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NASA NTRS2008

The Time Series Technique for Aerosol Retrievals over Land from MODIS: Algorithm MAIAC

Atmospheric aerosols interact with sun light by scattering and absorbing radiation. By changing irradiance of the Earth surface, modifying cloud fractional cover and microphysical properties and a number of other mechanisms, they affect the energy balance, hydrological cycle, and planetary climate [IPCC, 2007]. In many world regions there is a growing impact of aerosols on air quality and human health. The Earth Observing System [NASA, 1999] initiated high quality global Earth observations and operational aerosol retrievals over land. With the wide swath (2300 km) of MODIS instrument, the MODIS Dark Target algorithm [Kaufman et al., 1997; Remer et al., 2005; Levy et al., 2007] currently complemented with the Deep Blue method [Hsu et al., 2004] provides daily global view of planetary atmospheric aerosol. The MISR algorithm [Martonchik et al., 1998; Diner et al., 2005] makes high quality aerosol retrievals in 300 km swaths covering the globe in 8 days. With MODIS aerosol program being very successful, there are still several unresolved issues in the retrieval algorithms. The current processing is pixel-based and relies on a single-orbit data. Such an approach produces a single measurement for every pixel characterized by two main unknowns, aerosol optical thickness (AOT) and surface reflectance (SR). This lack of information constitutes a fundamental problem of the remote sensing which cannot be resolved without a priori information. For example, MODIS Dark Target algorithm makes spectral assumptions about surface reflectance, whereas the Deep Blue method uses ancillary global database of surface reflectance composed from minimal monthly measurements with Rayleigh correction. Both algorithms use Lambertian surface model. The surface-related assumptions in the aerosol retrievals may affect subsequent atmospheric correction in unintended way. For example, the Dark Target algorithm uses an empirical relationship to predict SR in the Blue (B3) and Red (B1) bands from the 2.1 m channel (B7) for the purpose of aerosol retrieval. Obviously, the subsequent atmospheric correction will produce the same SR in the red and blue bands as predicted, i.e. an empirical function of 2.1. In other words, the spectral, spatial and temporal variability of surface reflectance in the Blue and Red bands appears borrowed from band B7. This may have certain implications for the vegetation and global carbon analysis because the chlorophyll-sensing bands B1, B3 are effectively substituted in terms of variability by band B7, which is sensitive to the plant liquid water. This chapter describes a new recently developed generic aerosol-surface retrieval algorithm for MODIS. The Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm simultaneously retrieves AOT and surface bi-directional reflection factor (BRF) using the time series of MODIS measurements.

Lyapustin, Alexei↗

NASA NTRS2011

Comparative Analysis of Aerosol Retrievals from MODIS, OMI and MISR Over Sahara Region

MODIS is a wide field-of-view sensor providing daily global observations of the Earth. Currently, global MODIS aerosol retrievals over land are performed with the main Dark Target algorithm complimented with the Deep Blue (DB) Algorithm over bright deserts. The Dark Target algorithm relies on surface parameterization which relates reflectance in MODIS visible bands with the 2.1 micrometer region, whereas the Deep Blue algorithm uses an ancillary angular distribution model of surface reflectance developed from the time series of clear-sky MODIS observations. Recently, a new Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm has been developed for MODIS. MAIAC uses a time series and an image based processing to perform simultaneous retrievals of aerosol properties and surface bidirectional reflectance. It is a generic algorithm which works over both dark vegetative surfaces and bright deserts and performs retrievals at 1 km resolution. In this work, we will provide a comparative analysis of DB, MAIAC, MISR and OMI aerosol products over bright deserts of northern Africa.

Lyapustin, A.↗

NASA NTRS2019

Exploring Aerosols Near Clouds With High‐Spatial‐Resolution Aircraft Remote Sensing During SEAC(4)RS

