Exploring systematic offsets between aerosol products from the two MODIS sensors

Abstract. Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm has been applied to Moderate-resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55 μm) over both land and ocean, and Angstrom Exponent (AE derived from 0.55 and 0.86 μmm) over ocean. Here, we analyse the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by ~13 % (~0.02 over land and ~0.015 over ocean), and this offset (MOD04 – MYD04), has seasonal as well as long-term variability. Focusing on 2008, and deriving yearly gridded mean AOD and AE, we find that over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model, and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensors' polarization sensitivity, response-versus-scan angle, and to cross-calibration from MODIS-T to MODIS-A. A second convolves the de-trending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval, using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A Collection (C6.1) of all MODIS-atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that: a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. b) Because the MODIS-A product appears to have overall smaller bias compared to ground-truth, it may be more suitable for some applications, however c) since the AOD offset is only ~0.02 and within noise level for single retrievals, both MODIS products may be adequate for most applications.

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Exploring systematic offsets between aerosol products from the two MODIS sensors

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4073-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 4073-4092 ◽

Cited By ~ 31

Author(s):

Robert C. Levy ◽

Shana Mattoo ◽

Virginia Sawyer ◽

Yingxi Shi ◽

Peter R. Colarco ◽

...

Keyword(s):

Ground Truth ◽

Retrieval Algorithm ◽

Polarization Sensitivity ◽

Ground Truth Data ◽

Diurnal Cycles ◽

Aerosol Retrieval ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Cross Calibration

Abstract. Long-term measurements of global aerosol loading and optical properties are essential for assessing climate-related questions. Using observations of spectral reflectance and radiance, the dark-target (DT) aerosol retrieval algorithm is applied to Moderate Resolution Imaging Spectroradiometer sensors on both Terra (MODIS-T) and Aqua (MODIS-A) satellites, deriving products (known as MOD04 and MYD04, respectively) of global aerosol optical depth (AOD at 0.55 µm) over both land and ocean, and an Ångström exponent (AE derived from 0.55 and 0.86 µm) over ocean. Here, we analyze the overlapping time series (since mid-2002) of the Collection 6 (C6) aerosol products. Global monthly mean AOD from MOD04 (Terra with morning overpass) is consistently higher than MYD04 (Aqua with afternoon overpass) by ∼ 13 % (∼ 0.02 over land and ∼ 0.015 over ocean), and this offset (MOD04 – MYD04) has seasonal as well as long-term variability. Focusing on 2008 and deriving yearly gridded mean AOD and AE, we find that, over ocean, the MOD04 (morning) AOD is higher and the AE is lower. Over land, there is more variability, but only biomass-burning regions tend to have AOD lower for MOD04. Using simulated aerosol fields from the Goddard Earth Observing System (GEOS-5) Earth system model and sampling separately (in time and space) along each MODIS-observed swath during 2008, the magnitudes of morning versus afternoon offsets of AOD and AE are smaller than those in the C6 products. Since the differences are not easily attributed to either aerosol diurnal cycles or sampling issues, we test additional corrections to the input reflectance data. The first, known as C6+, corrects for long-term changes to each sensor's polarization sensitivity and the response versus the scan angle and to cross-calibration from MODIS-T to MODIS-A. A second convolves the detrending and cross-calibration into scaling factors. Each method was applied upstream of the aerosol retrieval using 2008 data. While both methods reduced the overall AOD offset over land from 0.02 to 0.01, neither significantly reduced the AOD offset over ocean. The overall negative AE offset was reduced. A collection (C6.1) of all MODIS Atmosphere products was released, but we expect that the C6.1 aerosol products will maintain similar overall AOD and AE offsets. We conclude that (a) users should not interpret global differences between Terra and Aqua aerosol products as representing a true diurnal signal in the aerosol. (b) Because the MODIS-A product appears to have an overall smaller bias compared to ground-truth data, it may be more suitable for some applications. However (c), since the AOD offset is only ∼ 0.02 and within the noise level for single retrievals, both MODIS products may be adequate for most applications.

