Moderate Resolution Imaging Spectroradiometer Aerosol Optical Depth Retrievals Research Articles

Evaluation and comparison of MODIS aerosol optical depth retrieval algorithms over Brazil

Brazil experiences significant aerosol loads throughout the year, particularly during the biomass-burning season in the Amazon. Thus, given the importance of aerosols for climate and health, this research aimed to validate and compare Aerosol Optical Depth (AOD) products over Brazil. This evaluation considers three algorithms that retrieve AOD by using data from Moderate Resolution Imaging Spectroradiometer (MODIS) sensor: Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), and Multi-Angle Implementation of Atmospheric Correction (MAIAC). To validate the satellite data, 17 sunphotometers from the AErosol RObotic NETwork (AERONET) were utilized. The results show a high correlation (R > 0.9) between the MODIS-AOD products and ground-based data. However, MODIS-AOD products tend to overestimate or underestimate AOD values, depending on the specific AOD value and algorithm evaluated. Additionally, it was observed that the performance of the algorithms is influenced by factors such as land cover type, view geometry, and the spatiotemporal distribution of aerosols. In particular, challenges were encountered when retrieving robust AOD data for Savanna and Urban cover classes. In conclusion, the results indicate that MAIAC and DB algorithms demonstrate greater stability in retrieving AOD values. Nevertheless, caution should be exercised when applying these products to map aerosols on specific surfaces, such as urban areas.

MODIS aerosol optical depth retrieval based on random forest approach

ABSTRACT Despite significant improvement in Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval, high-resolution–high-accuracy AOD retrieval remains a challenging task. This study utilizes machine learning for AOD retrieval of MODIS data. The global long-time-series data of AERONET sites and corresponding MODIS data in time and space were used as sample training data for aerosol retrieval via use of the random forest (RF) approach. The accuracy and stability of retrieval were ensured by processing AERONET site data, performing time–space matching between different data types, and determining related parameters in the RF model. MODIS data use bands 1–7 of the top-of-atmosphere reflectance (TOA) – extending from the visible to near-infrared radiation spectra – along with the corresponding observation geometry data, global land surface satellite (GLASS) albedo dataset, and normalized difference vegetation index (NDVI) data. The proposed method facilitates realizationthe of high-accuracy aerosol retrieval. Furthermore, significant enhancement in the efficiency of aerosol inversion is an added advantage.

Assessment of the Representativeness of MODIS Aerosol Optical Depth Products at Different Temporal Scales Using Global AERONET Measurements

Assessments of long-term changes of air quality and global radiative forcing at a large scale heavily rely on satellite aerosol optical depth (AOD) datasets, particularly their temporal binning products. Although some attempts focusing on the validation of long-term satellite AOD have been conducted, there is still a lack of comprehensive quantification and understanding of the representativeness of satellite AOD at different temporal binning scales. Here, we evaluated the performances of the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products at various temporal scales by comparing the MODIS AOD datasets from both the Terra and Aqua satellites with the entire global AErosol RObotic NETwork (AERONET) observation archive between 2000 and 2017. The uncertainty levels of the MODIS hourly and daily AOD products were similarly high, indicating that MODIS AOD retrievals could be used to represent daily aerosol conditions. The MODIS data showed the reduced quality when integrated from the daily to monthly scale, where the relative mean bias (RMB) changed from 1.09 to 1.21 for MODIS Terra and from 1.04 to 1.17 for MODIS Aqua, respectively. The limitation of valid data availability within a month appeared to be the primary reason for the increased uncertainties in the monthly binning products, and the monthly data associated uncertainties could be reduced when the number of valid AOD retrievals reached 15 times in one month. At all three temporal scales, the uncertainty levels of satellite AOD products decreased with increasing AOD values. The results of this study could provide crucial information for satellite AOD users to better understand the reliability of different temporal AOD binning products and associated uncertainties in their derived long-term trends.

Satellite-based identification of aerosol particle species using a 2D-space aerosol classification model

