang and Qinghai-Tibet remained stable. The seasonal changes of AOD among different areas over the northwest had the same trend, all gradually declined from spring to autumn, and increased in winter.

Comparison and Evaluation of Different MODIS Aerosol Optical Depth Products Over the Beijing-Tianjin-Hebei Region in China

Many aerosol retrieval algorithms based on the remote sensing technology have been developed and applied to produce aerosol optical depth (AOD) products for different satellite sensors. The dark target (DT) and deep blue (DB) algorithms are two main MODIS aerosol retrieval algorithms that are suitable for dark or bright areas. The estimation of land surface reflectance (LSR) is necessary to improve the accuracy of AOD retrievals. Therefore, in this paper, a new procedure to improve LSR estimation using MODIS surface reflectance products is developed. A new high-resolution aerosol retrieval algorithm with a priori LSR database support (HARLS) is proposed. The purpose of this paper is to evaluate the spatial adaptability of different MODIS AOD products produced by the above three algorithms. The Beijing-Tianjin-Hebei (Jing-Jin-Ji) region, which features complex surface structures and serious air pollution, was chosen as the study area, and the different AOD products are validated using aerosol robotic network (AERONET) AOD ground measurements from four stations located in dark and bright areas. Compared with the DT retrievals $(\text{R}\approx 0.88- 0.95)$ , the C6 DB AOD retrievals yield a stronger correlation $(\text{R} \approx 0.94- 0.97)$ with AERONET AOD and lower RMSE, MRE and MAE values, resulting in approximately 20%–30% less average overestimation. The C6 DT $\&$ DB AOD results show a retrieval quality $(\text{R}\approx 0.93- 0.97)$ similar to that of DB, with approximately 50%–70% of the collections falling within the expected error (EE). Moreover, DT $\&$ DB is much better than DT, with more than approximately 10%–20% of the collections falling within the EE. However, HARLS achieves a high correlation $(\text{R}\approx 0.93- 0.96)$ with the AERONET AODs, with low RMSE $(\approx 0.118- 0.128)$ and MAE $(\approx 0.09- 0.12)$ and small offsets $(\text{intercept}\approx 0.00- 0.04)$ . HARLS retrievals exhibited 7%–8% less uncertainty than the C6 DB retrievals, 37%–38% less uncertainty than the C6 DT $\&$ DB retrievals, and 39%–44% less uncertainty than the C5 and C6 DT retrievals. HARLS achieves greater accuracy and reliability in AOD retrieval, and it is less biased $(\text{RMB} \approx 0.90- 1.10)$ and better overall than the routine MODIS aerosol products over the Jing-Jin-Ji region.

A NOVEL INDEX FOR ATMOSPHERIC AEROSOL TYPES CATEGORIZATION WITH SPECTRAL OPTICAL DEPTHS FROM SATELLITE RETRIEVAL

Abstract. In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as Ångström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global area which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested, a novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA providing. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized between mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well agreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips highly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD compositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol Subtype.

A NOVEL INDEX FOR ATMOSPHERIC AEROSOL TYPES CATEGORIZATION WITH SPECTRAL OPTICAL DEPTHS FROM SATELLITE RETRIEVAL

In general, the type of atmospheric aerosols can be efficiently identified with the characteristics of optical properties, such as Ångström exponent (AE) and single scattering albedo (SSA). However, the retrieval of SSA is not frequently available to global area which may cause the difficulty in the identification of aerosol type. Since aerosol optical depth (AOD) can be easily requested, a novel index in terms of AOD, Normalized Gradient Aerosol Index (NGAI), is proposed to get over the constraint on SSA providing. With the NGAI derived from MODIS AOD products, the type of atmospheric aerosols can be clearly categorized between mineral dusts, biomass burning and anthropogenic pollutants. The results of aerosol type categorization show the well agreement with the ground-based observations (AERONET) in AE and SSA properties, implying that the proposed index equips highly practical for the application of aerosols type categorization by means of remote sensing. In addition, the fraction of AOD compositions can be potentially determined according to the value of index after compared with the products of CALIPSO Aerosol Subtype.

Aerosol Optical Depth investigated with satellite remote sensing observations in China

In this study, Aerosol Optical Depth (AOD) at 550nm from the MODIS sensor on board the Terra/Aqua satellites were compared with sun photometer (CE-318) measurements from 11 AERONET stations in China. The average correlation coefficient (R) value from the AOD product, using the Aqua-MODIS Deep Blue algorithm, in the Hexi Corridor was 0.67. The MODIS Dark Target algorithm AOD product is superior to Deep Blue algorithm AOD products in SACOL of the Semi-arid regions of the Loess Plateau. These two kinds of algorithm are not applicable to sites in Lanzhou city. The average R value of Dark Target algorithm AOD MODIS products is 0.91 for Terra and 0.88 for Aqua in the eastern part of China. According to the analysis of spatial and temporal characteristics of the two MODIS AOD products in China, high value areas are mainly distributed in the southern part of Xinjiang (0.5~0.8), Sichuan Basin (0.8~0.9), North China (0.6~0.8) and the middle and lower reaches of the Changjiang River (0.8~1.0). The Deep Blue algorithm for Aqua-MODIS is a good supplement for the retrieval of AOD above bright surfaces of deserts in Northwest China.

