Multi-angle Imaging Spectroradiometer Aerosol Products Research Articles

Evaluation and comparison of MODIS and MISR aerosol products with ground-based monitoring stations in the Amazon Basin

This study evaluates the long-term accuracy of six daily aerosol optical depth (AOD) datasets derived from the Moderate Resolution Imaging Spectroradiometers (MODIS) Dark Target (DT) at 3 and 10 km resolution, Deep Blue (DB), combined DT and DB datasets (DTB) and MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms and the Multi-angle Imaging SpectroRadiometer (MISR) Version 23, by comparing them with ground-based AOD observations in 18 Aerosol Robotic Network (AERONET) sites over the Amazon basin between 2000 and 2022. The AOD retrieval accuracy was also assessed under varying seasons, aerosol loading, particle size, elevation and land cover type. Overall results showed that Terra and Aqua-MODIS DB and MISR algorithms perform better, with about 88% of AOD retrievals within the expected error range. However, MISR presented a systematic high negative bias. MODIS DT 3 km overestimated AOD values and indicated unsatisfactory retrieval accuracy in almost all scenarios. MODIS and MISR retrievals showed improved performance under low aerosol loading, whereas Aqua-MODIS MAIAC under intermediate conditions (AOD>0.2). MISR indicated a large dependence on a coarse mode-dominated environment with considerable negative bias. Terra-MODIS MAIAC retrievals exhibited a smaller dependence for aerosol particle size distribution, while Terra-MODIS DB performed better for fine mode and Aqua-MODIS DB for coarse dominance. MAIAC also performed well under polluted scenarios in the dry season in contrast to the majority of satellite-based AOD retrievals, which showed good agreement under clean background conditions in the wet season. Almost all satellite-based retrievals showed higher uncertainty under extremely high altitudes (>3000 m) followed by non-satisfactory correlation and significant biases. Additionally, almost all satellite-based AOD retrievals indicated poor performance as vegetation coverage increases. On the contrary, MISR showed higher accuracy under forest type. In general, MODIS and MISR retrieval accuracy demonstrated distinct levels of dependencies that require further improvement in the Amazon Basin.

Validation and Analysis of MISR and POLDER Aerosol Products over China

Multi-angle polarization measurement is an important technical means of satellite remote sensing applied to aerosol monitoring. By adding angle information and polarization measurements, aerosol optical and microphysical properties can be more comprehensively and accurately retrieved. The accuracy of aerosol retrieval can reflect the advantages and specific accuracy improvement of multi-angle polarization. In this study, the Multi-angle Imaging SpectroRadiometer (MISR) V23 aerosol products and the Polarization and Directionality of the Earth’s Reflectance (POLDER) GRASP “high-precision” archive were evaluated with the Aerosol Robotic Network (AERONET) observations over China. Validation of aerosol optical depth (AOD), absorbing aerosol optical depth (AAOD), and the Ångström exponent (AE) properties was conducted. Our results show that the AOD inversion accuracy of POLDER-3/GRASP is higher with the correlation coefficient (R) of 0.902, slope of 0.896, root mean square error (RMSE) of 0.264, mean absolute error (MAE) of 0.190, and about 40.71% of retrievals within the expected error (EE, ± 0.05+0.2×AODAERONET) lines. For AAOD, the performance of two products is poor, with better results for POLDER-3/GRASP data. POLDER-3/GRASP AE also has higher R of 0.661 compared with that of MISR AE (0.334). According to the validation results, spatiotemporal distribution, and comparison with other traditional scalar satellite data, the performance of multi-angle polarization observations is better and is suitable for the retrieval of aerosol properties.

Introducing the MISR level 2 near real-time aerosol product

Abstract. Atmospheric aerosols are an important element of Earth's climate system and have significant impacts on the environment and on human health. Global aerosol modeling has been increasingly used for operational forecasting and as support for decision making. For example, aerosol analyses and forecasts are routinely used to provide air quality information and alerts in both civilian and military applications. The growing demand for operational aerosol forecasting calls for additional observational data that can be assimilated into models to improve model accuracy and predictive skill. These factors have motivated the development, testing, and release of a new near real-time (NRT) level 2 (L2) aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument on NASA's Terra platform. The NRT product capitalizes on the unique attributes of the MISR aerosol retrieval approach and product contents, such as reliable aerosol optical depth as well as aerosol microphysical information. Several modifications are described that allow for rapid product generation within a 3 h window following acquisition of the satellite observations. Implications for the product quality and consistency are discussed and compared to the current operational L2 MISR aerosol product. Several ways of implementing additional use-specific retrieval screenings are also highlighted.

