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This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 1 PM MAPPER®: An air.

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Presentation on theme: "This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 1 PM MAPPER®: An air."— Presentation transcript:

1 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 1 PM MAPPER®: An air quality monitoring system from MODIS data NGUYEN Thi Nhat Thanh 1,3, BOTTONI Maurizio 2, MANTOVANI Simone 1,2 1 MEEO SRL. Via Saragat 9, Ferrara, Italy 2 SISTEMA GmbH, Dr. Karl Lueger Platz 5, A-1010 Wien, Austria 3 University of Ferrara, Via Giuseppe Saragat 1, Ferrara, Italy

2 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 22 Outline OVERVIEW VALIDATION CONCLUSION

3 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 3 PM MAPPER® Overview

4 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 4 PM MAPPER® FeaturesPM MAPPER® InputMODIS – Moderate Resolution Imaging Spectroradiometer Output dataAerosol Optical Thickness Map PM 2.5/10 Concentration Map Air Quality Index Map Land cover information Output Resolution 3 x 3 km 2 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 µm Spatial resolution (250m (band 1 - 2), 500m (band 3- 7), 1km (band 8- 36))

5 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 5 PM MAPPER® vs. MODIS product FeaturesPM MAPPER® InputMODIS – Moderate Resolution Imaging Spectroradiometer Output dataAOT Map PM Concentration Map Air Quality Index Map Integrated surface information Output Resolution 3 x 3 km 2 MODIS product MODIS – Moderate Resolution Imaging Spectroradiometer AOT map 10 x 10 km 2 Modules[Modis_Flatfile] SOIL MAPPER® - [Modified Modis_Aerosol] [PM MAPPER] [Modis_Flatfile] [Modis_CloudMask] [Modis_CloudTop] [Modis_Profiles] [Modis_Aerosol]

6 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 6 System Overview AQI Map at 3x3 km 2 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) PM 2.5 & AOT relationship * US EPA 2006 health quality criteria Ancillary data Coefficient data PM Map at 3x3 km 2 Land/Water/Cloud Classification Preprocessing Flatfile Extraction SOIL MAPPER® MODIS data Lookup tables * Gupta et al., 2006

7 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 77 MODIS data 56 classes AOT over Ocean AOT over Land AQI map PM 2.5 map Integrated AOT

8 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 8 MODIS AOT vs. PM MAPPER® AOT

9 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 9 Land Cover Integration

10 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 10 PM MAPPER® Validation

11 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 11 Validation Objectives –Assess the quality of PM MAPPER® product at 3x3 km 2 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

12 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 12 PM MAPPER® with 3x3 km 2 resolution Average Correlation Coefficient over Land & Ocean: 0.88 Deviation: 0.78 – 0.95

13 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 13 PM MAPPER® with 3x3 km 2 resolution

14 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 GroupClasses NumberLabelDarknessAOT PixelsAOT Correlation 114Bright Weak Vegetation Bright Strong Shrub Rangeland Dark Strong Shrub Rangeland Strong Herbaceous Rangeland Dark Barren Land Bright Barren Land Average Barren Land 111, Dark Barren Land Strong Barren Land , Dark Barren Land Dark Peat Bogs Strong Barren Land Bright Strong Vegetation Dark Weak Vegetation Bright Barren Land , Bright Barren Land ,

15 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 15 PM MAPPER® over different backgrounds GroupClasses NumberLabelDarknessAOT PixelsAOT Correlation 421Average Herbaceous Rangeland117, Average Barren Land , Dark Range Land183, Dark Strong Vegetation187, Bright Peat Bogs0.9983, Wet land , Shadow Barren Land0.9788, Dark Average Shrub Rangeland150, Strong Barren Land 3152, Shadow Vegetation , Bright Average Shrub Rangeland , Strong Barren Land , Dark Barren Land 31129, Strong Barren Land , Bright Barren Land , Very Bright Average Vegetation , Average Barren Land 41206, Mid tone Peat Bogs15, Bright Average Vegetation11,133, Very Bright Average Vegetation 11392, Bright Rangeland , Dark Average Vegetation , Mid tone Rangeland1386, Average Barren Land 31326,

16 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 16 Conclusion and Future Work

17 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 17 Conclusion PM MAPPER® characteristics –Input: MODIS data –Output: AOT, PM 2.5, PM 10, 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 km 2 Apply our approach for other existing satellite sensors (AATSR) Extend PM MAPPER® to future mission like Sentinel 3

19 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 19 Thank you for your attention

20 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 20 CONTACTS Via Saragat, 9. I-44122, Ferrara, Italy Tel.: Fax:

21 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 21 MODIS 10 x 10 km 2 PM MAPPER® 3 x 3 km 2 PM MAPPER® with 3 x 3 km 2 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 23 MODIS MODerate resolution Imaging Spectroradiometer (MODIS) sensors –On Polar-orbiting satellite: Terra, Aqua –700km altitude, 2330km swath –Measure spectrum region from 0.41 – 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 Small model Large model Where is single-mode log-normal distribution function

26 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 mask Discard 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 to Measured radiancePath radiance Reflection radiance from surface

28 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 This document is proprietary. Any dispatch or disclosure of content is authorized only after written authorization by MEEO S.r.l. 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 threshold Fail 2 Success LUT -Scattering angle -Lookup Reflectance -Path radiance, slope, error If (dark pixels > 10%), calculate the average reflectance -Spatial variability


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