Since aerosols are important to our climate system, we seek to observe the variability of aerosol properties within cloud systems. When applied to the satellite‐borne Moderate‐resolution Imaging Spectroradiometer (MODIS), the Dark Target retrieval algorithm provides global aerosol optical depth (AOD; at 0.55 μm) in cloud‐free scenes. Since MODIS' resolution (500‐m pixels, 3‐ or 10‐km product) is too coarse for studying near‐cloud aerosol, we ported the Dark Target algorithm to the high‐resolution (~50‐m pixels) enhanced‐MODIS Airborne Simulator (eMAS), which flew on the high‐altitude ER‐2 during the Studies of Emissions, Atmospheric Composition, Clouds, and Climate Coupling by Regional Surveys Airborne Science Campaign over the United States in 2013. We find that even with aggressive cloud screening, the ~0.5‐km eMAS retrievals show enhanced AOD, especially within 6 km of a detected cloud. To determine the cause of the enhanced AOD, we analyze additional eMAS products (cloud retrievals and degraded‐resolution AOD), coregistered Cloud Physics Lidar profiles, MODIS aerosol retrievals, and ground‐based Aerosol Robotic Network observations. We also define spatial metrics to indicate local cloud distributions near each retrieval and then separate into near‐cloud and far‐from‐cloud environments. The comparisons show that low cloud masking is robust, and unscreened thin cirrus would have only a small impact on retrieved AOD. Some of the enhancement is consistent with clear‐cloud transition zone microphysics such as aerosol swelling. However, 3‐D radiation interaction between clouds and the surrounding clear air appears to be the primary cause of the high AOD near clouds.

aerosol retrieval algorithm↗

NASA NTRS2013

MODIS 3km Aerosol Product: Algorithm and Global Perspective

After more than a decade of producing a nominal 10 km aerosol product based on the dark target method, the MODIS aerosol team will be releasing a nominal 3 km product as part of their Collection 6 release. The new product differs from the original 10 km product only in the manner in which reflectance pixels are ingested, organized and selected by the aerosol algorithm. Overall, the 3 km product closely mirrors the 10 km product. However, the finer resolution product is able to retrieve over ocean closer to islands and coastlines, and is better able to resolve fine aerosol features such as smoke plumes over both ocean and land. In some situations, it provides retrievals over entire regions that the 10 km product barely samples. In situations traditionally difficult for the dark target algorithm, such as over bright or urban surfaces the 3 km product introduces isolated spikes of artificially high aerosol optical depth (AOD) that the 10 km algorithm avoids. Over land, globally, the 3 km product appears to be 0.01 to 0.02 higher than the 10 km product, while over ocean, the 3 km algorithm is retrieving a proportionally greater number of very low aerosol loading situations. Based on collocations with ground-based observations for only six months, expected errors associated with the 3 km land product are determined to be greater than for the 10 km product: 0.05 0.25 AOD. Over ocean, the suggestion is for expected errors to be the same as the 10 km product: 0.03 0.05 AOD. The advantage of the product is on the local scale, which will require continued evaluation not addressed here. Nevertheless, the new 3 km product is expected to provide important information complementary to existing satellite-derived products and become an important tool for the aerosol community.

Remer, L. A.↗

NASA NTRSDate not supplied

Direct Aerosol Radiative Effect Derived From CERES Data Products

The CERES team uses MODIS and VIIRS derived aerosol optical thickness to compute surface and in atmosphere irradiances. In producing the data products, aerosol optical thicknesses derived by Dark Target and Deep Blue algorithms are assimilated in an aerosol transport model. In addition, the aerosol transport model provides aerosol optical thicknesses under cloudy conditions and aerosol optical thicknesses over polar regions. These aerosol optical thicknesses are used for hourly irradiance computations in 11 grids. Global mean top-of-atmosphere direct aerosol radiative effects are -5.2 Wm-2 and -2.2 Wm-2 for, respectively, clear-sky and all-sky conditions. Spatial pattern of the trend of the computed clear-sky direct aerosol radiative effect agrees with that derived from CERES observations. In this presentation, the importance of seamless transition of aerosol optical thicknesses derived from MODIS to those derived from VIIRS is emphasized. Currently, the CERES team is working with MODIS dark target and deep blue teams to achieve the seamless transition in producing CERES Edition 4 data products.