Introducing the 4.4 km spatial resolution Multi-Angle Imaging SpectroRadiometer (MISR) aerosol product

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) instrument has been operational on the National Aeronautics and Space Administration (NASA) Earth Observing System (EOS) Terra satellite since early 2000, creating an extensive data set of global Earth observations. Here we introduce the latest version of the MISR aerosol products. The level 2 (swath) product, which is reported on a 4.4 km spatial grid, is designated as version 23 (V23) and contains retrieved aerosol optical depth (AOD) and aerosol particle property information derived from MISR's multi-angle observations over both land and water. The changes from the previous version of the algorithm (V22) have significant impacts on the data product and its interpretation. The V23 data set is created from two separate retrieval algorithms that are applied over dark water and land surfaces, respectively. Besides increasing the horizontal resolution to 4.4 km compared with the coarser 17.6 m resolution in V22 and streamlining the format and content, the V23 product has added geolocation information, pixel-level uncertainty estimates, and improved cloud screening. MISR data can be obtained from the NASA Langley Research Center Atmospheric Science Data Center at https://eosweb.larc.nasa.gov/project/misr/misr_table (last access: 11 October 2019). The version number for the V23 level 2 aerosol product is F13_0023. The level 3 (gridded) aerosol product is still reported at 0.5∘×0.5∘ spatial resolution with results aggregated from the higher-resolution level 2 data. The format and content at level 3 have also been updated to reflect the changes made at level 2. The level 3 product associated with the V23 level 2 product version is designated as F15_0032. Both the level 2 and level 3 products are now provided in NetCDF format.

New approach to the retrieval of AOD and its uncertainty from MISR observations over dark water

Abstract. A new method for retrieving aerosol optical depth (AOD) and its uncertainty from Multi-angle Imaging SpectroRadiometer (MISR) observations over dark water is outlined. MISR's aerosol retrieval algorithm calculates cost functions between observed and pre-simulated radiances for a range of AODs (from 0.0 to 3.0) and a prescribed set of aerosol mixtures. The previous version 22 (V22) operational algorithm considered only the AOD that minimized the cost function for each aerosol mixture and then used a combination of these values to compute the final, “best estimate” AOD and associated uncertainty. The new approach considers the entire range of cost functions associated with each aerosol mixture. The uncertainty of the reported AOD depends on a combination of (a) the absolute values of the cost functions for each aerosol mixture, (b) the widths of the cost function distributions as a function of AOD, and (c) the spread of the cost function distributions among the ensemble of mixtures. A key benefit of the new approach is that, unlike the V22 algorithm, it does not rely on empirical thresholds imposed on the cost function to determine the success or failure of a particular mixture. Furthermore, a new aerosol retrieval confidence index (ARCI) is established that can be used to screen high-AOD retrieval blunders caused by cloud contamination or other factors. Requiring ARCI ≥0.15 as a condition for retrieval success is supported through statistical analysis and outperforms the thresholds used in the V22 algorithm. The described changes to the MISR dark water algorithm will become operational in the new MISR aerosol product (V23), planned for release in 2017.

Size-resolved particulate matter concentrations derived from 4.4 km-resolution size-fractionated Multi-angle Imaging SpectroRadiometer (MISR) aerosol optical depth over Southern California