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Analysis of the MODIS Above-Cloud Aerosol Retrieval Algorithm Using MCARS

10.5194/gmd-2021-17 ◽

2021 ◽

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Kerry G. Meyer ◽

Steven Platnick ◽

Peter M. Norris

Keyword(s):

Boundary Layer ◽

Optical Thickness ◽

Marine Boundary Layer ◽

Single Case ◽

Ground Truth ◽

Retrieval Algorithm ◽

Boundary Layer Clouds ◽

Aerosol Retrieval ◽

Source Data ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) was viewing a combination of atmospheric column inclusive of clouds, aerosols and a variety of gases and land/ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass burning aerosols overlying marine boundary layer clouds in the Southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules was used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4 and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE = 0.107. When only near-nadir pixels were considered (view zenith angle within +/−20 degrees) the agreement with source data further improved (0.977, 0.051 and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with offset −0.007 and RMSE of 0.097 at pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

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Analysis of the MODIS above-cloud aerosol retrieval algorithm using MCARS

Geoscientific Model Development ◽

10.5194/gmd-15-1-2022 ◽

2022 ◽

Vol 15 (1) ◽

pp. 1-14

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Kerry G. Meyer ◽

Steven Platnick ◽

Peter M. Norris

Keyword(s):

Boundary Layer ◽

Optical Thickness ◽

Marine Boundary Layer ◽

Single Case ◽

Ground Truth ◽

Retrieval Algorithm ◽

Boundary Layer Clouds ◽

Aerosol Retrieval ◽

Source Data ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. The Multi-sensor Cloud and Aerosol Retrieval Simulator (MCARS) presently produces synthetic radiance data from Goddard Earth Observing System version 5 (GEOS-5) model output as if the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of atmospheric column inclusive of clouds, aerosols, and a variety of gases and land–ocean surface at a specific location. In this paper we use MCARS to study the MODIS Above-Cloud AEROsol retrieval algorithm (MOD06ACAERO). MOD06ACAERO is presently a regional research algorithm able to retrieve aerosol optical thickness over clouds, in particular absorbing biomass-burning aerosols overlying marine boundary layer clouds in the southeastern Atlantic Ocean. The algorithm's ability to provide aerosol information in cloudy conditions makes it a valuable source of information for modeling and climate studies in an area where current clear-sky-only operational MODIS aerosol retrievals effectively have a data gap between the months of June and October. We use MCARS for a verification and closure study of the MOD06ACAERO algorithm. The purpose of this study is to develop a set of constraints a model developer might use during assimilation of MOD06ACAERO data. Our simulations indicate that the MOD06ACAERO algorithm performs well for marine boundary layer clouds in the SE Atlantic provided some specific screening rules are observed. For the present study, a combination of five simulated MODIS data granules were used for a dataset of 13.5 million samples with known input conditions. When pixel retrieval uncertainty was less than 30 %, optical thickness of the underlying cloud layer was greater than 4, and scattering angle range within the cloud bow was excluded, MOD06ACAERO retrievals agreed with the underlying ground truth (GEOS-5 cloud and aerosol profiles used to generate the synthetic radiances) with a slope of 0.913, offset of 0.06, and RMSE=0.107. When only near-nadir pixels were considered (view zenith angle within ±20∘) the agreement with source data further improved (0.977, 0.051, and 0.096 respectively). Algorithm closure was examined using a single case out of the five used for verification. For closure, the MOD06ACAERO code was modified to use GEOS-5 temperature and moisture profiles as an ancillary. Agreement of MOD06ACAERO retrievals with source data for the closure study had a slope of 0.996 with an offset of −0.007 and RMSE of 0.097 at a pixel uncertainty level of less than 40 %, illustrating the benefits of high-quality ancillary atmospheric data for such retrievals.

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Ten Years of TRMM/VIRS On-Orbit Calibrations and Multiyear Comparisons of VIRS and MODIS

Journal of Atmospheric and Oceanic Technology ◽

10.1175/2008jtecha1110.1 ◽

2008 ◽

Vol 25 (12) ◽

pp. 2259-2270 ◽

Cited By ~ 2

Author(s):

Cheng-Hsuan Lyu ◽

William L. Barnes

Keyword(s):

Tropical Rainfall Measuring Mission ◽

Radiometric Calibration ◽

Successful Operation ◽

Earth Observing System ◽

Spectral Bands ◽

Calibration Algorithm ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Phase Angles

Abstract After 10 years of successful operation of the Tropical Rainfall Measuring Mission (TRMM)/Visible Infrared Scanner (VIRS), based on sensor performance, the authors have reexamined the calibration algorithms and identified several ways to improve the current VIRS level-1B radiometric calibration software. This study examines the trends in VIRS on-orbit calibration results by using lunar measurements to enable separation of the solar diffuser degradation from that of the VIRS Earth-viewing sensor and by comparing the radiometric data with two nearly identical Moderate Resolution Imaging Spectroradiometer (MODIS) instruments on board the NASA Earth Observing System (EOS) Terra and Aqua satellites. For the VIRS, with spectral bands quite similar to several of the MODIS bands, the integrated lunar reflectance data were measured, from January 1998 to March 2007, at phase angles ranging from 0.94° to 121.8°. The authors present trending of the lunar data over periods of 4 yr (Aqua/MODIS), 6 yr (Terra/MODIS), and 10 yr (TRMM/VIRS) and use these observations to examine instrument radiometric stability. The VIRS-measured lunar irradiances are compared with the MODIS-measured lunar irradiances at phase angles around 54°–56°. With the upcoming modified VIRS level-1B version 7 calibration algorithm, the VIRS, along with MODIS, should provide better references for intercalibrating multiple Earth-observing sensors.

