Moderate Resolution Imaging Spectroradiometer Aerosol Products Research Articles

The Aerosol Optical Depth Retrieval from Wide-Swath Imaging of DaQi-1 over Beijing

The Wide-Swath Imaging (WSI) sensor is a Chinese satellite launched in 2022, capable of providing data at resolutions ranging from 75 to 600 m for monitoring aerosols, fire points, and dust, among other uses. In this study, we developed a Dark Dense Vegetation method to retrieve the Aerosol Optical Depth (AOD) quickly from WSI 600 m data. First, after splitting into three types according to the Normalized Difference Vegetation Index (NDVI), we calculated the empirical parameters of land reflectance between the red (0.65 μm) and blue (0.47 μm) channels using Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance products over the Beijing area. Second, the decrease in the NDVI was simulated and analyzed under different AODs and solar zenith angles, and we introduced an iterative inversion approach to account for it. The simulation retrievals demonstrated that the iterative inversion produced accurate results after less than four iterations. Thirdly, we utilized the atmospherically corrected NDVI for dark target identification and output the AOD result. Finally, retrieval experiments were conducted using WSI 600 m data collected over Beijing in 2023. The retrieved AOD images highlighted two air pollution events occurring during 3–8 March and 27–31 October 2023. The inversion results in 2023 showed a strong correlation with Aerosol Robotic Network station data (the correlation coefficient was greater than 0.9). Our method exhibited greater accuracy than the MODIS aerosol product, though it was less accurate than the Multi-Angle Implementation of Atmospheric Correction product.

Assessment of severe aerosol events from NASA MODIS and VIIRS aerosol products for data assimilation and climate continuity

Abstract. While the use and data assimilation (DA) of operational Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace, MODIS is scheduled to sunset in the next year. For data continuity, focus has turned to the development of next-generation aerosol products and sensors such as those associated with the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and NOAA-20. Like MODIS algorithms, products from these sensors require their own set of extensive error characterization and correction exercises. This is particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithms. These MODIS “baseline algorithms” are compared to NASA's recently released NASA Deep Blue algorithm for use with VIIRS. Using this new dataset, the relative performance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical depth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.

A high-precision aerosol retrieval algorithm for FY-3D MERSI-II images

The Medium Resolution Spectral Imager-II (MERSI-II) onboard the recently launched Chinese Fengyun-3D (FY-3D) satellite has great capability in detecting global aerosols as it includes aerosol bands similar to Moderate Resolution Imaging Spectroradiometer (MODIS). However, to date, aerosol retrieval based on MERSI-II is still limited to dark target regions and there is no official aerosol products for the MERSI-II. This study focuses on developing a high-precision algorithm to retrieve aerosol optical depth (AOD) suitable for entire land areas (except snow/ice and inland waters) based on MERSI-II measurements. Considering both the accuracy and retrieval efficiency, a new cost function is constructed based on (1) the fact that the AOD (550 nm) retrieved independently from different bands should be the same with the correct aerosol model, and (2) the assumption that the aerosol types are the same in the 5 × 5 km spatial range. The retrieval method based on the new cost function is nearly 50 times faster than most current methods using iterative calculations. To extend the application adaption of the FY-3D MERSI-II AOD retrieval and avoid the errors caused by the surface Lambertian hypothesis, a bidirectional reflectance distribution function (BRDF) database is built using MODIS products. Eight candidate aerosol models in different natural zones of China are constructed based on AERONET aerosol products from 2010 − 2021. The new method is applied to MERSI-II images over China and validated against ground-based measurements at 14 stations from 2020 to 2021. MODIS aerosol products from three operational algorithms are also used for comparison purposes. The results show that MERSI-II AOD retrievals agree well with the ground-based measurements with correlation coefficient (R), root mean square error (RMSE), and relative mean bias (RMB) of 0.913, 0.123, and 0.955, respectively. In addition, 72.19 % of AOD matchups fall within the expected error (EE) envelopes. The MERSI-II retrievals show higher accuracy than that of MODIS dark target (DT) and deep blue (DB) products and comparable accuracy of the MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) product. MERSI-II AOD also shows higher stability in terms of spatial and temporal and better performance under heavy aerosol loading conditions than MODIS products. A good AOD agreement with R from 0.777 to 0.863 between MERSI-II and MODIS products is found over the land of China. The new method showing high retrieval efficiency and accuracy has great potential to be operationally applied on AOD retrieval for MERSI-II.