There is significant public health interest in gaining a better understanding of the health effects associated with particulate matter (PM) of differing sizes and compositions. Unfortunately the PM monitoring data needed for such studies are limited. Satellite observations of aerosol optical depth (AOD) have been used to overcome this limitation, but existing techniques using AOD to estimate surface concentrations of PM have limited ability to differentiate particle size and type, and the spatial resolution is often too coarse to evaluate PM variability on local scales. Fortunately there has been a significant breakthrough in aerosol product from the Multi-angle Imaging SpectroRadiometer (MISR) instrument onboard the NASA Terra satellite. Recent computational advances have made it possible to derive a “local mode” MISR aerosol product at a spatial resolution of 4.4km that maintains the quality of the 17.6km resolution Version 22 operational aerosol product. MISR is uniquely capable of providing total AOD as well as AOD fractionated by size (small, medium, and large), enabling estimation of size-resolved surface PM concentrations.Total and size-fractionated AOD from the MISR 4.4km aerosol product were used to develop spatio-temporal models to estimate surface concentrations of PM2.5, PM10, and PM2.5 chemical species over Southern California. We found, and confirmed by leave-one-site-out cross validation, that PM2.5 was best estimated with a spatio-temporal model of AOD small+medium that also included adjustment for relative humidity and wind speed (R2=0.67, CV R2=0.51), while PM10 was best estimated from AOD large with adjustment for dew point and wind speed (R2=0.76, CV R2=0.44). Total AOD was most strongly associated with PM2.5 components SO42− (R2=0.74, CV R2=0.69) and NO3− (R2=0.72, CV R2=0.39). The best fitting models were applied to all available MISR aerosol retrievals over the region, generating surfaces of estimated size-resolved PM concentrations that will be a great asset to the environmental science and public health communities.

Evaluation of a MISR-Based High-Resolution Aerosol Retrieval Method Using AERONET DRAGON Campaign Data

Satellite-retrieved aerosol optical depth (AOD) can potentially provide an effective way to complement the spatial coverage limitation of a ground particulate air-pollution monitoring network such as the U.S. Environment Protection Agency's regulatory monitoring network. One of the current state-of-the-art AOD retrieval methods is the National Aeronautics and Space Administration's Multiangle Imaging SpectroRadiometer (MISR) operational algorithm, which has a spatial resolution of 17.6 km $\times$ 17.6 km. Although the MISR's aerosol products lead to exciting research opportunities to study particle composition at a regional scale, its spatial resolution is too coarse for analyzing urban areas, where the air pollution has stronger spatial variations and can severely impact public health and the environment. Accordingly, a novel AOD retrieval algorithm with a resolution of 4.4 km $\times$ 4.4 km has been recently developed, which is based on hierarchical Bayesian modeling and the Monte Carlo Markov chain (MCMC) inference method. In this paper, we carry out detailed quantitative and qualitative evaluations of the new algorithm, which is called the HB-MCMC algorithm, using recent AErosol RObotic NETwork (AERONET) Distributed Regional Aerosol Gridded Observation Networks (DRAGON) campaign data obtained in the summer of 2011. These data, which were not available in a previous study, contain spatially dense ground measurements of the AOD and other aerosol particle characteristics from the Baltimore–Washington, DC region. Our results show that the HB-MCMC algorithm has 16.2% more AOD retrieval coverage and improves the root-mean-square error by 38.3% compared with the MISR operational algorithm. Our detailed analyses with various metrics show that the improvement of our scheme is coming from the novel modeling and inference method. Furthermore, the map overlay of the retrieval results qualitatively confirms the findings of the quantitative analyses.

Improving satellite-retrieved aerosol microphysical properties using GOCART data

Abstract. The Multi-angle Imaging SpectroRadiometer (MISR) aboard the NASA Earth Observing System's Terra satellite can provide more reliable aerosol optical depth (AOD) and better constraints on particle size (Ångström exponent, or ANG), sphericity, and single-scattering albedo (SSA) than many other satellite instruments. However, many aerosol mixtures pass the algorithm acceptance criteria, yielding a poor constraint, when the particle-type information in the MISR radiances is low, typically at low AOD. We investigate adding value to the MISR aerosol product under these conditions by filtering the list of MISR-retrieved mixtures based on agreement between the mixture ANG and absorbing AOD (AAOD) values, and simulated aerosol properties from the Goddard Chemistry Aerosol Radiation and Transport (GOCART) model. MISR–GOCART ANG difference and AAOD ratio thresholds for applying GOCART constraints were determined based on coincident AOD, ANG, and AAOD measurements from the AErosol RObotic NETwork (AERONET). The results were validated by comparing the adjusted MISR aerosol optical properties over the contiguous USA between 2006 and 2009 with additional AERONET data. The correlation coefficient (r) between the adjusted MISR ANG derived from this study and AERONET improves to 0.45, compared to 0.29 for the MISR Version 22 standard product. The ratio of the adjusted MISR AAOD to AERONET increases to 0.74, compared to 0.5 for the MISR operational retrieval. These improvements occur primarily when AOD < 0.2 for ANG and AOD < 0.5 for AAOD. Spatial and temporal differences among the aerosol optical properties of MISR V22, GOCART, and the adjusted MISR are traced to (1) GOCART underestimation of AOD and ANG in polluted regions; (2) aerosol mixtures lacking in the MISR Version 22 algorithm climatology; (3) low MISR sensitivity to particle type under some conditions; and (4) parameters and thresholds used in our method.

Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products

Abstract. Using Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based cloud screening methods, the impacts of cloud contamination on the Terra Multi-angle Imaging Spectroradiometer (MISR) aerosol optical depth (AOD) product are evaluated. Based on one year of collocated MISR and MODIS data, this study suggests that cloud contamination exists in both over-water and over-land MISR AOD data, with heavier cloud contamination occurring over the high latitude southern hemispheric oceans. On average globally, this study shows that thin cirrus cloud contamination introduces a possible ~ 0.01 high bias for the over-water MISR AOD retrievals. Over the mid- to high-latitude oceans and Southeast Asia, this number increases to 0.015–0.02. However, biases much larger than this mean value are found in individual retrievals, especially in retrievals that are near cloud edges. This study suggests that cloud-clearing methods using observations from MISR alone, which has only visible and near-infrared channels, may not be sufficient for all scenarios. Measurements from MODIS can be applied to assist cloud-clearing of the MISR aerosol retrievals. Cloud screening algorithms based on multi-sensor approaches are feasible and should be considered for current and future satellite aerosol studies.

MISR Dark Water aerosol retrievals: operational algorithm sensitivity to particle non-sphericity

Abstract. The aim of this study is to theoretically investigate the sensitivity of the Multi-angle Imaging SpectroRadiometer (MISR) operational (version 22) Dark Water retrieval algorithm to aerosol non-sphericity over the global oceans under actual observing conditions, accounting for current algorithm assumptions. Non-spherical (dust) aerosol models, which were introduced in version 16 of the MISR aerosol product, improved the quality and coverage of retrievals in dusty regions. Due to the sensitivity of the retrieval to the presence of non-spherical aerosols, the MISR aerosol product has been successfully used to track the location and evolution of mineral dust plumes from the Sahara across the Atlantic, for example. However, the MISR global non-spherical aerosol optical depth (AOD) fraction product has been found to have several climatological artifacts superimposed on valid detections of mineral dust, including high non-spherical fraction in the Southern Ocean and seasonally variable bands of high non-sphericity. In this paper we introduce a formal approach to examine the ability of the operational MISR Dark Water algorithm to distinguish among various spherical and non-spherical particles as a function of the variable MISR viewing geometry. We demonstrate the following under the criteria currently implemented: (1) Dark Water retrieval sensitivity to particle non-sphericity decreases for AOD below about 0.1 primarily due to an unnecessarily large lower bound imposed on the uncertainty in MISR observations at low light levels, and improves when this lower bound is removed; (2) Dark Water retrievals are able to distinguish between the spherical and non-spherical particles currently used for all MISR viewing geometries when the AOD exceeds 0.1; (3) the sensitivity of the MISR retrievals to aerosol non-sphericity varies in a complex way that depends on the sampling of the scattering phase function and the contribution from multiple scattering; and (4) non-sphericity artifacts occur at those view-illumination geometries where dust aerosols are indistinguishable from certain types of cirrus particles. Based on these results, we suggest that interested parties use caution with the version 22 MISR Dark Water aerosol non-sphericity product in situations where cirrus may be present.

