QA filtering of individual pixels to enable a more accurate validation of aerosol products

Maksym PetrenkoPresented at MODIS Collection 7 and

beyond Retreat

• MAPSS supports Level 2 aerosol data from different sensors–MODIS–MISR– OMI– POLDER– CALIOP– AERONET

• MAPSS uniformly samples Level-2 aerosol products from multiple sensors over uniform areas of 55km centered around AERONET sun photometer ground stations

• Stores resulting statistics in simple CSV files

MAPSS: Multi-sensor Aerosol Products Sampling System

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Sensor ground footprint (at nadir)50

km

11/16/11

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Subset statistics

• General– Number of pixels in sample space (Ndat)– Valid pixel count (Nval)– Closest pixel value (Cval)– Mean, Median, Mode– Standard Deviation

• Spatio-temporal variability– Slope of fitted Plane or Line– Azimuth (direction) of slope– Multiple (or Linear) Correlation Coefficient11/16/11

Ndat=9Nval =6

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Additional subset data

• Geometry– Solar zenith angle– Sensor zenith angle– Scattering angle– … and so on

• Data provenance– Name of source data file– Index of the closest pixel in the data file

• Quality control / Quality assurance (QA)– Mode or mean of QA flags in the subset area– Bit mask flags are decoded and reported as plain numbers11/16/11

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Accounting for data quality

OMI data with all QA OMI data with best QAMODIS data with all QA MODIS data with best QA

11/16/11

QA experiment

• Compare means of satellite-derived AOD over 55km areas to means of AERONET AOD (interpolated) within ±30 minutes of satellite overpasses

• Filter satellite data based on QA:– No filtering (left column)– Compute mean AOD based only on pixels with Best QA

(central column)– Compute mean AOD based on all pixels in the sample,

but only if mode of QA over the whole sample is Best (right column)

MODIS AOD Land - Corrected No QA filtering Pixel QA=3 Mode QA=3

MODIS AOD Ocean Average No QA filtering Pixel QA=3 Mode QA=3

MODIS AOD Deep Blue No QA filtering Pixel QA=3 Mode QA=3

OMI No QA filtering Pixel QA=0 Mode QA=0

Observations

• For MODIS products, considering the overall data quality in the observation region produces a more accurate (but smaller) subset of the data

• Applying QA filtering to individual pixels in MODIS Land or Ocean products discards certain data points, but does not change the overall character of the data

• For OMI, pixel-based filtering produces somewhat better results, perhaps because of higher variability of measurement error in sampling areas (e.g., subpixel cloud contamination)

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Dust storm at Djougou on February 26, 2005

Source: MODIS Terra Rapid Response, AERONET Data Synergy Tool

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Daily AERONET AOD data during FEB 2005 at Djougou

Source: AERONET site

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Best-QA mean AOD from multiple sensors at Djougou

•The event was observed by multiple sensors, but most best-QA measurements underestimated the actual AOD value•Interestingly, there were no best-QA Deep Blue AOD retrievals from MODIS, but there were some from SeaWiFS

2/20/01 2/21/01 2/22/01 2/23/01 2/24/01 2/25/010

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(AERONET,MODIS and SeaWiFS at 550nm, MISR at 555nm, OMI at 500nm)

AERONET MISR MODIS Dark Target

MODIS Deep Blue OMI SeaWiFS Deep Blue

Date

AOD

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Dark Target and Deep Blue AOD from MODIS at Djougou

•AOD at Djougou is retrieved by MODIS using both Dark Target and Deep Blue algorithms• However, only Dark Target retrievals have the best quality

2/20/01 2/21/01 2/22/01 2/23/01 2/24/01 2/25/010

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10

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25 MODIS Dark Target - All QA

MODIS Dark Target - Best QA

MODIS Deep Blue - All QA

MODIS Deep Blue - Best QA

Date

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ber o

f val

id p

ixel

s in

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et

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All-QA AOD from multiple sensors at Djougou

2/20/01 2/21/01 2/22/01 2/23/01 2/24/01 2/25/010

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6 (AERONET,MODIS and SeaWiFS at 550nm, MISR at 555nm, OMI at 500nm)

AERONET MISR MODIS Dark TargetMODIS Deep Blue OMI SeaWiFS Deep Blue

Date

AOD

•A significantly larger number of lower-QA observations indicates the conditions (i.e., bright surface, high aerosol loading) were hard for most sensors•Deep Blue AOD from MODIS and SeaWiFS seem to be the most accurate in these conditions, however they should be used with caution because of the low QA•AOD retrieved by OMI on Feb.26 was the closest to the peak AERONET AOD, but was also indicated as having a low QA

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Mean Angstrom Exponent at Djougou

2/20/01 2/21/01 2/22/01 2/23/01 2/24/01 2/25/010

0.1

0.2

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0.9

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(MODIS QA=best, AERONET - L2, AERONET INV - L1.5 )

AERONET at 440-670nmAERONET INV at 870-440nmMODIS Dark Target at 470-660nm

Date

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Mean Single Scattering Albedo at Djougou

2/20/01 2/21/01 2/22/01 2/23/01 2/24/01 2/25/010.8

0.85

0.9

0.95

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(MODIS Deep Blue QA=all, OMI QA=best)

MODIS Deep Blue at 470nm OMI at 500nm

Date

SSA

Potential of QA misrepresentation• Under extreme or uncertain conditions, retrieval algorithms might have

difficulty assigning the correct QA flags, and even valid data might be marked as “Bad”

• Analysis of long-term data can help to reveal areas with systematic uncertainties in QA values, e.g. coastal areas

• For example at COVE region (coast line of Virginia), most of the sensors retrieve AOD in a close agreement to AERONET (left plot). However, only a small portion of these data has Best QA (right plot, please note a changed scale)

Summary

• During validation studies, filtering individual pixels by QA and then computing mean, rather than filtering the mean of the sample by the QA mode, might produce more accurate (albeit somewhat worse) results

• Bad-QA data with good correlation to AERONET should be investigated for a possibility of raising its score