The development of Air Quality Indices using AOD-Retrieved Images during haze events in Peninsular Malaysia

Advances in satellite sensors provide new datasets to assist in the observation of air quality at urban and local scales. Quantitative Aerosol Optical Depth (AOD) and qualitative true colour image data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on the Terra satellite have been widely used to assess air quality. In this study, both 3 km and 10 km MODIS aerosol products were used to provide an overview of the state of fine particulate matter (PM2.5) and coarse particulate matter (PM10) during haze episodes over Peninsular Malaysia covering the period 21 to 26 June 2013. The result showed a high concentration of PM during the haze period based on ground-level data, with an average level of PM2.5 (mean ± standard deviation) of 99.86 ± 23.99, 190.37 ± 105.59, and 205.191 ± 165.76 (µg/m3) in Tanah Merah station, Cheras station, and Putrajaya station, respectively. Meanwhile, the PM10 in the Tanah Merah, Charas, and Putrajaya stations was 100.56 ± 28.17, 181.31 ± 83.70, and 197.99 ± 74.67, respectively. Overall, the results showed a correlation between monitoring station measurements and satellite data. Satellite remote sensing analysis of atmospheric aerosols remain a great method for estimating haze distribution and can be further used to monitor the atmospheric environment in Malaysia.

MODIS Collection 6.1 aerosol optical depth products over land and ocean: validation and comparison

Recently, the newest Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6.1 (C6.1) aerosol optical depth (AOD) products were available with various refinements and improvements made to both the radiation calibration and Dark Target (DT) and Deep Blue (DB) algorithms. A combined DT and DB dataset (DTB) was also added based on piecewise fixed thresholds using the Normalized Difference Vegetation Index (NDVI) for taking advantage of one's merits. This study provides a cross-comparison and evaluation of these Terra MODIS aerosol products with reference to the enhanced ground-based AOD measurements by the Aerosol Robotic Network (AERONET) Level 3 Version 2.0 data at 384 ground stations. Their absolute and relative performance are evaluated in the period of 2013–2017 among the products, as well as between the current (C6.1) and previous (C6) releases. In general, the C6.1 aerosol products are found to be superior over the C6 products for three datasets from all scales, but the differences and improvements are rather non-uniform that varies with region. Overall, the DB AOD products show the best performance in most regions at about half of the sites, especially in Europe and North America. Meanwhile, besides bright surfaces (i.e., deserts and arid/semi-arid areas), DB products match more closely with the AERONET AODs than that of DT over medium or densely vegetated areas. The dependences of retrieval errors illustrate that the performance of three datasets deteriorates as surface reflectance, elevation and aerosol loading increase. However, the DB algorithm remains relatively more stable and less affected by changes in atmospheric and surface conditions. While the merged product using NDVI has some improvements over individual ones in general, worse performance is also shown in many cases. A more optimal method is thus wanting.

Assessment of CALIOP and MODIS aerosol products over Iran to explore air quality

Monitoring air quality is crucial for Middle East countries such as Iran, where dust and polluted aerosol sources heavily influence local air quality. The use of active satellite remote sensing techniques is therefore considered in monitoring air quality. This study presents an initial assessment of NASA’s Cloud-Aerosol LIdar with Orthogonal Polarization (CALIOP) aerosol data over Tabriz and Mashhad cities in the north-western and north-eastern regions of Iran. We examined the Cloud and Aerosol Discrimination (CAD) score values, extinction coefficient, and the CALIOP Vertical Feature Mask (VFM) data product and Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue Aerosol Optical Depth (DBOD) at wavelength of 0.55 μm. The ground-based PM10 measurements were analyzed for different time periods, seasons, and years from 2005 to 2016. We investigated the profiles of the particle backscatter and extinction coefficient, as well as information about the determined feature types (e.g., clouds or aerosols) and aerosol subtypes (e.g., dust, and smoke) from the VFM data product in 2 months of August 2009 and July 2013, which were statistically selected from 2009 to 2016. Evaluation of the comparison of the relative humidity, temperature, and their inversion shows that the performance of the CALIOP in the detection of aerosols in mid-troposphere (around 5.0 km) is better than cloud detection. Additionally, the correlations of the PM10 concentration, MODIS AOD, and MODIS DBOD were investigated for January 2005 to December 2014. The overall analyses show that monthly ground-based PM10 concentration measurements reveal better correlation (r = 0.65 and 0.67 for Tabriz and Mashhad, respectively) with monthly MODIS-DBOD than MODIS-AOD for different seasons. The observed differences in the investigation of the CALIPSO dataset with the actual measured values and the overall correlation results show that the cloud and aerosol discrimination algorithm should be modified and calibrated based on local measurements of relative humidity, temperature, and their inversions, MODIS-DBOD, and ground-based PM10 for the Iran region.

