scholarly journals A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

2011 ◽  
Vol 11 (23) ◽  
pp. 11977-11991 ◽  
Author(s):  
H. Zhang ◽  
A. Lyapustin ◽  
Y. Wang ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
The United States ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Polar Orbiting Satellite

Abstract. Aerosol optical depth (AOD) retrievals from geostationary satellites have high temporal resolution compared to the polar orbiting satellites and thus enable us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosols and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) in the channel 1 of GOES is proportional to seasonal average MODIS BRDF in the 2.1 μm channel. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of AOD and surface reflectance retrievals are evaluated through comparisons against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US with correlation coefficients ranges from 0.71 to 0.85 at five out of six sites. At the two western sites Railroad Valley and UCSB, the MAIAC AOD retrievals have correlations of 0.8 and 0.85 with AERONET AOD, and are more accurate than GASP retrievals, which have correlations of 0.7 and 0.74 with AERONET AOD. At the three eastern sites, the correlations with AERONET AOD are from 0.71 to 0.81, comparable to the GASP retrievals. In the western US where surface reflectance is higher than 0.15, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

scholarly journals A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States

2011 ◽  
Vol 11 (4) ◽  
pp. 12519-12560
Author(s):  
H. Zhang ◽  
A. Lyapustin ◽  
Y. Wang ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
The United States ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Western Us ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Polar Orbiting Satellite

Abstract. Aerosol optical depth (AOD) retrieval from geostationary satellites has high temporal resolution compared to the polar orbiting satellites and thus enables us to monitor aerosol motion. However, current Geostationary Operational Environmental Satellites (GOES) have only one visible channel for retrieving aerosol and hence the retrieval accuracy is lower than those from the multichannel polar-orbiting satellite instruments such as the Moderate Resolution Imaging Spectroradiometer (MODIS). The operational GOES AOD retrieval algorithm (GOES Aerosol/Smoke Product, GASP) uses 28-day composite images from the visible channel to derive surface reflectance, which can produce large uncertainties. In this work, we develop a new AOD retrieval algorithm for the GOES imager by applying a modified multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm. The algorithm assumes the surface Bidirectional Reflectance Distribution Function (BRDF) at channel 1 of GOES is proportional to seasonal average BRDF in the 2.1 μm channel from MODIS. The ratios between them are derived through time series analysis of the GOES visible channel images. The results of the AOD and surface reflectance retrievals are evaluated through comparison against those from Aerosol Robotic Network (AERONET), GASP, and MODIS. The AOD retrievals from the new algorithm demonstrate good agreement with AERONET retrievals at several sites across the US. They are comparable to the GASP retrievals in the eastern-central sites and are more accurate than GASP retrievals in the western sites. In the western US where surface reflectance is high, the new algorithm also produces larger AOD retrieval coverage than both GASP and MODIS.

Validation of JAXA Himawari-8 Aerosol Optical Depth Products over China with AERONET and CARSNET Observations

2020 ◽  
Author(s):  
Ling Gao ◽  
Chengcai Li ◽  
Lin Chen ◽  
Jun Li ◽  
Huizheng Che
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Southern China ◽  
Correlation Coefficients ◽  
Northern China ◽  
Temporal Variations ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Modis Aod

