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

Gupta, Pawan; Levy, Robert C.; Mattoo, S.; Remer, L. A.; Munchak, L. A.

doi:10.5194/amt-9-3293-2016

Cited by 173 publications

(118 citation statements)

References 37 publications

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“…The method used to determine surface reflectance from multi-year samples in the V2 algorithm is applied to all pixels identically regardless of surface type, which can result in a bias that varies with NDVI. The positive bias over urban areas is similar to that of the MODIS Collection 6 DT AOD Gupta et al, 2016). The positive bias of V1 ocean AOD is generally lower using the V2 algorithm because the 500-600 nm channels that are strongly affected by ocean bio-optical property variance are not used in the V2 ocean algorithm.…”

Section: Bias As a Function Of Ndvisupporting

confidence: 53%

“…The MB of τ MDB is closest to zero, and τ MDT has a positive MB of 0.043. The overestimation of τ MDT has been attributed to the urbanization effect of the biased reflectance estimation and has been corrected in the MODIS DT research algorithm (not used here) using the modified urban surfacereflectance algorithm (Gupta et al, 2016). The GOCI V2 land AOD results can be recategorized as coastal or inland according to whether each site is collocated with both GOCI ocean and land AOD or with GOCI land AOD only.…”

Section: Validation Of Goci Yaer V2 Land Aod and Comparison With Othementioning

confidence: 99%

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GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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Abstract. The Geostationary Ocean Color Imager (GOCI) Yonsei aerosol retrieval (YAER) version 1 algorithm was developed to retrieve hourly aerosol optical depth at 550 nm (AOD) and other subsidiary aerosol optical properties over East Asia. The GOCI YAER AOD had accuracy comparable to ground-based and other satellite-based observations but still had errors because of uncertainties in surface reflectance and simple cloud masking. In addition, near-real-time (NRT) processing was not possible because a monthly database for each year encompassing the day of retrieval was required for the determination of surface reflectance. This study describes the improved GOCI YAER algorithm version 2 (V2) for NRT processing with improved accuracy based on updates to the cloud-masking and surface-reflectance calculations using a multi-year Rayleighcorrected reflectance and wind speed database, and inversion channels for surface conditions. The improved GOCI AOD τ G is closer to that of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) AOD than was the case for AOD from the YAER V1 algorithm. The V2 τ G has a lower median bias and higher ratio within the MODIS expected error range (0.60 for land and 0.71 for ocean) compared with V1 (0.49 for land and 0.62 for ocean) in a validation test against Aerosol Robotic Network (AERONET) AOD τ A from 2011 to 2016. A validation using the Sun-Sky Radiometer Observation Network (SONET) over China shows similar results. The bias of error (τ G − τ A ) is within −0.1 and 0.1, and it is a function of AERONET AOD and Ångström exponent (AE), scattering angle, normalized difference vegetation index (NDVI), cloud fraction and homogeneity of retrieved AOD, and observation time, month, and year. In addition, the diagnostic and prognostic expected error (PEE) of τ G are estimated. The estimated PEE of GOCI V2 AOD is well correlated with the actual error over East Asia, and the GOCI V2 AOD over South Korea has a higher ratio within PEE than that over China and Japan.

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Section: Bias As a Function Of Ndvisupporting

confidence: 53%

Section: Validation Of Goci Yaer V2 Land Aod and Comparison With Othementioning

confidence: 99%

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

et al. 2018

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“…This includes the DT aerosol retrieval for MOD04. The DT team used the opportunity to make modest improvements to the retrieval algorithm, 10 including A) to include the corrections for urban surfaces (Gupta et al, 2016), and B) to revise the logic regarding detecting/rejecting ocean pixels using the 1.63 µm band, and additional diagnostic changes that did affect the output retrieved AOD or AE. Therefore, when applied to C6 L1B input (not corrected with C6+), there are minimal global differences between the C6.1 and C6 DT aerosol retrievals.…”

Section: Collection 61mentioning

confidence: 99%

Exploring systematic offsets between aerosol products from the two MODIS sensors

Levy¹,

Mattoo²,

Sawyer³

et al. 2018

Preprint

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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 15 land and ocean, and Angstrom Exponent (AE derived from 0.55 and 0.86 µm) 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 20 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 25 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 30 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.Atmos. Meas. Tech. Discuss., https://doi

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“…As discussed in Section 5.1, the properties of the surface reflectance in urban areas might not be well represented in the DT 5 retrievals. The problem with urban surfaces in DT is a well-known problem and in Gupta et al (2016b) a modified surface reflectance relationship was proposed to be used over urban areas. BDT algorithm seems to better handle the urban surfaces than the DT algorithm and carries out the AOD retrieval with similar accuracy as for the surrounding regions.…”

Section: Retrieval Over Urban Areasmentioning

confidence: 99%

“…In addition, we evaluate the BDT posterior uncertainty estimates by comparing them to the discrepancies between AERONET and BDT algorithm AODs. by the urban surface that probably is not well described by the DT surface reflectance relationships used in the operational 5 retrieval (Gupta et al, 2016b). The overestimation of AOD over urban areas due to surface may cause significant biases, for example, to the results of satellite-based air quality studies.…”

mentioning

confidence: 99%

Bayesian Dark Target Algorithm for MODIS AOD retrieval over land

Lipponen

Mielonen

Pitkänen

et al. 2017

Preprint

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Abstract. We have developed a Bayesian Dark Target (BDT) algorithm for the Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth (AOD) retrieval over land. In the BDT algorithm, we simultaneously retrieve all 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 550 nm, fine-mode fraction (FMF), and surface reflectances at four different wavelengths (466, 550, 644, and 2100 nm). The The results show that the BDT 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 BDT 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, 10FMF and surface reflectance, the BDT also gives pixel-level posterior uncertainty estimates for the retrieved parameters. The BDT 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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A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm

Cited by 173 publications

References 37 publications

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

GOCI Yonsei aerosol retrieval version 2 products: an improved algorithm and error analysis with uncertainty estimation from 5-year validation over East Asia

Exploring systematic offsets between aerosol products from the two MODIS sensors

Bayesian Dark Target Algorithm for MODIS AOD retrieval over land

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