A NEW ALGORITHM FOR THE AOD INVERSION FROM NOAA/AVHRR DATA

Abstract. The advanced very high resolution radiometer (AVHRR) data from the National Oceanic and Atmospheric Administration satellite is one of the earliest data applied in aerosol research. The dense dark vegetation (DDV) algorithm is a popular method for the present land aerosol retrieval. One of the most crucial steps in the DDV algorithm with AVHRR data is estimating the land surface reflectance (LSR). However, LSR cannot be easily estimated because of the lack of a 2.13 μm band. In this article, the moderate resolution imaging spectroradiometer (MODIS) vegetation index product (MYD13) is introduced to support the estimation of AVHRR LSR. The relationship between MODIS NDVI and the AVHRR LSR of the visible band is analysed to retrieve aerosol optical depth (AOD) from AVHRR data. Retrieval experiments are carried out in mid-eastern America. The AOD data from AErosol RObotic NETwork (AERONET) measurements are used to evaluate the aerosol retrieval from AVHRR data, the results indicate that about 74 % of the retrieved AOD are within the expected error range of ±(0.05 + 0.2), and a cross comparison of the AOD retrieval results with the MODIS aerosol product (MYD04) shows that the AOD datasets have a similar spatial distribution.

A Geostatistics-Based Method to Determine the Pixel Distance in a Structure Function Model for Aerosol Optical Depth Inversion

Inversion of aerosol optical depth (AOD) over bright land surface by optical remote sensing is particularly challenging because surface reflectance dominates the satellite signal. A structure function method is suitable and can effectively solve aerosol optical depth inversion in high reflectance areas. How to select d (the pixel distance between two pixels) value is one of the key problems with the structure function method. We present a method based on geostatistics, where variogram theory is referenced, to determine the pixel distance in structure function model for aerosol optical depth inversion. This method was validated by the Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km resolution level 1B data from Beijing, China. The results indicate that the relationship between variogram and d in four different directions can be fitted by exponential function, of which correlation coefficients are all above 0.9. Compared with the MODIS aerosol product (MOD 04 product), the inversion AOD has higher accuracy, with an absolute error of −0.00187 instead of −0.00854 and a relative error of 0.99% instead of 4.35%, based on AERosol RObotic NETwork (AERONET) observations. For validation, we applied this new method separately to both Beijing–Tianjin–Hebei region and MODIS 500 m resolution images for region and resolution validation. The results show that inversion AOD has higher accuracy and more efficient pixels.

Retrieval of Aerosol Optical Depth over Arid Areas from MODIS Data

Moderate Resolution Imaging Spectroradiometer (MODIS) data have been widely applied for the remote sensing of aerosol optical depth (AOD) because the MODIS sensor features a short revisit period and a moderate spatial resolution. The Dense Dark Vegetation (DDV) method is the most popular retrieval method. However, the DDV method can only be used to retrieve the AOD with high precision when the surface reflectance in the visible spectrum is low, such as over dense vegetation or water. To obtain precise AOD values in areas with higher reflectance, such as arid areas, Land Surface Reflectance (LSR) must be estimated accurately. This paper proposes a method of estimating LSR for AOD retrieval over arid areas from long-term series of MODIS images. According to the atmospheric parameters (AOD and water vapor), the clearest image without clouds was selected from the long-term series of continuous MODIS images. Atmospheric correction was conducted based on similar ground-measured atmospheric parameters and was used to estimate the LSR and retrieve the AOD at adjacent times. To validate this method, aerosol inversion experiments were performed in northern Xinjiang, in which the inverted AOD was compared to ground-measured AOD and MODIS aerosol products (MOD04). The AOD retrieved using the new algorithm was highly consistent with the AOD derived from ground-based measurements, with a correlation coefficient of 0.84. Additionally, 82.22% of the points fell within the expected error defined by NASA. The precision of the retrieved AOD data was better than that of MOD04 AOD products over arid areas.

Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

Implications of MODIS bow-tie distortion on aerosol optical depth retrievals, and techniques for mitigation

Abstract. The scan geometry of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensors, combined with the Earth's curvature, results in a pixel shape distortion known as the "bow-tie effect". Specifically, sensor pixels near the edge of the swath are elongated along-track and across-track compared to pixels near the centre of the swath, resulting in an increase of pixel area by up to a factor of ∼ 9 and, additionally, the overlap of pixels acquired from consecutive scans. The Deep Blue and Dark Target aerosol optical depth (AOD) retrieval algorithms aggregate sensor pixels and provide level 2 (L2) AOD at a nominal horizontal pixel size of 10 km, but the bow-tie distortion means that they also suffer from this size increase and overlap. This means that the spatial characteristics of the data vary as a function of satellite viewing zenith angle (VZA) and, for VZA > 30°, corresponding to approximately 50 % of the data, are areally enlarged by a factor of 50 % or more compared to this nominal pixel area and are not spatially independent of each other. This has implications for retrieval uncertainty and aggregated statistics, causing a narrowing of AOD distributions near the edge of the swath, as well as for data comparability from the application of similar algorithms to sensors without this level of bow-tie distortion. Additionally, the pixel overlap is not obvious to users of the L2 aerosol products because only pixel centres, not boundaries, are provided within the L2 products. A two-step procedure is proposed to mitigate the effects of this distortion on the MODIS aerosol products. The first (simple) step involves changing the order in which pixels are aggregated in L2 processing to reflect geographical location rather than scan order, which removes the bulk of the overlap between L2 pixels and slows the rate of growth of L2 pixel size vs. VZA. This can be achieved without significant changes to existing MODIS processing algorithms. The second step involves additionally changing the number of sensor pixels aggregated across-track as a function of VZA, which preserves L2 pixel size at around 10 km × 10 km across the whole swath but would require algorithmic quality assurance tests to be re-evaluated. Both of these steps also improve the extent to which the pixel locations a user would infer from the L2 data products represent the actual spatial extent of the L2 pixels.

China Collection 2.0: The aerosol optical depth dataset from the synergetic retrieval of aerosol properties algorithm

A wide range of data products have been published since the operation of the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor on NASA's TERRA and AQUA satellites. Based on DarkTarget and DeepBlue method, NASA has published Aerosol Optical Depth (AOD) products Collection 5.0 and Collection 5.1 at 10 km spatial resolution. The Collection 6.0 will be published soon with spatial resolution of 3 km. Although validated globally, regional and systematic errors are still found in the MODIS-retrieved AOD products. This is especially remarkable for bright heterogeneous land surface, such as mainland China. In order to solve the aerosol retrieval problem over heterogeneous bright land surface, the Synergetic Retrieval of Aerosol Properties algorithm (SRAP) has been developed based on the synergetic use of the MODIS data of TERRA and AQUA satellites. Using the SRAP algorithm, we produced AOD dataset-China Collection 2.0, dated from August 2002 to August 2012, and compared the AOD results with AErosol Robotic NETwork (AERONET) and Chinese Meteorological Administration Aerosol Remote Sensing Network (CARSNET) measurements. We find that 62% of China Collection 2.0 AOD values are within an expected error (EE) range of ±(0.05 + 20%) and that 56% are within an EE range of ±(0.05 + 15%) when compared with AERONET-observed values. For CARSNET validation, we find that 60% of China Collection 2.0 AOD values are within an expected error (EE) range of ±(0.05 + 20%) and that 53% are within an EE range of ±(0.05 + 15%). In addition, we also compare the AOD results with MODIS aerosol products, the cross validation shows that the two AOD have good consistency. Monthly averaged AOD results show that AOD is generally high in China's eastern coastal region from March to August, and AOD is not more than 0.5 in other months. Season averaged results show that the higher values of AOD are mostly distributed in eastern and southern China.

A new method for improving the retrieved aerosol fine-mode fraction from MODIS over ocean

A new method for improving the retrieved aerosol fine-mode fraction (550) based on the current Moderate Resolution Imaging Spectroradiometer (MODIS) ocean algorithm is proposed. In the current MODIS ocean algorithm, the top of the atmosphere (TOA) apparent reflectance needs calculation from lookup tables (LUTs). The weighting parameters used in the calculation show an obvious spectral dependence, which is not taken into account in the current algorithm. The main measure taken in this study is to consider the spectral dependence of the weighting parameters. The MODIS aerosol products and the Aerosol Robotic Network (AERONET) data of Hong Kong Hok Tsui, Midway Island, Martha’s Vineyard Coastal Observatory (MVCO) and COVE, Virginia, where aerosols exhibit different loading and size distribution, are used to test the new method. The results show that the new method improves the retrieved fine-mode fraction, which is underestimated in anthropogenic-dominated aerosol conditions and overestimated in the sea salt-dominated aerosol conditions by the current algorithm. The correlation of the retrieved fine-mode fraction between the new method and AERONET is much higher (correlation coefficient, r = 0.92) than that between the current MODIS and AERONET (r = 0.80). The retrieved aerosol optical depth (AOD) is also improved. More AODs retrieved from the new method lie within the expected error bars.