<p>The performance of JAXA Himawari-8 Advanced Himawari Imager (AHI) aerosol optical depth (AOD) products over China is evaluated with ground-based AErosol RObotic NETwork (AERONET) and Sun-Sky Radiometer Observation Network (CARSNET) observations as well as the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD products. Considering the quality and quantity of valid data, the study was limited to AOD products from AHI with a Quality Assurance Flag (QA_Flag) of “good” and “very good.” The spatial distribution of the AHI AOD product is similar to that of the MODIS AOD product. The AOD correlation between AHI and MODIS is better in the morning than in the afternoon after March, however, using MODIS AOD as a reference resulted in underestimation in the morning and overestimation in the afternoon. The bias is also larger in spring and autumn than in summer and winter. Validation with sun-photometer observations indicates good correlation between AHI AOD and ground-based observations with correlation coefficients larger than 0.75 (N>1000) when barren and sparsely vegetated surfaces are excluded. At 02:30 UTC, 53% of the collocated AHI AOD observations fall in the expected error (EE) range and at 5:30 UTC, 59.3% fall above the EE. The AHI AOD overestimation was apparent at the Northern China stations in April and after October, whereas the underestimation was apparent in southern China throughout the year. The temporal variations of AHI and AERONET AOD also show that the overestimation occurred in the afternoon and underestimation occurred in the morning.</p><p>The assumption that the solar geometries were nearly identical and the surface reflectance unchanged for a month causes the surface reflectance underestimation and leads to the AOD overestimation for barren surfaces in autumn and winter. Because background aerosols were neglected, the surface reflectance was overestimated, leading to AOD underestimation in vegetated surfaces.</p><p>Overall, the JAXA AOD provides a reliable and high temporal resolution aerosol product for environmental and climate research and the aerosol retrieval algorithm requires improvement.</p>

scholarly journals Aerosol Optical Depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the Western US

2012 ◽  
Vol 5 (5) ◽  
pp. 7945-7981
Author(s):  
H. Zhang ◽  
R. M. Hoff ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
A. Lyapustin
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Western United States ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Retrieval Accuracy ◽  
Western Us ◽  
Data Coverage

Abstract. Aerosol Optical Depth (AOD) in the Western United States is observed independently by both the GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus NOAA obtains two separate aerosol optical depth values at the same time for the same location. The TOA radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses combined GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on clear days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF. The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the Western US. The AOD retrieval accuracy from the hybrid technique using the two satellites is similar to that from one satellite over UCSB and Railroad Valley. Improvement of the accuracy is observed at Boulder. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81 over the three sites. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high reflectance. The number of coincidences with AERONET observations increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

scholarly journals Aerosol optical depth (AOD) retrieval using simultaneous GOES-East and GOES-West reflected radiances over the western United States

2013 ◽  
Vol 6 (2) ◽  
pp. 471-486 ◽  
Author(s):  
H. Zhang ◽  
R. M. Hoff ◽  
S. Kondragunta ◽  
I. Laszlo ◽  
A. Lyapustin
Keyword(s):  
United States ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
High Surface ◽  
Atmospheric Correction ◽  
Correlation Coefficients ◽  
Western United States ◽  
Retrieval Algorithm ◽  
Hybrid Technique ◽  
Single Observation

Abstract. Aerosol optical depth (AOD) in the western United States is observed independently by both the (Geostationary Operational Environmental Satellites) GOES-East and GOES-West imagers. The GASP (GOES Aerosol/Smoke Product) aerosol optical depth retrieval algorithm treats each satellite as a unique sensor and thus obtains two separate aerosol optical depth values at the same time for the same location. The TOA (the top of the atmosphere) radiances and the associated derived optical depths can be quite different due to the different viewing geometries with large difference in solar-scattering angles. In order to fully exploit the simultaneous observations and generate consistent AOD retrievals from the two satellites, the authors develop a new "hybrid" aerosol optical depth retrieval algorithm that uses data from both satellites. The algorithm uses both GOES-East and GOES-West visible channel TOA reflectance and daily average AOD from GOES Multi-Angle Implementation of Atmospheric Correction (GOES-MAIAC) on low AOD days (AOD less than 0.3), when diurnal variation of AOD is low, to retrieve surface BRDF (Bidirectional Reflectance Distribution Function). The known BRDF shape is applied on subsequent days to retrieve BRDF and AOD. The algorithm is validated at three AERONET sites over the western US. The AOD retrieval accuracy from the "hybrid" technique using the two satellites is similar to that from one satellite over UCSB (University of California Santa Barbara) and Railroad Valley, Nevada. Improvement of the accuracy is observed at Boulder, Colorado. The correlation coefficients between the GOES AOD and AERONET AOD are in the range of 0.67 to 0.81. More than 74% of AOD retrievals are within the error of ±(0.05 + 0.15 τ) compared to AERONET AOD. The hybrid algorithm has more data coverage compared to the single satellite retrievals over surfaces with high surface reflectance. For single observation areas the number of valid AOD data increases from the use of two-single satellite algorithms by 5–80% for the three sites. With the application of the new algorithm, consistent AOD retrievals and better retrieval coverages can be obtained using the data from the two GOES satellite imagers.