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Bayesian aerosol retrieval algorithm for MODIS AOD retrieval over land

Atmospheric Measurement Techniques ◽

10.5194/amt-11-1529-2018 ◽

2018 ◽

Vol 11 (3) ◽

pp. 1529-1547 ◽

Cited By ~ 7

Author(s):

Antti Lipponen ◽

Tero Mielonen ◽

Mikko R. A. Pitkänen ◽

Robert C. Levy ◽

Virginia R. Sawyer ◽

...

Keyword(s):

Computation Time ◽

Retrieval Algorithm ◽

Surface Reflectance ◽

Aerosol Retrieval ◽

Correlation Models ◽

Modis Aod ◽

Uncertainty Estimates ◽

Average Computation Time ◽

Moderate Resolution Imaging Spectroradiometer ◽

Aerosol Parameters

Abstract. We have developed a Bayesian aerosol retrieval (BAR) algorithm for the retrieval of aerosol optical depth (AOD) over land from the Moderate Resolution Imaging Spectroradiometer (MODIS). In the BAR algorithm, we simultaneously retrieve all dark land pixels in a granule, utilize spatial correlation models for the unknown aerosol parameters, use a statistical prior model for the surface reflectance, and take into account the uncertainties due to fixed aerosol models. The retrieved parameters are total AOD at 0.55 µm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (0.47, 0.55, 0.64, and 2.1 µm). The accuracy of the new algorithm is evaluated by comparing the AOD retrievals to Aerosol Robotic Network (AERONET) AOD. The results show that the BAR significantly improves the accuracy of AOD retrievals over the operational Dark Target (DT) algorithm. A reduction of about 29 % in the AOD root mean square error and decrease of about 80 % in the median bias of AOD were found globally when the BAR was used instead of the DT algorithm. Furthermore, the fraction of AOD retrievals inside the ±(0.05+15%) expected error envelope increased from 55 to 76 %. In addition to retrieving the values of AOD, FMF, and surface reflectance, the BAR also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BAR algorithm always results in physical, non-negative AOD values, and the average computation time for a single granule was less than a minute on a modern personal computer.

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Retrieval of aerosol optical depth over land surfaces from AVHRR data

Atmospheric Measurement Techniques ◽

10.5194/amt-7-2411-2014 ◽

2014 ◽

Vol 7 (8) ◽

pp. 2411-2420 ◽

Cited By ~ 25

Author(s):

L. L. Mei ◽

Y. Xue ◽

A. A. Kokhanovsky ◽

W. von Hoyningen-Huene ◽

G. de Leeuw ◽

...

Keyword(s):

Time Series ◽

Aerosol Optical Depth ◽

Optical Depth ◽

Limited Information ◽

Additional Information ◽

Aerosol Retrieval ◽

Avhrr Data ◽

Moderate Resolution Imaging Spectroradiometer ◽

Land Surfaces

Abstract. The Advanced Very High Resolution Radiometer (AVHRR) provides a global, long-term, consistent time series of radiance data in several wavebands which are used for the retrieval of surface spectral reflectance, albedo and surface temperature. Long-term time series of such data products are necessary for studies addressing climate change, sea ice distribution and movement, and ice sheet coastal configuration. AVHRR radiances have also been used to retrieve aerosol properties over ocean and land surfaces. However, the retrieval of aerosol over land is challenging because of the limited information content in the data which renders the inversion problem ill defined. Solving the radiative transfer equations requires additional information to reduce the number of unknowns. In this contribution we utilise an empirical linear relationship between the surface reflectances in the AVHRR channels at wavelengths of 3.75 μm and 2.1 μm, which has been identified in the Moderate Resolution Imaging Spectroradiometer (MODIS) data. Following the MODIS dark target approach, this relationship is used to obtain the surface reflectance at 0.64 μm. The comparison of the estimated surface reflectances with MODIS reflectance products (MOD09) shows a strong correlation. Once this was established, the MODIS "dark-target" aerosol retrieval method was adapted to AVHRR data. A simplified look-up table (LUT) method, adopted from the Bremen AErosol Retrieval (BAER) algorithm, was used in the retrieval. The aerosol optical depth (AOD) values retrieved from AVHRR with this method compare favourably with ground-based measurements, with 71.8% of the points located within ±(0.1 + 0.15τ) (τ is the AOD) of the identity line. This method can be easily applied to other satellite instruments which do not have a 2.1 μm channel, such as those currently planned to be used on geostationary satellites.