Validation of multi-angle imaging spectroradiometer aerosol products in China

Validation of multi-angle imaging spectroradiometer aerosol products in China

Validation of multi-angle imaging spectroradiometer aerosol products in China

Based on AErosol RObotic NETwork and Chinese Sun Hazemeter Network data, the Multi-angle Imaging SpectroRadiometer (MISR) level 2 aerosol optical depth (AOD) products are evaluated in China. The MISR retrievals depict well the temporal aerosol trend in China with correlation coefficients exceeding 0.8 except for stations located in northeast China and at the Lanzhou site. In general, the MISR AOD retrievals agree well with ground-based observations for AOD 0.5. The retrievals are systematically underestimated for AOD > 0.5 in the east, southwest and northeast regions of China. Concerning surface types, the greatest underestimations occur in farmland and forest ecosystems. The largest and smallest biases are seen in spring and in summer, respectively. The systematic underestimation seems to stem from the use of too high single scattering albedos ∼0.96 which is significantly higher than those estimated from ground-based observations. Further improvements to the MISR aerosol algorithm, especially in the aerosol model, are recommended.

Multisensor Data Product Fusion for Aerosol Research

Combining data sets from multiple satellite sensors is a powerful method for studying Earth-atmosphere problems. By fusing data, we can utilize the strengths of the individual sensors that may not be otherwise possible. In this paper, we provide the framework for combining level 2 data products, using data from three sensors aboard the National Aeronautics and Space Administration (NASA)'s Terra satellite. These data include top-of-the-atmosphere (TOA) radiative energy fluxes obtained from the Clouds and the Earth's Radiant Energy System (CERES), aerosol optical thickness from the multispectral Moderate Resolution Imaging Spectroradiometer (MODIS), and aerosol properties from the Multi-angle Imaging SpectroRadiometer (MISR). The CERES Single Scanner Footprint (SSF) contains the pixel level CERES TOA fluxes and the level 2 MODIS aerosol data. We specifically focus upon fusing the CERES SSF with the MISR aerosol products. Although this project was undertaken specifically to address aerosol research, the methods employed for fusing data products can be used for other problems requiring synergistic data sets. We present selected case studies over different aerosol regimes and indicate that multisensor information provides value-added information for aerosol research that is not available from a single sensor.

Using aerosol optical thickness to predict ground-level PM 2.5 concentrations in the St. Louis area: A comparison between MISR and MODIS

Using two general linear regression models, we compared the ability of the aerosol optical thickness (AOT) retrieved by the Multiangle Imaging SpectroRadiometer (MISR) and the Moderate Resolution Imaging Spectroradiometer (MODIS) to predict ground-level PM 2.5 concentrations in St. Louis, MO and its surrounding areas . The models included meteorological parameters obtained from the National Oceanic and Atmospheric Administration (NOAA)'s Rapid Update Cycle (RUC20) model as covariates. Both MISR and MODIS AOT values were highly significant predictors of PM 2.5 concentrations. The MISR and MODIS models have overall comparable predictability of ground-level PM 2.5 concentrations. The MISR model explained a slightly greater percentage (62%) of the variability in PM 2.5 concentrations than the MODIS model (51%), and thus was a better fit. Over the entire data range, the MISR model underpredicts PM 2.5 concentrations by approximately 12%, whereas the MODIS model underpredicts PM 2.5 concentrations by approximately 18%. This underestimation occurred primarily at higher PM 2.5 concentrations in both models. The regression coefficients from two models were highly comparable, suggesting that combining MISR and MODIS AOT data might benefit from the higher predicting accuracy of MISR and the better spatial coverage of MODIS. The newly developed particle size/shape indicators in MISR and MODIS aerosol product did not significantly improve our ability to predict PM 2.5 concentrations using AOT measurements. Finally, using hourly PM 2.5 concentrations did not seem to improve its association with AOT for the current study region.

Analysis of Multi‐angle Imaging SpectroRadiometer (MISR) aerosol optical depths over greater India during winter 2001–2004

We present the first detailed spatial analysis of a four‐year, wintertime visible aerosol optical depth (AOD) climatology from the Multi‐angle Imaging SpectroRadiometer (MISR) over greater India. Meteorological fields from the National Centers for Environmental Prediction (NCEP) reanalysis, topographic data, and information related to aerosol source regions are used to explain the spatial patterns in MISR AODs. High AODs are found over much of greater India. The highest AODs are over the northern Indian state of Bihar, where we show that meteorology, topography, and aerosol sources all favor development of a concentrated pool of airborne particles. MISR AODs are validated against five ground‐based sites in India and Nepal, revealing similar error characteristics found in other validation studies for the MISR aerosol product.