The classification of aerosol types using remote sensing tools is crucial for improving our knowledge on the impacts of aerosol species on global and regional climate. Ground-based classification methods have proven useful though they are limited by discontinuous spatial observations of aerosol optical properties. As such, there is a need to develop methods in aerosol species identification using satellite data. In this study, a 2D-space model suitable for MODIS AOD bands is developed to distinguish aerosol particle species based on aerosol optical depth (AOD) and aerosol relative optical depth (AROD). The model can be used to classify five aerosol types, including biomass burning (BB: AOD470 ≥ 0.45, AROD660/470 < 0.58), urban industry (UI: AOD470 ≥ 0.45, 0.68 > AROD660/470 ≥ 0.58), sub-continental (SB: AOD470 ≥ 0.45, 0.91 > AROD660/470 ≥ 0.68), desert dust (DD: AROD660/470 ≥ 0.91) and continental (CO: AOD470 < 0.45, AROD660/470 < 0.91). AROD values corresponding to different types of aerosols are calculated through Mie scattering theory using the aerosol particle size distribution parameters and spectral complex refractive index. The accuracy and applicability of the model are verified in three steps: 1) we compare aerosol discrimination results of model with ground-based data and the local atmospheric conditions over different regions, 2) we compare aerosol identification results from Moderate Resolution Imaging Spectroradiometer (MODIS) AOD retrieval and ground-based data, and 3) we test our model to validate its use in individual cases studies and regional observations. Our results demonstrate that aerosol species classification for each study region is consistent with the characteristics of their atmospheric conditions, and the classification results using MODIS AOD retrieval are similar to the ground-based observation results. The differences in MODIS versus AERONET AOD, caused by MODIS observation errors, is the major contributor to the differences between ground-based and satellite-based recognition results. Our three-steps validation results indicate that the 2D-space model developed in this paper can accurately identify different types of aerosols and has strong applicability. This provides a basis for the use of satellite data for continuous spatial identification of aerosol type, which greatly promotes research in related fields.

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.

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

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

Is the Awareness of the Aerosol State Useful in Predicting Enhanced Lightning for Lightning-Producing Storms over Northern Alabama?

AbstractThe Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals from theTerraandAquasatellites currently provide the largest satellite aerosol dataset for investigating relationships to meteorological phenomena, such as aerosol impact on electrification in deep convection. The usefulness of polar-orbiting satellite aerosol retrievals in lightning inference is examined by correlating MODIS AOD retrievals with lightning observations of the thunderstorms in the summers during 2002–14 over northern Alabama. Lightning flashes during the 1400–1700 local standard time peak period show weak but positive correlations with the MODIS AOD retrievals 2–4 h earlier. The correlation becomes stronger in particular meteorological conditions, including weak vertical wind shear and prevailing northerly winds over northern Alabama. Results show that the MODIS AOD retrievals are less useful in predicting enhanced lightning flash rate for lightning-producing storms than the forecasts of other meteorological variables that are more closely linked to the intensification of convective storms. However, when relatively weaker convective available potential energy (CAPE) is forecast, the probability of enhanced lightning flash rate increases in a more polluted environment, making the knowledge of aerosols more useful in lightning inference in such CAPE regimes. The aerosol enhancement of lightning, if present, may be associated with enhanced convergence in the boundary layer and secondary convection.

Comparison of aerosol optical depth between observation and simulation from MIROC-SPRINTARS: Effects of temporal inhomogeneous sampling

The global distribution of aerosol optical depth (AOD) is simulated using an aerosol transport model coupled to an atmospheric general circulation model with high spatial and temporal resolution. Daily representative AOD from model simulation is estimated after consideration of observation sampling in daytime (ground-based) and overpass time (satellite) after cloud masking. Large deviations in AOD are found after considering temporally inhomogeneous sampling, with positive differences over desert regions and negative differences over anthropogenic pollution and biomass burning regions. Mean difference in daily AOD of 5.33% (standard deviation of 8.02%), because of temporal inhomogeneous sampling, is identified based on observation time information from the Moderate-Resolution Imaging Spectroradiometer (MODIS). Relative differences in AOD of >50% and >30% were found in 7.9% and 22.8% of the data, respectively. Based on the observation time information from the AERONET, relative root mean square error (rRMSE) of AOD due to temporal inhomogeneous sampling is estimated to be 4.30–18.66%. After correcting for temporal sampling inhomogeneity, the simulated global AOD was compared with AODs from MODIS and AERONET. The simulated AOD becomes lower than MODIS AOD because of emission and transport discrepancies related to dust, a limited accounting of nitrate processes, and limitation errors from MODIS AOD retrieval. A regional positive bias in SPRINTARS AOD was found in biomass burning regions, which is due to transport pattern errors related to the initial injection height of emissions. A weak correlation is found over the regions with multiple aerosol sources because of complex interactions of individual aerosol types.