Utilization of satellite observation of ozone and aerosols in providing initial and boundary condition for regional air quality studies

[1] To demonstrate the efficacy of satellite observations in the realization of the background and transboundary transport of pollution in regional air quality modeling practices, satellite observations of ozone and aerosol optical depth were incorporated in the EPA Models-3 Community Multiscale Air Quality (CMAQ) model (http://www.cmascenter.org). Observations from Ozone Monitoring Instrument (OMI) aboard NASA's Aura satellite and AOD products from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra (EOS AM) and Aqua (EOS PM) satellites were used to specify initial and lateral boundary conditions (IC/BC) for a simulation that spanned over August 2006. The tools and techniques using the satellite data were tested in the context of current regulatory air quality modeling practices. Daily satellite observations were remapped onto the modeling domain and used as IC/BC for daily segments of a month-long simulation and the results were evaluated against surface and ozonesonde observations. Compared to the standard application of CMAQ, OMI O3 profiles significantly improved model performance in the free troposphere and MODIS aerosol products substantially improved PM2.5 predictions in the boundary layer. The utilization of satellite data for BC helped in the realization of transboundary transport of pollution and was able to explain the recirculation of pollution from Northeast Corridor to the southeastern region. Ozone in the mid- to upper-troposphere was largely dominated by transport and thus benefited most from satellite provided BC. The ozone within the boundary layer was mostly affected by fast production/loss mechanisms that are impacted by surface emissions, chemistry and removal processes and was not impacted as much. A case study for August 18–22 demonstrated that model errors in the placement of a stationary front were the main reason for errors in PM2.5 predictions as the front acted as a boundary between high and low PM2.5 concentrations.

A methodology to evaluate the aerosol effective radius based on MODIS aerosol products applicable to other satellite platforms

A strategy to evaluate the effective radius (r eff) as a function of aerosol retrievals is provided in this work. This methodology is based on the MODerate resolution Imaging Spectroradiometer (MODIS) aerosol products, using the 0.66 and 0.87 µm bands. These data have been studied from February 2000 to December 2005 in a grid situated at Subtropical North‐east Atlantic region. To reduce the number of MODIS useful variables a Factorial Analysis by Principal Components has been applied, decreasing the aerosol parameters from 18 to five. With these parameters, backscattering ratios and asymmetry factors at 0.66 and 0.87 µm besides the Ångström parameter, a lineal multivariate analysis technique has been applied to find the combination of variables that better evaluate the r eff. The standard error for the predicted value of r eff is ±0.09 µm. The expression obtained here has the advantage that it can be applied to other remote sensors like AVHRR/NOAA, HRV/SPOT, TM/LANDSAT, and so on, with long time series.

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.

Retrieval, validation, and application of the 1-km aerosol optical depth from MODIS measurements over Hong Kong

The Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol retrieval algorithm was developed to derive aerosol properties at a global scale, suitable for climate studies. Under favorable conditions (clear sky and over dark surfaces), the standard 10/spl times/10 km MODIS aerosol products are also useful on regional scales to monitor aerosol distributions and transports. However, the 10-km resolution is insufficient to depict aerosol variation on local or urban scales, due to inherent aerosol variability as well as complex surface terrain. In this study, we have modified the MODIS algorithm to retrieve aerosol optical depth (AOD) at 1-km resolution over Hong Kong, a city of just over 1000 km/sup 2/ with very complex surface features. Accompanied by the increased spatial resolution are new aerosol models derived with single-scattering albedo (SSA) around 0.91-0.94 to accommodate higher aerosol absorption encountered in Hong Kong than that was presumed for MODIS standard products (SSA/spl sim/0.97) over the region. The derived AOD data are compared to handheld Microtops II sunphotometer observations at the Hong Kong University of Science and Technology and other locations across Hong Kong. Retrieval errors within 15% to 20% of sunphotometer measurements are found. Moreover, when compared with the standard 10-km AOD products, the 1-km AOD data are much better correlated with PM/sub 10/ measurements across Hong Kong, suggesting that the new 1-km AOD data can be used to better characterize the particulate matter distribution for cities like Hong Kong than the MODIS standard products.