Seiji Kato↗

NASA NTRSDate not supplied

Open Source Application of Fusing Aerosol Products from GEO and LEO Satellites

Retrieving aerosol optical depths (AODs) from sun-synchronous polar orbiting (aka low earth orbit, LEO) satellites, such as MODISs, and VIIRSs, OMI, TROPOMI, etc, has become well-established as a tool for extracting information on particulate matter (PM) and related processes in the atmosphere. However, with recently launched geostationary satellites (GEO), such as GOES-16/17/18, and Himawari-8/9, and Meteosat Third Generation (MTG) they provide a much higher temporal resolution (order of 10 minutes), typically an image once or more per hour during daylight compared to LEO once per day. By combining these observations, we may be able to characterize the diurnal cycle of global AOD at the local, regional and global scale. While the science community is still exploring the new data from GEO observations, we have been thinking about how to properly combine/merge/fuse those data considering differences in their spatial and temporal resolutions. However, this poses a “Big Data” challenge. The big data challenge is not just about data storage, but also about data discoverability, and accessibility, and even more, about data migration/mirroring in the cloud-computing environment. This paper is merely showing some of the efforts and approaches we have attempted in fusing six satellites’ Level 2 aerosol data (three are from GEO (GOES-16/17 and Himawari-8), and the other three are from LEO (TERRA/MODIS, AQUA/MODIS, SNPP-VIIRS) from Dark Target (DT) aerosol retrieval algorithm. Having the on-demand capability of fusing remote sensing products onto the desired temporal and spatial domain enables researchers and application practitioners to better manipulate and work with satellite and sensor data. It is our hopeWe hope that by making such an open-source package, and the accompanying functionality, the scientific community will be granted easier access to aerosol data processing resources. The MEaSUREs Program (Making Earth System Data Records for Use in Research Environments) expands our understanding of the Earth's current system through atmospheric and surface measurements. In an effort to aid the scientific research component and improve open source methods, this project developed Python code for fusing six satellite Level 2 aerosol data (three are from geostationary satellites (GEO), and the other three are from low earth orbital satellites (LEO)) from Dark Target Aerosol Retrieval Algorithm.

Jennifer Wei↗

NASA NTRS2016

An Evaluation of MODIS-Retrieved Aerosol Optical Depth over a Mountainous AERONET Site in the Southeastern US

The literature shows that aerosol optical depth (AOD) derived from the MODIS Collection 5 (C5) dark target algorithm has been extensively validated by spatiotemporal collocation with AERONET sites on both global and regional scales.Although generally comparing well over the eastern US region, poor performance over mountains in other regions indicate the need to evaluate the MODIS product over a mountain site. This study compares MODIS C5 AOD at 550nm to AOD measured at the Appalachian State University AERONET site in Boone, NC over 30 months between August 2010 and September 2013. For the combined Aqua and Terra datasets, although more than 70% of the 500 MODIS AOD measurements agree with collocated AERONET AOD to within error envelope of +/- (0.05 + 15%), MODIS tends to have a low bias (0.02-0.03). The agreement between MODIS and AERONET AOD does not depend on MODIS quality assurance confidence (QAC) value. However, when stratified by satellite, MODIS-Terra data does not perform as well as Aqua, with especially poor correlation (r = 0.39) for low aerosol loading conditions (AERONET AOD less than 0.15).Linear regressions between Terra and AERONET possess statistically-different slopes for AOD < 0.15 and AOD > or = 0.15. AERONET AOD measured only during MODIS overpass hours is highly correlated with daily-averaged AERONET AOD. MODIS monthly-averaged AOD also tracks that of AERONET over the study period. These results indicate that MODIS is sensitive to the day-to-day variability, as well as the annual cycle of AOD over the Appalachian State AERONET site. The complex topography and high seasonality in AOD and vegetation indices allow us to specifically evaluate MODIS dark target algorithm surface albedo and aerosol model assumptions at a regionally-representative SE US mountain site.

air quality↗

NASA NTRS2012

Near-Infrared Photon-Counting Camera for High-Sensitivity Observations

The dark current of a transferred-electron photocathode with an InGaAs absorber, responsive over the 0.9-to-1.7- micron range, must be reduced to an ultralow level suitable for low signal spectral astrophysical measurements by lowering the temperature of the sensor incorporating the cathode. However, photocathode quantum efficiency (QE) is known to reduce to zero at such low temperatures. Moreover, it has not been demonstrated that the target dark current can be reached at any temperature using existing photocathodes. Changes in the transferred-electron photocathode epistructure (with an In- GaAs absorber lattice-matched to InP and exhibiting responsivity over the 0.9- to-1.7- m range) and fabrication processes were developed and implemented that resulted in a demonstrated >13x reduction in dark current at -40 C while retaining >95% of the approximately equal to 25% saturated room-temperature QE. Further testing at lower temperature is needed to confirm a >25 C predicted reduction in cooling required to achieve an ultralow dark-current target suitable for faint spectral astronomical observations that are not otherwise possible. This reduction in dark current makes it possible to increase the integration time of the imaging sensor, thus enabling a much higher near-infrared (NIR) sensitivity than is possible with current technology. As a result, extremely faint phenomena and NIR signals emitted from distant celestial objects can be now observed and imaged (such as the dynamics of redshifting galaxies, and spectral measurements on extra-solar planets in search of water and bio-markers) that were not previously possible. In addition, the enhanced NIR sensitivity also directly benefits other NIR imaging applications, including drug and bomb detection, stand-off detection of improvised explosive devices (IED's), Raman spectroscopy and microscopy for life/physical science applications, and semiconductor product defect detection.

