this document is proprietary. any dispatch or disclosure of content is authorized only after written...

This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l.

11

PM MAPPER®: An air quality monitoring system from MODIS data

NGUYEN Thi Nhat Thanh1,3, BOTTONI Maurizio2, MANTOVANI Simone1,2

1 MEEO SRL. Via Saragat 9, 44122 Ferrara, Italy2 SISTEMA GmbH, Dr. Karl Lueger Platz 5, A-1010 Wien, Austria

3 University of Ferrara, Via Giuseppe Saragat 1, 44122 Ferrara, Italy


22

Outline

• OVERVIEW

• VALIDATION

• CONCLUSION


33

PM MAPPER® Overview


4

PM MAPPER®Features PM MAPPER®

Input MODIS – Moderate Resolution Imaging Spectroradiometer

Output data Aerosol Optical Thickness Map

PM2.5/10 Concentration Map

Air Quality Index Map

Land cover information

Output Resolution

3 x 3 km2

•Orbit: 705km, sun-synchronous, near-polar, circular 10:30 a.m. descending node (Terra) or 1:30 p.m. ascending node (Aqua) •2330 km (cross track) by 10 km (along track at nadir) •Spectrum region from 0.41 to 14.235 µm•Spatial resolution (250m (band 1 -2), 500m (band 3-7), 1km (band 8-36))


5

PM MAPPER® vs. MODIS product

Features PM MAPPER®

Input MODIS – Moderate Resolution Imaging Spectroradiometer

Output data AOT Map

PM Concentration Map

Air Quality Index Map

Integrated surface information

Output Resolution

3 x 3 km2

MODIS product

MODIS – Moderate Resolution Imaging Spectroradiometer

AOT map

10 x 10 km2

Modules [Modis_Flatfile]

SOIL MAPPER®

-

-

[Modified Modis_Aerosol]

[PM MAPPER]

[Modis_Flatfile]

[Modis_CloudMask]

[Modis_CloudTop]

[Modis_Profiles]

[Modis_Aerosol]


6

System Overview

AQI Map at 3x3 km2

Aerosol Optical Thickness retrieval

(MODIS algorithm)

Particulate Matter (PM) and Air Quality Index

(AQI) retrieval

Aerosol Over Ocean Algorithm

Aerosol Over Land Algorithm (Dense dark vegetation algorithm)

PM2.5 & AOT relationship *

US EPA 2006 health quality criteria

Ancillary data

Coefficient data

PM Map at 3x3 km2

Land/Water/Cloud Classification

Preprocessing Flatfile Extraction

SOIL MAPPER®

MODIS data

Lookup tables

* Gupta et al., 2006


77

MODIS data 56 classes AOT over Ocean AOT over Land

AQI map PM2.5 map

Inte

grat

ed A

OT


8

MODIS AOT vs. PM MAPPER® AOT


9

Land Cover Integration


101010

PM MAPPER® Validation


1111

Validation

• Objectives– Assess the quality of PM MAPPER® product at 3x3

km2 spatial resolutions– Assess the performance of PM MAPPER® over

different land backgrounds• Comparison: MODIS products• Data set

– Over Italy– 6 months (January 2008 – June 2008)– Selection of 15 images (out of the 180 available)

• Validation Factors – Correlation Coefficient– Number of Retrieved Pixels


1212

PM MAPPER® with 3x3 km2 resolution

Correlation Coefficient

0

0.2

0.4

0.6

0.8

1

1.2

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images

Co

rre

lati

on

Land & Ocean

Land

Ocean

• Average Correlation Coefficient over Land & Ocean: 0.88• Deviation: 0.78 – 0.95


1313

PM MAPPER® with 3x3 km2 resolution

Retrieval Pixels over L & O

0

200000

400000

600000

800000

1000000

1200000

1400000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images

Num

ber o

f Pix

els PM-L&O

IMAPP-L&O

Retrieval Pixels over Land

0100000200000

300000400000500000600000

700000800000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images

Num

ber o

f Pix

els

PM-Land

IMAPP-Land

Retrieval Pixels over Ocean

0

100000

200000

300000

400000

500000

600000

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15

Images

Num

ber

of P

ixel

sPM-Ocean

IMAPP-Ocean


14

PM MAPPER® over different backgrounds• Group 1, group 2 : poor statistics on dataset and low correlation • Group 3 : bright and dense classes• Group 4 : dark, large number of retrieval pixels, and high correlation