scholarly journals Retrieval of High Temporal Resolution Aerosol Optical Depth Using the GOCI Remote Sensing Data

2021 ◽  
Vol 13 (12) ◽  
pp. 2376
Author(s):  
Lijuan Chen ◽  
Ying Fei ◽  
Ren Wang ◽  
Peng Fang ◽  
Jiamei Han ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Temporal Resolution ◽  
Deep Understanding ◽  
Atmospheric Environment ◽  
P Value ◽  
High Temporal Resolution ◽  
Surface Reflectance ◽  
Polar Orbiting Satellite ◽  
Solar Angle

High temporal resolution aerosol optical depth (AOD) products are very important for the studies of atmospheric environment and climate change. Geostationary Ocean Color Imager (GOCI) is a suitable data source for AOD retrieval, as it can monitor hourly aerosol changes and make up for the low temporal resolution deficiency of polar orbiting satellite. In this study, we proposed an algorithm for retrieving high temporal resolution AOD using GOCI data and then applied the algorithm in the Yangtze River Delta, a typical region suffering severe air pollution issues. Based on Moderate-resolution Imaging Spectroradiometer (MODIS) surface reflectance determined by MODIS V5.2 algorithm and MODIS Bidirectional Reflectance Distribution Function (BRDF) data, after spectral conversion between MODIS and GOCI, the GOCI surface reflectance at different solar angles were obtained and used to retrieve AOD. Five indicators including correlation coefficient (R), significant level of the correlation (p value), mean absolute error (MAE), mean relative error (MRE) and root mean square error (RMSE) were employed to analyze the errors between the Aerosol Robotic Network (AERONET) observed AOD and the GOCI retrieved AOD. The results showed that the GOCI AOD retrieved by the continental aerosol look-up table was consistent with the AERONET AOD (R > 0.7, p ≤ 0.05). The highest R value of Taihu Station and Xuzhou CUMT Station are both 0.84 (8:30 a.m.); the minimum RMSE at Taihu and Xuzhou-CUMT stations were 0.2077 (11:30 a.m.) and 0.1937 (10:30 a.m.), respectively. Moreover, the results suggested that the greater the solar angle of the GOCI sensor, the higher the AOD retrieval accuracy, while the retrieved AOD at noon exhibited the largest error as assessed by MAE and MRE. We concluded that the inaccurate estimation of surface reflectance was the root cause of the retrieval errors. This study has implications in providing a deep understanding of the effects of solar angle changes on retrieving AOD using GOCI.

scholarly journals A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm

2016 ◽  
Vol 9 (7) ◽  
pp. 3293-3308 ◽  
Author(s):  
Pawan Gupta ◽  
Robert C. Levy ◽  
Shana Mattoo ◽  
Lorraine A. Remer ◽  
Leigh A. Munchak
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Land Surface ◽  
Urban Areas ◽  
Urban Surface ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Surface Parameterization ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. The MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aboard the two Earth Observing System (EOS) satellites Terra and Aqua, provide aerosol information with nearly daily global coverage at moderate spatial resolution (10 and 3 km). Almost 15 years of aerosol data records are now available from MODIS that can be used for various climate and air-quality applications. However, the application of MODIS aerosol products for air-quality concerns is limited by a reduction in retrieval accuracy over urban surfaces. This is largely because the urban surface reflectance behaves differently than that assumed for natural surfaces. In this study, we address the inaccuracies produced by the MODIS Dark Target (MDT) algorithm aerosol optical depth (AOD) retrievals over urban areas and suggest improvements by modifying the surface reflectance scheme in the algorithm. By integrating MODIS Land Surface Reflectance and Land Cover Type information into the aerosol surface parameterization scheme for urban areas, much of the issues associated with the standard algorithm have been mitigated for our test region, the continental United States (CONUS). The new surface scheme takes into account the change in underlying surface type and is only applied for MODIS pixels with urban percentage (UP) larger than 20 %. Over the urban areas where the new scheme has been applied (UP > 20 %), the number of AOD retrievals falling within expected error (EE %) has increased by 20 %, and the strong positive bias against ground-based sun photometry has been eliminated. However, we note that the new retrieval introduces a small negative bias for AOD values less than 0.1 due to the ultra-sensitivity of the AOD retrieval to the surface parameterization under low atmospheric aerosol loadings. Global application of the new urban surface parameterization appears promising, but further research and analysis are required before global implementation.