Satellite remote sensing of fine particulate matter (PM2.5) air quality over Beijing using MODIS

Fine particulate matter (aerodynamic diameters of less than 2.5 µm, PM2.5) air pollution has become one of the major environmental challenges, causing severe environmental issues in urban visibility, climate, and public health. In this study, ground-level PM2.5 concentrations, air-quality categories (AQCs), and health risk categories (HRCs) over Beijing, China, have been estimated based on mid-visible column aerosol optical depth (AOD) measurements extracted from Moderate Resolution Imaging Spectroradiometer (MODIS) data on board both Terra and Aqua satellites. Our results indicate that the MODIS AOD retrievals at 550 nm (AOD550) match hourly aerosol robotic network (AERONET) measurements with correlation coefficients (r) of 0.950 for Terra and 0.895 for Aqua. The relationship between ground-level PM2.5 and MODIS AOD550 from March 2012 to February 2013 showed correlation coefficients of 0.69, 0.60, and 0.73 for spring, summer, and autumn, respectively. The atmospheric boundary layer height and relative humidity (RH) adjustments improved the AOD–PM2.5 relationship in summer months. The estimates of daily average PM2.5 from satellite measurements were used to predict both AQCs and HRCs, which are well matched with observations. Satellite remote sensing of atmospheric aerosols continues to show great potential for estimating ground-level PM2.5 concentrations and can be further used to monitor the atmospheric environment in China.

Role of Coarse and Fine Mode Aerosols in MODIS AOD Retrieval: a case study over southern India

Abstract. In the present study we compare the MODIS (Moderate Resolution Imaging Spectroradiometer) derived aerosol optical depth (AOD) data with that obtained from operating sky-radiometer at a remote rural location in southern India (Gadanki, 13.45° N, 79.18° E) from April 2008 to March 2011. While the comparison between total (coarse mode + fine mode) AODs shows correlation coefficient (R) value of about 0.71 for Terra and 0.77 for Aqua, if one separates the AOD into fine and coarse mode, the comparison becomes very poor, particularly for fine mode with an R value of 0.44 for both Terra and Aqua. The coarse mode AOD derived from MODIS and sky-radiometer compare better with an R value of 0.74 for Terra and 0.66 for Aqua. The seasonal variation is also well captured by both ground-based and satellite measurements. It is shown that both the total AOD and fine mode AOD are significantly underestimated with slope of regression line 0.75 and 0.35 respectively, whereas the coarse mode AOD is overestimated with a slope value of 1.28 for Terra. Similar results are found for Aqua where the slope of the regression line for total AOD and fine mode AOD are 0.72 and 0.27 whereas 0.95 for coarse mode. The fine mode fraction derived from MODIS data is less than one-half of that derived from the sky-radiometer data. Based on these observations and comparison of single scattering albedo observed using sky-radiometer with that of MODIS aerosol models, we argue that the selection of aerosol types used in the MODIS retrieval algorithm may not be appropriate particularly in the case of southern India. Instead of selecting a moderately absorbing aerosol model (as being done currently in the MODIS retrieval) a more absorbing aerosol model could be a better fit for the fine mode aerosols, while reverse is true for the coarse mode aerosols, where instead of using "dust aerosols" which is relatively absorbing type, usage of coarse sea-salt particles which is less absorbing is more appropriate. However, not all the differences could be accounted based on aerosol model, other factors like errors in retrieval of surface reflectance may also be significant in causing underestimation of AOD by MODIS.

Mesoscale modeling and satellite observation of transport and mixing of smoke and dust particles over northern sub‐Saharan African region

AbstractThe transport and vertical distribution of smoke and dust aerosols over the northern sub‐Saharan African region are simulated in the Weather Research and Forecasting model with Chemistry (WRF‐Chem), which uses hourly dynamic smoke emissions from the Fire Locating and Modeling of Burning Emissions database derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) fire products. Model performance for February 2008 is evaluated using MODIS true color images, aerosol optical depth (AOD) measurements from the Aerosol Robotic Network, MODIS AOD retrievals, and the Cloud‐Aerosol Lidar data with Orthogonal Polarization (CALIOP) atmospheric backscattering and extinction products. Specification of smoke injection height of 650 m in WRF‐Chem yields aerosol vertical profiles that are most consistent with CALIOP observations of aerosol layer height. Between the equator and 10°N, Saharan dust is often mixed with smoke near the surface, and their transport patterns manifest the interplay of trade winds, subtropical highs, precipitation associated with the Intertropical Convergence Zone, and the high mountains located near the Great Rift Valley region. At the 700 hPa level and above, smoke layers spread farther to the north and south and are often above the dust layers over the Sahel region. In some cases, transported smoke can also be mixed with dust over the Saharan region. Statistically, 5% of the CALIOP valid measurements in February 2007–2011 show aerosol layers either above or between the clouds, reinforcing the importance of the aerosol vertical distribution for quantifying aerosol impact on climate in the Sahel region.