Latest decade’s spatial–temporal properties of aerosols over China based on Multiangle Imaging SpectroRadiometer observations

The analysis of the spatial–temporal variability and trends of aerosols over China based on ∼11 years (February 2000 to December 2010) of Terra-Multiangle Imaging SpectroRadiometer (MISR) Level 3 aerosol products is the focus. The study shows that the MISR aerosol optical depth (AOD) is in good agreement with corresponding AOD from AERONET stations and suggests that MISR aerosol products have higher applicability compared with MODIS AOD products over China. The spatial–temporal distribution and trends for annual and seasonal AOD values over the middle and eastern regions of China are the main focus. The results of our analysis show the significant characteristics and seasonal variation of the aerosol distribution over the study areas. The seasonal distribution in AOD over the study areas is not consistent with the MODIS data. An insignificant trend (5.3%) is observed over the whole study area during the study period. The larger positive tendencies are found in the Yangtze River Delta and the Pearl River Delta (PRD); the values increased by 24.4% and 18% and are considered to be significant with a confidence level of >95% . However, in contrast to the recently reported negligible AOD trends of 0.002 per decade using MODIS data over Sichuan, significant increasing trends of 0.046 per 11 years for MISR over the same areas have been measured. The analysis of seasonal variation revealed the seasonal trend in the AOD and the main types of aerosols. During spring, the largest increasing trend (30.2%) is observed in the PRD with a stable composition of fine and coarse aerosols. A pronounced decreasing trend is found over Shanxi and decreased by −18.7% in spring during the period, which has not been previously reported. MODIS-Terra currently shows artificial negative AOD trends over land. Therefore, trends derived from MODIS-Terra may not reflect the realistic decadal aerosol changes and variability. The study shows that seasonal distribution and trend analyses are inconsistent when using MISR AOD products and MODIS AOD products over study areas, which indicates that the analysis of aerosol variation using MISR sensors is necessary.

Global evaluation of the Collection 5 MODIS dark-target aerosol products over land

Abstract. NASA's MODIS sensors have been observing the Earth from polar orbit, from Terra since early 2000 and from Aqua since mid 2002. We have applied a consistent retrieval and processing algorithm to both sensors to derive the Collection 5 (C005) dark-target aerosol products over land. Here, we validate the MODIS along-orbit Level 2 products by comparing to quality assured Level 2 AERONET sunphotometer measurements at over 300 sites. From 85 463 collocations, representing mutually cloud-free conditions, we find that >66% (one standard deviation) of MODIS-retrieved aerosol optical depth (AOD) values compare to AERONET-observed values within an expected error (EE) envelope of ±(0.05 + 15%), with high correlation (R = 0.9). Thus, the MODIS AOD product is validated and quantitative. However, even though we can define EEs for MODIS-reported Ångström exponent and fine AOD over land, these products do not have similar physical validity. Although validated globally, MODIS-retrieved AOD does not fall within the EE envelope everywhere. We characterize some of the residual biases that are related to specific aerosol conditions, observation geometry, and/or surface properties, and relate them to situations where particular MODIS algorithm assumptions are violated. Both Terra's and Aqua's–retrieved AOD are similarly comparable to AERONET, however, Terra's global AOD bias changes with time, overestimating (by ~0.005) before 2004, and underestimating by similar magnitude after. This suggests how small calibration uncertainties of <2% can lead to spurious conclusions about long-term aerosol trends.

Aerosol Monitoring over Athens Using Satellite and Ground-Based Measurements

Satellite data of moderate spatial resolution (MODIS and MERIS) were used to retrieve the aerosol optical depth (AOD) over the urban area of Athens. MODIS products were obtained at a horizontal resolution of 10 by 10 km2centered over Athens, while the differential textural analysis (DTA) code was applied to MERIS images to retrieve relative-to-reference AOD with a resolution of 260 m by 290 m. The possibility of exploiting the full resolution of MERIS data in retrieving AOD over a grid of a few hundreds metres was thereby investigated for the first time. MERIS-based AOD, centred at 560 nm, showed strong positive correlation to ground-basedPM10data (R2= 0.85), while MODIS AOD products were in agreement with both MERIS andPM10. Back trajectories were used to study the impact of atmospheric conditions prevailing during the examined days. Days associated with Saharan air masses corresponded to enhanced AOD and predominance of coarse-mode particles. The results suggest that, at least for the case of Athens, AOD retrieved by MERIS images using the DTA code over cloud-free areas can be related toPM10. The accuracy of retrieval mainly depends on the successful selection of the reference satellite data, namely, an image being least contaminated by tropospheric aerosols.