scholarly journals A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS dark target retrieval algorithm

2016 ◽  
Author(s):  
P. Gupta ◽  
R. C. Levy ◽  
S. Mattoo ◽  
L. A. Remer ◽  
L. A. Munchak
Keyword(s):  
Air Quality ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Land Surface ◽  
Urban Areas ◽  
Retrieval Algorithm ◽  
Surface Reflectance ◽  
Surface Parameterization ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Surface Scheme

Abstract. The MODerate resolution Imaging Spectroradiometer (MODIS) instruments, aboard two Earth Observing Satellites (EOS) Terra and Aqua, provide aerosol information with nearly daily global coverage at moderate spatial resolution (10 km and 3 km). Almost 15 years of aerosol data records are now available from MODIS that can be used for various climate and air quality applications. However, the application of MODIS aerosol products for air quality concerns is limited by a reduction in retrieval accuracy over urban surfaces. Here, in this study, we address the inaccuracies produced by the MODIS dark target algorithm (MDT) Aerosol Optical Depth (AOD) retrievals over urban areas and suggest improvements by modifying the surface reflectance scheme in the algorithm. By integrating MODIS land surface reflectance and land cover type information into the aerosol surface parameterization scheme for urban areas, much of the issues associated with the standard algorithm have been mitigated for our test region, the Continental United States (CONUS). The new surface scheme takes into account the change in under lying surface type and is only applied for MODIS pixels with urban percentage (UP) larger than 20%. Over the urban areas where the new scheme has been applied (UP > 20 %), the number of AOD retrievals falling within expected error (EE %) has increased by 20 %, and the strong positive bias against ground-based sunphotometry has been eliminated. However, we note that the new retrieval introduces a small negative bias for AOD values less than 0.1, due to ultra sensitivity of the AOD retrieval to the surface parameterization under low atmospheric aerosol loadings. Global application of the new urban surface parameterization appears promising, but further research and analysis are required before global implementation.

scholarly journals Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

2015 ◽  
Vol 8 (10) ◽  
pp. 4083-4110 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Quantitative Information ◽  
Environmental Data ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Data Record ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

scholarly journals Improved MODIS Dark Target aerosol optical depth algorithm over land: angular effect correction

2016 ◽  
Vol 9 (11) ◽  
pp. 5575-5589 ◽  
Author(s):  
Yerong Wu ◽  
Martin de Graaf ◽  
Massimo Menenti
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Vegetation Index ◽  
Surface Reflectance ◽  
Infrared Band ◽  
Surface Reflection ◽  
Global Land ◽  
Bidirectional Reflectance Factor ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
The Relationship