Group Classes Number Label Darkness AOT Pixels AOT Correlation

1 14 Bright Weak Vegetation 0 0 0

1 16 Bright Strong Shrub Rangeland 0 0 0

1 17 Dark Strong Shrub Rangeland 0 0 0

1 20 Strong Herbaceous Rangeland 0 0 0

1 36 Dark Barren Land 2 0 0 0

2 23 Bright Barren Land 1 0.989 409 0.38683900

2 31 Average Barren Land 1 1 1,450 0.53916040

2 35 Dark Barren Land 1 1 102 0.55672200

2 28 Strong Barren Land 2 0.982 2,775 0.67422529

2 38 Dark Barren Land 4 1 191 0.70847186

2 7 Dark Peat Bogs 1 99 0.70906600

2 27 Strong Barren Land 1 1 485 0.73638586

2 11 Bright Strong Vegetation 1 76 0.75544625

2 15 Dark Weak Vegetation 0.989 110 0.83535935

3 24 Bright Barren Land 2 0.163 105,765 0.62656844



15

PM MAPPER® over different backgroundsGroup Classes Number Label Darkness AOT Pixels AOT Correlation

4 21 Average Herbaceous Rangeland 1 17,985 0.74601631

4 32 Average Barren Land 2 0.999 6,436 0.74735650

4 50 Dark Range Land 1 83,288 0.76368429

4 10 Dark Strong Vegetation 1 87,393 0.76461758

4 9 Bright Peat Bogs 0.998 3,806 0.78243092

4 44 Wet land 0.996 13,829 0.78631986

4 41 Shadow Barren Land 0.978 8,914 0.79855887

4 19 Dark Average Shrub Rangeland 1 50,557 0.80909021

4 29 Strong Barren Land 3 1 52,505 0.81401573

4 40 Shadow Vegetation 0.921 76,124 0.81550220

4 18 Bright Average Shrub Rangeland 0.999 697,678 0.81552407


4 37 Dark Barren Land 3 1 129,072 0.82782053



4 48 Very Bright Average Vegetation 2 0.998 976,442 0.83837347


4 8 Mid tone Peat Bogs 1 5,225 0.84086842

4 12 Bright Average Vegetation 1 1,133,162 0.85488120

4 47 Very Bright Average Vegetation 1 1 392,399 0.85551107

4 39 Bright Rangeland 0.999 231,669 0.85601407

4 13 Dark Average Vegetation 0.999 308,450 0.86284347

4 22 Mid tone Rangeland 1 386,860 0.87782180



Conclusion and Future Work


17

Conclusion• PM MAPPER® characteristics

– Input: MODIS data

– Output: AOT, PM2.5, PM10, AQI, Land Cover information

– Consistent with MODIS standard products

• Advantages– Finer spatial resolution– Increase the number of retrieval pixels– Remove the coastline effects– Land cover classes integration


18

Future works• Improve AOT quality on some bright and dark

surfaces by statistic approach (data-driven model)

• Validate PM MAPPER® products in the comparison with ground-based sensors

• Continue to increase spatial resolution up to 1x1 km2

• Apply our approach for other existing satellite sensors (AATSR)

• Extend PM MAPPER® to future mission like Sentinel 3


191919

Thank you for your attention

20


20

CONTACTS

Via Saragat, 9. I-44122, Ferrara, ItalyTel.: +39-0532-1861501Fax: +39-0532-1861637

[email protected] www.meeo.it

21


MODIS 10 x 10 km2

PM MAPPER® 3 x 3 km2

PM MAPPER® with 3 x 3 km2 resolution

• Effectively monitoring air pollution at the finer scale (i.e. over urban areas where surface and pollution field are complex).

• Providing detailed AOT distribution maps to identify emission sources.

22


22

Background map Aerosol map

• Providing background information useful to analyze potential factors affecting the AOT retrieval algorithm.

• Providing the assessment of AOT retrieval algorithms on different backgrounds, which is valuable for algorithms’ analysis and improvements.