An analysis of potential cloud artifacts in MODIS over ocean aerosol optical thickness products

Using spatially and temporally collocated MODerate Resolution Imaging Spectroradiometer (MODIS) products and data from seven AErosol RObotic NETwork (AERONET) sun‐photometer sites, we explored the relationship between MODIS aerosol optical depth and cloud fraction over remote oceans that have been recently reported in the literature. We show that artifacts such as cloud contamination or adjacency effect contribute to the majority of the relationship in clean marine conditions. This cloud fraction effect could result in a 10–20% overestimation in monthly mean aerosol optical depth or aerosol direct forcing values that are derived using MODIS aerosol products over cloud free oceans. It may also explain some of the high optical depth values derived in the mid‐latitude southern oceans. We also suggest that covariances of meteorological phenomenon such as wind or humidity in cloudy regions while logical might only account for a minor portion of the ensemble relationship.

Comparison of MODIS and AERONET derived aerosol optical depth over the Ganga Basin, India

Abstract. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard EOS Terra measures global aerosol optical depth and optical properties since 2000. MODIS aerosol products are freely available and are being used for numerous studies. In this paper, we present a comparison of aerosol optical depth (AOD) retrieved from MODIS with Aerosol Robotic Network (AERONET) data for the year 2004 over Kanpur, an industrial city lying in the Ganga Basin in the northern part of India. AOD retrieved from MODIS (τaMODIS) at 0.55µm wavelength has been compared with the AERONET derived AOD (τaAERONET), within an optimum space-time window. Although the correlation between τaMODIS and τaAERONET during the post-monsoon and winter seasons (R2~0.71) is almost equal to that during the pre-monsoon and monsoon seasons (R2~0.72), MODIS is found to overestimate AOD during the pre-monsoon and monsoon period (characterized by severe dust loading) and underestimate during the post-monsoon and winter seasons. The absolute difference between τaMODIS and τaAERONET is found to be low (0.12±0.11) during the non-dust loading season and much higher (0.4±0.2) during dust-loading seasons. The absolute error in τaMODIS is found to be about ~25% of the absolute values of τaMODIS. Our comparison shows the importance of modifying the existing MODIS algorithm during the dust-loading seasons, especially in the Ganga Basin in northern part of India.

Two MODIS Aerosol Products over Ocean on the Terra and Aqua CERES SSF Datasets

Abstract Understanding the impact of aerosols on the earth’s radiation budget and the long-term climate record requires consistent measurements of aerosol properties and radiative fluxes. The Clouds and the Earth’s Radiant Energy System (CERES) Science Team combines satellite-based retrievals of aerosols, clouds, and radiative fluxes into Single Scanner Footprint (SSF) datasets from the Terra and Aqua satellites. Over ocean, two aerosol products are derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) using different sampling and aerosol algorithms. The primary, or M, product is taken from the standard multispectral aerosol product developed by the MODIS aerosol group while a simpler, secondary [Advanced Very High Resolution Radiometer (AVHRR) like], or A, product is derived by the CERES Science Team using a different cloud clearing method and a single-channel aerosol algorithm. Two aerosol optical depths (AOD), τA1 and τA2, are derived from MODIS bands 1 (0.644 μm) and 6 (1.632 μm) resembling the AVHRR/3 channels 1 and 3A, respectively. On Aqua the retrievals are made in band 7 (2.119 μm) because of poor quality data from band 6. The respective Ångström exponents can be derived from the values of τ. The A product serves as a backup for the M product. More importantly, the overlap of these aerosol products is essential for placing the 20+ year heritage AVHRR aerosol record in the context of more advanced aerosol sensors and algorithms such as that used for the M product. This study documents the M and A products, highlighting their CERES SSF specifics. Based on 2 weeks of global Terra data, coincident M and A AODs are found to be strongly correlated in both bands. However, both domains in which the M and A aerosols are available, and the respective τ/α statistics significantly differ because of discrepancies in sampling due to differences in cloud and sun-glint screening. In both aerosol products, correlation is observed between the retrieved aerosol parameters (τ/α) and ambient cloud amount, with the dependence in the M product being more pronounced than in the A product.