Investigating enhanced Aqua MODIS aerosol optical depth retrievals over the mid‐to‐high latitude Southern Oceans through intercomparison with co‐located CALIOP, MAN, and AERONET data sets

AbstractA band of enhanced aerosol optical depth (AOD) over the mid‐to‐high latitude Southern Oceans exists in some passive satellite‐based aerosol data sets, including Moderate Resolution Imaging Spectroradiometer (MODIS) products. Past studies suggest several potential causes contributing to this phenomenon, including signal uncertainty, retrieval bias, and cloud contamination. In this paper, quality‐assured Aqua MODIS aerosol products in this zonal band are investigated to assess cloud contamination as a cause. Spatially and temporally collocated cloud and aerosol products produced by the Cloud‐Aerosol Lidar with Orthogonal Polarization (CALIOP) project relative to Aqua MODIS AOD in this region are considered. Maritime Aerosol Network (MAN) and Aerosol Robotic Network (AERONET) AOD data are also collocated with Aqua MODIS retrievals for surface context. The results of this study indicate that the high Aqua MODIS AOD are not seen in the CALIOP aerosol products, cannot be screened using active profiling of collocated observations for cloud presence, and are not detected by ground‐based observations such as MAN and AERONET. Enhanced AOD values are attributable primarily to stratocumulus and low broken cumulus cloud contamination, as identified with CALIOP products. But these clouds explain only about 30–40% of the total anomaly. Cirrus cloud contamination is also a factor. However, in contrast to the rest of the globe, they contribute less overall, relative to low‐level liquid water clouds, which are considered likely the result of misidentification of relatively warm cloud tops compared with surrounding open seas.

A Study of Air Quality in the Southeastern Hampton–Norfolk–Virginia Beach Region with Airborne Lidar Measurements and MODIS Aerosol Optical Depth Retrievals

Abstract A study of air quality was performed using a compact, aircraft aerosol lidar designed in the Science Directorate at NASA Langley Research Center and Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrievals. Five flights of lidar measurements conducted in the Hampton–Norfolk–Virginia Beach, Virginia, region showed complex regional aerosol distributions. Comparisons with MODIS AOD at 10 km × 10 km and 5 km × 5 km resolutions show good agreement, with correlation R2 values of 0.82 and 0.88, respectively. Linear regressions of particulate matter with a diameter of less than 2.5 μm (PM2.5) and AOD within the ranges of 5–40 μg m−3 and 0.05–0.7, respectively, result in R2 values of ∼0.64 and ∼0.82 for MODIS and the Compact Aerosol Lidar, respectively. The linear regressions reflect approximately 51 μg m−3 to 1 AOD. These relationships are in agreement with previous findings for air pollution aerosols in the eastern United States and in northern Italy. However, large vertical variation is seen case by case, with planetary boundary layer heights ranging between 0.7 and 2 km and uncertainties ranging between 0.1 and 0.4 km. The results of the case studies suggest that AOD can be used as an indicator of surface measurements of PM2.5 but with larger uncertainties associated with small aerosol loading (AOD &amp;lt; 0.3).

Spatial characteristics of the difference between MISR and MODIS aerosol optical depth retrievals over mainland Southeast Asia

The difference between aerosol optical depths (AODs) retrieved from the Multi-angle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) is examined over mainland Southeast Asia from a spatial perspective. Though ideally the difference between these measurement methods should be small and randomly distributed over space, our analysis suggests that this difference has a strong negative relationship with MODIS AODs and tend to be spatially clustered. We quantify the spatial dependence in MISR/MODIS AOD differences and explore the extent to which the spatial patterns in these differences can be explained by variables that reflect the influence of physical environment and human activities. While these variables show a strong relationship with MISR/MODIS AOD differences, the results also suggest that further research is needed to fully understand the spatial dependence in these differences.

Evaluation of the MODIS aerosol optical depth retrieval over different ecosystems in China during EAST-AIRE

The accuracy of the Moderate Resolution Imaging Spectroradiometer's (MODIS) aerosol products is still uncertain in China, due to a lack of validation by long-term and large-scale ground-based observations. In this paper, the MODIS aerosol optical depth (AOD) product is evaluated using Chinese Sun Hazemeter Network (CSHNET) data as ground truths over different ecological regions in China during the East Asian Study of Tropospheric Aerosols—an International Regional Experiment (EAST-AIRE). The evaluation results show very large differences in the MODIS AOD retrieval between different ecosystems and geographic locations. The most agreement between the MODIS data and that of the CSHNET was in farmland sites in central-southern China, where high correlation ( R>0.82) and large percentages ( R2>72%) within the expected error lines issued by NASA were found. In temperate forest, coastal regions, and northeast and central farmlands, there appeared moderate agreement, with R∼0.64–0.80 and 45–73% of retrieval data falling within the expected errors. The poorest agreement existed in northern arid and semiarid regions, in remote northeast farmlands, in the Tibetan and Loess Plateau, and in southern forests, with 13–54% of retrieval data falling within the expected errors. In addition, the MODIS AOD retrievals were significantly overestimated in the northern arid and semiarid regions and underestimated in remote northeast farmlands and southern forests.