PM MAPPER® with background information

23


23

MODIS• MODerate resolution Imaging Spectroradiometer (MODIS)

sensors– On Polar-orbiting satellite: Terra, Aqua– 700km altitude, 2330km swath– Measure spectrum region from 0.41 – 14.235 µm

• MODIS data for PMMAPPER®– 8 Bands (depend on characteristics of Aerosol Retrieval Algorithms)

– Data Size• 1km resolution (1354 x 2030 pixels), 500m resolution, and 250m

resolution– Calibration data: Level 1B (L1B)

24


Aerosol Algorithm over Ocean - Principles

• Measured radiance = path radiance + ocean surface reflection

• Physical Factors– Ocean surface reflection

• Glitter (glitter angle)• Foam reflection:

– Independent with visible channels– Decrease to 0.8, 0.5, and 0.25 at 1.24, 1.64, and 2.13µm

• Lambertian Reflectance (Water-leaving radiance): – Affect much on reflectance of 0.47, 0.55, 0.66 µm– Almost un-affected on reflectance of other bands

– Atmosphere factors:• Cloud contamination: 0.55 µm• Dust: 0.47, 0.66 µm• Cloudy: 0.47 µm• Cirrus cloud: 1.38, 1.24 µm

Ocean surface reflection is almost un-affected on some special bands

Choosing special bands to eliminate atmosphere factors

25


Aerosol Algorithm over Ocean - Principles

• Define Aerosol models– Bi-modal log-normal distribution

• Present Radiance is detected by satellite

• Estimate Aerosol models to minimize the quantity

cL

sl

),,()1(),,(),,( vvsl

vvss

vvsc LLL

Small model Large model

2

1

)()()(

j

j

dr

rdN

dr

rdNrn

Where is single-mode log-normal distribution function

dr

rdN j )(

n

j vvsm

vvsc

vvsm

sl j

jj

L

LL

n 1

2

01.0),,(

),,(),,(1

sl

26


Aerosol Algorithm over Ocean

AOT

Masking pixel by pixel

-Reflectance-Averaged reflectance for the box-Solar zenith angle-Lookup reflectance-Scattering angle

Interpolate and calculate associated parameters

LUT• Associated parameters• Aerosol Model Parameters• Model Contribution Parameters • Optical Thickness

-Spatial variability (0.55)-Dust call back (0.47, 0.66)-Cloudy (0.47)-IR test-Cirrus cloud test (1.38, 1.24)-Sediment maskDiscard brightest 25% & darkest 25%

(0.86)

Enough good pixels & condition?

0.47, 0.55, 0.66, 0.86, 1.24, 1.38, 1.64, 2.13µm

Derive the optical thickness

Estimate Aerosol model (small and large size distribution)

27


Aerosol Algorithm over Land - Principles

• Separate path radiance from measured radiance of satellite

• Conditions– Contribution of ρ* from path

radiance is large• Shorter wavelengths• Low values of surface reflectance

(ρ <0.06)

– Small uncertainty of path radiance • From +-0.005 to +- 0.01

)1(

),,()()(),,(),,(

'00

00*

p

pTFpp da

Measured radiance Path radiance Reflection radiance from surface

28


Aerosol Algorithm over Land - Principles

• Physical Factors– The scattering & absorption effect dominates to surface

reflection on the dark surface (surface reflectance ρ <0.06)

– Surface reflectance is correlated to some extent

• Soil: 0.47, 0.66, 2.1, and 3.8 µm

• Vegetation: visible channels and IR channels

• Wet soil: visible channels and 2.1 and 3.8 µm

• Dark pixels is located by mid-IR (2.1 or 3.8 µm)

– Aerosol effect is much smaller in the mid-IR (2.1 µm) than in the visible bands

• Dark pixels are used to derive Aerosol

• Red & blue bands are used to AOT derivation

29


Aerosol Algorithm over Land

AOT

LUT

Average Land Reflectance-Detect and delete cloud pixels-Identify dark pixels (2.1µm)-Remove 50% brightest & 20% darkest pixels (0.66µm)

0.47, 0.66, 2.12, 3.8 µm

Determine the aerosol model

Compute and interpolate the associated parameters

Derive non-cloudy AOT from MODIS measured radiances

-Continental aerosol-Biomass burning-Industrial/urban aerosol-Dust aerosol

Fail 1, change cloud thresholdFail 2

Success

LUT

-Scattering angle-Lookup Reflectance -Path radiance, slope, error

If (dark pixels > 10%), calculate the average reflectance

-Spatial variability

this document is proprietary. any dispatch or disclosure of content is authorized only after written...

Documents