Jurkovic, Michael↗

NASA NTRS2000

Sensitivity of MODIS 2.1 micron Channel for Off-Nadir View Angles for Use in Remote Sensing of Aerosol

Remote sensing of aerosol over land, from MODIS will be based on dark targets using mid-IR channels 2.1 and 3.9 micron. This approach was developed by Kaufman et al (1997), who suggested that dark surface reflectance in the red (0.66 micron -- rho(sub 0.66)) channel is half of that at 2.2 micron (rho(sub 2.2)), and the reflectance in the blue (0.49 micron - rho(sub 0.49)) channel is a quarter of that at 2.2 micron. Using this relationship, the surface reflectance in the visible channels can be predicted within Delta.rho(sub 0.49) approximately Delat.rho(sub 0.66) approximately 0.006 from rho(sub 2.2) for rho(sub 2.2) <= 0.10. This was half the error obtained using the 3.75 micron and corresponds to an error in aerosol optical thickness of Delat.tau approximately 0.06. These results, though applicable to several biomes (e.g. forests, and brighter lower canopies), have only been tested at one view angle - the nadir (theta = 0 deg). Considering the importance of the results in remote sensing of aerosols over land surfaces from space, we are validating the relationships for off-nadir view angles using Cloud Absorption Radiometer (CAR) data. The CAR data are available for channels between 0.3 and 2.3 micron and for different surface types and conditions: forest, tundra, ocean, sea-ice, swamp, grassland and over areas covered with smoke. In this study we analyzed data collected during the Smoke, Clouds, and Radiation - Brazil (SCAR-B) experiment to validate Kaufman et al.'s (1997) results for non-nadir view angles. We will show the correlation between rho(sub 0.472), rho(sub 0.675), and rho(sub 2.2) for view angles between nadir (0 deg) and 55 deg off-nadir, and for different viewing directions in the backscatter and forward scatter directions.

Gatebe, C. K.↗

NASA NTRS2016

Retrieval of Aerosol Optical Properties Using MERIS Observations: Algorithm and Some First Results

The MEdium Resolution Imaging Spectrometer (MERIS) instrument on board ESA Envisat made measurements from 2002 to 2012. Although MERIS was limited in spectral coverage, accurate Aerosol Optical Thickness (AOT) from MERIS data are retrieved by using appropriate additional information. We introduce a new AOT retrieval algorithm for MERIS over land surfaces, referred to as eXtensible Bremen AErosol Retrieval (XBAER). XBAER is similar to the dark-target (DT) retrieval algorithm used for Moderate-resolution Imaging Spectroradiometer (MODIS), in that it uses a lookup table (LUT) to match to satellite-observed reflectance and derive the AOT. Instead of a global parameterization of surface spectral reflectance, XBAER uses a set of spectral coefficients to prescribe surface properties. In this manner, XBAER is not limited to dark surfaces (vegetation) and retrieves AOT over bright surface (desert, semiarid, and urban areas). Preliminary validation of the MERIS-derived AOT and the ground-based Aerosol Robotic Network (AERONET) measurements yield good agreement, the resulting regression equation is y (0.92 x +/- 0.07) + (0.05 +/- 0.01) and Pearson correlation coefficient of R 0.78. Global monthly means of AOT have been compared from XBAER, MODIS and other satellite-derived datasets.

satellite retrievals↗

NASA NTRS2011

Multiangle Implementation of Atmospheric Correction (MAIAC): 2. Aerosol Algorithm

An aerosol component of a new multiangle implementation of atmospheric correction (MAIAC) algorithm is presented. MAIAC is a generic algorithm developed for the Moderate Resolution Imaging Spectroradiometer (MODIS), which performs aerosol retrievals and atmospheric correction over both dark vegetated surfaces and bright deserts based on a time series analysis and image-based processing. The MAIAC look-up tables explicitly include surface bidirectional reflectance. The aerosol algorithm derives the spectral regression coefficient (SRC) relating surface bidirectional reflectance in the blue (0.47 micron) and shortwave infrared (2.1 micron) bands; this quantity is prescribed in the MODIS operational Dark Target algorithm based on a parameterized formula. The MAIAC aerosol products include aerosol optical thickness and a fine-mode fraction at resolution of 1 km. This high resolution, required in many applications such as air quality, brings new information about aerosol sources and, potentially, their strength. AERONET validation shows that the MAIAC and MOD04 algorithms have similar accuracy over dark and vegetated surfaces and that MAIAC generally improves accuracy over brighter surfaces due to the SRC retrieval and explicit bidirectional reflectance factor characterization, as demonstrated for several U.S. West Coast AERONET sites. Due to its generic nature and developed angular correction, MAIAC performs aerosol retrievals over bright deserts, as demonstrated for the Solar Village Aerosol Robotic Network (AERONET) site in Saudi Arabia.