Abstract. Aerosol optical depth (AOD) product retrieved from MODerate Resolution Imaging Spectroradiometer (MODIS) measurements has greatly benefited scientific research in climate change and air quality due to its high quality and large coverage over the globe. However, the current product (e.g., Collection 6) over land needs to be further improved. The is because AOD retrieval still suffers large uncertainty from the surface reflectance (e.g., anisotropic reflection) although the impacts of the surface reflectance have been largely reduced using the Dark Target (DT) algorithm. It has been shown that the AOD retrieval over dark surface can be improved by considering surface bidirectional distribution reflectance function (BRDF) effects in previous study. However, the relationship of the surface reflectance between visible and shortwave infrared band that applied in the previous study can lead to an angular dependence of the AOD retrieval. This has at least two reasons. The relationship based on the assumption of isotropic reflection or Lambertian surface is not suitable for the surface bidirectional reflectance factor (BRF). However, although the relationship varies with the surface cover type by considering the vegetation index NDVISWIR, this index itself has a directional effect and affects the estimation of the surface reflection, and it can lead to some errors in the AOD retrieval. To improve this situation, we derived a new relationship for the spectral surface BRF in this study, using 3 years of data from AERONET-based Surface Reflectance Validation Network (ASRVN). To test the performance of the new algorithm, two case studies were used: 2 years of data from North America and 4 months of data from the global land. The results show that the angular effects of the AOD retrieval are largely reduced in most cases, including fewer occurrences of negative retrievals. Particularly, for the global land case, the AOD retrieval was improved by the new algorithm compared to the previous study and MODIS Collection 6 DT algorithm, with the increase of 2.0 and 4.5 % AOD retrievals falling within the expected accuracy envelope ±(0.05 + 15 %), respectively. This implies that the users can get more accurate data without angular bias, i.e., more meaningful AOD data.

scholarly journals Comparison of MODIS 3 km and 10 km resolution aerosol optical depth retrievals over land with airborne sunphotometer measurements during ARCTAS summer 2008

2014 ◽  
Vol 14 (4) ◽  
pp. 2015-2038 ◽  
Author(s):  
J. M. Livingston ◽  
J. Redemann ◽  
Y. Shinozuka ◽  
R. Johnson ◽  
P. B. Russell ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Retrieval Algorithm ◽  
Grid Cells ◽  
Data Set ◽  
Clear Sky ◽  
Modis Aod ◽  
Research Aircraft ◽  
Bright Surface ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. Airborne sunphotometer measurements acquired by the NASA Ames Airborne Tracking Sunphotometer (AATS-14) aboard the NASA P-3 research aircraft are used to evaluate dark-target over-land retrievals of extinction aerosol optical depth (AOD) from spatially and temporally near-coincident measurements by the Moderate Resolution Imaging Spectroradiometer (MODIS) during the summer 2008 Arctic Research of the Composition of the Troposphere from Aircraft and Satellites (ARCTAS) field campaign. The new MODIS Collection 6 aerosol data set includes retrievals of AOD at both 10 km × 10 km and 3 km × 3 km (at nadir) resolution. In this paper we compare MODIS and AATS AOD at 553 nm in 58 10 km and 134 3 km retrieval grid cells. These AOD values were derived from data collected over Canada on four days during short time segments of five (four Aqua and one Terra) satellite overpasses of the P-3 during low-altitude P-3 flight tracks. Three of the five MODIS–AATS coincidence events were dominated by smoke: one included a P-3 transect of a well-defined smoke plume in clear sky, but two were confounded by the presence of scattered clouds above smoke. The clouds limited the number of MODIS retrievals available for comparison, and led to MODIS AOD retrievals that underestimated the corresponding AATS values. This happened because the MODIS aerosol cloud mask selectively removed 0.5 km pixels containing smoke and clouds before the aerosol retrieval. The other two coincidences (one Terra and one Aqua) occurred during one P-3 flight on the same day and in the same general area, in an atmosphere characterized by a relatively low AOD (< 0.3), spatially homogeneous regional haze from smoke outflow with no distinguishable plume. For the ensemble data set for MODIS AOD retrievals with the highest-quality flag, MODIS AOD agrees with AATS AOD within the expected MODIS over-land AOD uncertainty in 60% of the retrieval grid cells at 10 km resolution and 69% at 3 km resolution. These values improve to 65 % and 74%, respectively, when the cloud-affected case with the strongest plume is excluded. We find that the standard MODIS dark-target over-land retrieval algorithm fails to retrieve AOD for thick smoke, not only in cloud-contaminated regions but also in clear sky. We attribute this to deselection, by the cloud and/or bright surface masks, of 0.5 km resolution pixels that contain smoke.

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