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Validation of MODIS 3 km Land Aerosol Optical Depth from NASA's EOS Terra and Aqua Missions

10.5194/amt-2018-44 ◽

2018 ◽

Author(s):

Pawan Gupta ◽

Lorraine A. Remer ◽

Robert C. Levy ◽

Shana Mattoo

Keyword(s):

Aerosol Optical Depth ◽

Optical Depth ◽

Retrieval Algorithm ◽

Data Sets ◽

Aerosol Retrieval ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Sensor Degradation ◽

Over Time

Abstract. The two MODerate Resolution Imaging Spectroradiometer (MODIS) sensors, aboard Earth Observing Satellites (EOS) Terra and Aqua, have been making aerosol observations for more than 15 years. From these observations, the MODIS dark target (DT) aerosol retrieval algorithm provides aerosol optical depth (AOD) products, globally over both land and ocean. In addition to the standard resolution product (10 × 10 km2), the MODIS collection 6 (C006) data release included a higher resolution (3 × 3 km2). Other than accommodations for the two different resolutions, the 10 km, and 3 km DT algorithms are basically the same. In this study, we perform global validation of the higher resolution AOD over global land by comparing against AERONET measurements. The MODIS-AERONET collocated data sets consist of 161,410 high-confidence AOD pairs from 2000 to 2015 for MODIS Terra and 2003 to 2015 for MODIS-Aqua. We find that 62.5 % and 68.4 % of AODs retrieved from MODIS-Terra and MODIS-Aqua, respectively, fall within previously published expected error bounds of ±(0.05 + 0.2*AOD), with a high correlation (R = 0.87). The scatter is not random but exhibits a mean positive bias of ~ 0.06 for Terra and ~ 0.03 for Aqua. These biases for the 3 km product are approximately 0.03 larger than the biases found in similar validations of the 10 km product. The validation results for the 3 km product did not have a relationship to aerosol loading (i.e. true AOD) but did exhibit dependence on quality flags, region, viewing geometry, and aerosol spatial variability. Time series of global MODIS-AERONET differences show that validation is not static, but has changed over the course of both sensors' lifetimes, with MODIS-Terra showing more change over time. The likely cause of the change of validation over time is sensor degradation, but changes in the distribution of AERONET stations and differences in the global aerosol system itself could be contributing to the temporal variability of validation.

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High Efficiency of Solar Cell Model Based on Two Types of Nanoparticles

Jordan Journal of Physics ◽

10.47011/13.1.9 ◽

2020 ◽

Vol 13 (1) ◽

pp. 87-92

Keyword(s):

Solar Cell ◽

High Efficiency ◽

Cell Model ◽

Deep Blue ◽

Moderate Resolution ◽

Resolution Imaging ◽

Moderate Resolution Imaging Spectroradiometer ◽

Terra Modis ◽

Level 2

Climatology of aerosols, their trends and classification based on the long-term Moderate Resolution Imaging Spectroradiometer (MODIS) measurements (from February 2000 to July 2015) of aerosol optical depths at 550 nm (τ550) and Angstrom exponent (α470-660) using the wavelengths of 470 and 660nm in Nairobi, Skukuza and Ilorin AERONET stations were analyzed in this work. The level-2 collection-6 Deep Blue (L2 C006 DB) of the parameters listed above from the aqua- (MYD04) and terra- (MOD04) MODIS of the study area were statistically analyzed using SPSS. To be able to understand the temporal variation in the characteristics of aerosols in the three stations and during each season separately, MODIS measurements of τ, retrieved for the study area, were compared with AERONET τ. Overall, aqua-MODIS τ corroborate the AERONET measurements well in Nairobi and Ilorin stations with underestimation of 29.80 % and overestimation of 2.90 % respectively, whereas Skukuza station has terra-MODIS τ as the best representation of the AERONET measurements with underestimation of 1.90 %. ....