Lyapustin, A.↗

NASA NTRS2021

Polarimetric calibration of the Multi-Angle Imager for Aerosols (MAIA)

NASA’s Multi-Angle Imager for Aerosols (MAIA) mission, under development at the Jet Propulsion Laboratory, is designed to study the adverse health effects of different types of particulate air pollution. Planned for launch in 2023 for a 3-year mission, the MAIA satellite instrument will focus on a selected set of metropolitan target areas, where air quality monitors and health data are available. Aerosol concentration and speciation are inferred from multi-angle measurements of backscattered sunlight in 14 spectral bands from 350-2200 nm, with bands near 442, 645 and 1040 nm measuring the degree (DoLP) and angle of linear polarization (AoLP) in addition to radiance. The pushbroom camera has a ~240-km cross-track field of view with a nadir resolution of ~200 m, and is mounted onto a biaxial gimbal to provide along-track view angles within ±60°, to extend the field of regard to ±48°, and to view the instrument’s onboard calibrator (OBC) and dark target. The OBC consists of a sunlit transmissive diffuser, followed by 12 polarizers at different orientations. MAIA’s polarimetry is implemented using miniature wire grid polarizers on the focal plane array, and dual photoelastic modulators (PEMs) and achromatic quarter-wave plates to rapidly rotate the polarization. The resulting ~26-Hz intensity modulation encodes the linearly polarized and total radiance in each pixel, leaving the DoLP and AoLP insensitive to gain calibration. We report on the polarimetric calibration of the MAIA camera using a vacuum-compatible polarization state generator, consisting of a 1600W Xenon lamp, 12-inch integrating sphere, and rotating high-extinction polarizer. Mueller-matrix-based calibration coefficients for each detector pixel are derived from measurements at multiple polarizer angles, and are used to correct the measurements for instrumental polarization aberrations. Prior to flight, the calibrated MAIA camera is panned across the OBC to characterize its output, using uniform illumination with an irradiance similar to the Sun.

Werne, Thomas↗

NASA NTRS1990

Reflectance spectra from eutrophic Mono Lake California, measured with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS)

An AVIRIS image was obtained for Mono Lake, California, on May 26, 1989, a day with excellent visibility. Atmospherically-corrected reflectance spectra derived from the image indicate a spectral signature for chlorophyll a, the dominant photosynthetic pigment in the phytoplankton of the lake. Chlorophyll a concentrations in the lake were about 22 mg/cu m, and the upwelling radiance was low with a peak reflectance at about 570 nm of about 5 percent. Coherent noise appeared in the image as regular variations of 0.1 to 0.2 microwatts/sq cm per str oriented diagonally to the flight line. A simple ratio of two spectral bands removed the conspicuous undulations, but modifications of the shielding within the instrument are needed to improve the signal especially over dark targets such as lakes.

Melack, John M.↗

NASA NTRS1991

Comparison of three atmospheric correction models for a vegetated airborne visible/infrared imaging spectrometer (AVIRIS) scene

Current atmospheric correction models applied to imaging spectroscopy data include such methods as residual or scene average, flat field correction, regression method or empirical line algorithm, the continuum interpolated band ratio (CIBR) derivation and the LOWTRAN 7 method. Due to the limitations of using residual and flat field corrections on vegetated scenes, three methods will be compared: regression, CIBR derivation and LOWTRAN 7. Field-measured bright and dark targets taken at the time of the 13 April, 1989 AVIRIS overflight of Jasper Ridge, California were used to formulate the regression method atmospheric correction. Using this corrected scene as 'ground truth', the CIBR derivation and the LOWTRAN 7 method with both input models are compared on the vegetated Jasper Ridge scene. Although representing a qualitative approach, this is a first approximation and shows the need for more quantitative analysis.