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Aerosol retrieval based on 10 years of passive remote sensing satellite measurements over the Arctic – validation and trend analysis

10.5194/egusphere-egu2020-15037 ◽

2020 ◽

Author(s):

Soheila Jafariserajehlou ◽

Marco Vountas ◽

Larysa Istomina ◽

John P. Burrows

Keyword(s):

Remote Sensing ◽

Trend Analysis ◽

Optical Thickness ◽

Arctic Region ◽

The Arctic ◽

Retrieval Algorithm ◽

Identification Algorithm ◽

Aerosol Retrieval ◽

Along Track

The Aerosol Optical Thickness (AOT) retrieval over the Arctic region is a challenging task due to uncertainties and difficulties in its prerequisites, mainly (i) cloud masking methods and (ii) modeling the underlying snow/ice surface. In the past this led to a large data gap over the Arctic which hampered our understanding of the direct/indirect aerosol effect on Arctic and global climate change. For the purpose of improving our knowledge, we present, for the first time, long-term AOT maps of snow and ice covered areas based on satellite remote sensing.In this study, a previously developed aerosol retrieval algorithm over snow/ice, (Istomina et al., 2012; in IUP, University of Bremen) is used to retrieve AOT for a period of 10 years, 2002-2012, over the Arctic and to analyze its spatial and temporal changes. This algorithm is based on a multi-angle approach and uses pre-computed look-up tables to retrieve AOT.The algorithm has been improved with respect to cloud masking (based on clear snow spectral shape) using the ASCIA cloud identification algorithm (Jafariserajehlou et al., 2019). The modified AOT retrieval algorithm is applied to observations from Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency&#8217;s (ESA) measurements. The retrieved dataset provides long-term AOT at a spatial resolution of 1 km2 over snow/ice covered surface in the extended Arctic region (60&#176;- 90&#176;) during polar day. The results show that Arctic haze events appearing every late-winter and early spring are very well captured in AATSR derived AOTs. To validate the retrieved AOTs, results are compared with ground-based AERONET data. The comparisons revealed partially excellent agreement but also limits of the retrieval algorithm are discussed. In addition, some preliminary results of a trend analysis of the long-term record will be presented. It is foreseen to use the results in the trans-regional research project (AC)&#179; investigating Arctic amplification.References[1] Istomina, L.: Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer, PhD thesis, University of Bremen, Bremen, Germany, 2012.[2] Jafariserajehlou, S. and Mei, L. and Vountas, M. and Rozanov, V. and Burrows, J. P. and Hollmann, R., A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019, 2019.&#160;

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Evaluation and comparison of MAIAC, DT and DB aerosol products over China

10.5194/acp-2018-1339 ◽

2019 ◽

Author(s):

Ning Liu ◽

Bin Zou ◽

Huihui Feng ◽

Yuqi Tang ◽

Yu Liang

Keyword(s):

Atmospheric Correction ◽

Ground Truth ◽

Coefficient Of Determination ◽

Land Type ◽

Deep Blue ◽

Aerosol Retrieval ◽

Geometry Dependence ◽

Retrieval Algorithms ◽

Moderate Resolution ◽

Moderate Resolution Imaging Spectroradiometer

Abstract. A new Multiangle Implementation of Atmospheric Correction (MAIAC) algorithm has been applied in Moderate Resolution Imaging Spectroradiometer (MODIS) sensor and recently provides globally high spatial resolution Aerosol Optical Depth (AOD) products at 1 km. Meanwhile, several improvements are modified in classical Dark Target (DT) and Deep Blue (DB) aerosol retrieval algorithms in MODIS collection 6.1 products. However, validation and comparison for MAIAC, DT and DB algorithms is still lacking in China. In this paper, a comprehensive assessment and comparison of AOD products at 550 nm wavelength based three aerosol retrieval algorithms in MODIS sensor using ground-truth measurements from Aerosol Robotic Network (AERONET) sites over China during 2000 to 2017 is presented. In general, after quality assurance (QA) filter, the coefficient of determination (R2=0.854), correlation coefficient (R=0.929), root-mean-square error (RMSE=0.178), mean bias (Bias=0.019) and the fraction fall within expected error (Within_EE=67.10 %, EE=±(0.05+0.15×AOD)) results for MAIAC algorithm show better accuracy than those from DT and DB algorithms. While the R2, R, RMSE, Bias and Within_EE of DT algorithm are 0.817, 0.930, 0.192, 0.077, 55.36 %, respectively, those corresponding statistics for DB algorithm are 0.827, 0.921, 0.190, 0.018, 63.32 %. Moreover, the spatiotemporal completeness for MAIAC (29.69 %) product is also better than DT (8.00 %) and DB (19.50 %) products after QA filter. In addition, the land type dependence characteristic, view geometry dependence, spatiotemporal retrieval accuracy and spatial variation pattern difference for three products are also analyzed in details.

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