Van Den Bosch, J. M.↗

NASA NTRS1992

Satellite aerosols retrieval over land surfaces using the structure functions

Remote sensing of aerosol optical thickness from space is difficult over continental surfaces. There are two retrieval algorithms, one based on the use of dark targets and a second based on contrast reduction between selected pixels. Improvements in the contrast reduction method are reported. A procedure is developed for using the satellite image to evaluate whether conditions for applying the structure method are met. The theoretical background is discussed, and the usefulness of the structure functions is demonstrated. The method is applied to NOAA Advanced Very High Resolution Radiometer (AVHRR) imagery where simultaneous ground measurements are used for validation.

Tanre, D.↗

NASA NTRS1992

Seasonal and spatial variations in phytoplanktonic chlorophyll in eutrophic Mono Lake, California, measured with the Airborne Visible and Infrared Imaging Spectrometer (AVIRIS)

The principal problem with application of airborne imaging spectrometers to lakes is the weak upwelling signal, especially when narrow spectral bands with high spatial resolution are sought. Furthermore, atmospheric path radiance dominates the signal received from dark targets such as lakes. Once atmospheric effects have been removed from the radiance received at the sensor, semi-empirical relationships can be developed to extract information about phytoplankton pigment concentrations for different underwater optical conditions. In lakes where concentrations of dissolved organics and suspended detritus may not co-vary with phytoplankton pigments, the many spectral channels of an imaging spectrometer such as AVIRIS are likely to be required to distinguish the various aquasols. The objectives of our study are to: (1) estimate the chlorophyll content of a lake with hundred-fold seasonal ranges in chlorophyll concentration using atmospherically corrected upwelling radiances derived from AVIRIS imagery, and (2) to examine spatial patterns in chlorophyll after reduction of the coherent noise in the imagery by filtering techniques.

Melack, John M.↗

NASA NTRS1999

Principles in Remote Sensing of Aerosol from MODIS Over Land and Ocean

The well-calibrated spectral radiances measured by MODIS will be processed to retrieve daily aerosol properties that include optical thickness and mass loading over land and optical thickness, the mean particle size of the dominant mode and the ratio between aerosol modes over ocean. In addition, after launch, aerosol single scattering albedo will be calculated as an experimental product. The retrieval process over land is based on a dark target method that identifies appropriate targets in the mid-IR channels and uses an empirical relationship found between the mid-ER and the visible channels to estimate surface reflectance in the visible from the mid-HZ reflectance measured by satellite. The method employs new aerosol models for industrial, smoke and dust aerosol. The process for retrieving aerosol over the ocean makes use of the wide spectral band from 0.55-2.13 microns and a look-up table constructed from combinations of five accumulation modes and five coarse modes. Both the over land and over ocean algorithms have been validated with satellite and airborne radiance measurements. We estimate that MODIS will be able to measure aerosol optical thickness (t) to within 0.05 +/- 0.2t over land and to within 0.05 +/- 0.05t over ocean. Much of the earth's surface is located far from aerosol sources and experience very low aerosol optical thickness. Will the accuracy expected from MODIS retrievals be sufficient to measure the global aerosol direct and indirect forcing? We are attempting to answer this question using global model results and cloud climatology.

Remer, L. A.↗

NASA NTRS2011

A Critical Examination of Spatial Biases Between MODIS and MISR Aerosol Products - Application for Potential AERONET Deployment

AErosol RObotic NETwork (AERONET) data are the primary benchmark for evaluating satellite-retrieved aerosol properties. However, despite its extensive coverage, the representativeness of the AERONET data is rarely discussed. Indeed, many studies have shown that satellite retrieval biases have a significant degree of spatial correlation that may be problematic for higher-level processes or inverse-emissions-modeling studies. To consider these issues and evaluate relative performance in regions of few surface observations, cross-comparisons between the Aerosol Optical Depth (AOD) products of operational MODIS Collection 5.1 Dark Target (DT) and operational MODIS Collection 5.1 Deep Blue (DB) with MISR version 22 were conducted. Through such comparisons, we can observe coherent spatial features of the AOD bias while side-stepping the full analysis required for determining when or where either retrieval is more correct. We identify regions where MODIS to MISR AOD ratios were found to be above 1.4 and below 0.7. Regions where lower boundary condition uncertainty is likely to be a dominant factor include portions of Western North America, the Andes mountains, Saharan Africa, the Arabian Peninsula, and Central Asia. Similarly, microphysical biases may be an issue in South America, and specific parts of Southern Africa, India Asia, East Asia, and Indonesia. These results help identify high-priority locations for possible future deployments of both in situ and ground based remote sensing measurements. The Supplement includes a km1 file.

Shi, Y.↗