Assessing MODIS C006 urban corrections using the High Resolution Dragon AERONET Network
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This document discusses assessing corrections to the MODIS C06 3km aerosol product over urban areas using high-resolution AERONET data. It finds biases in the 3km product over urban sites compared to AERONET measurements. The authors aim to improve MODIS' land surface correction algorithms, which were trained on non-urban surfaces, by combining AERONET and MODIS data over sufficiently clean days. They retrieve land surface spectral ratios using this approach and apply filters to ensure minimal aerosol contamination. The improved land surface models could then provide better aerosol retrievals over urban regions.
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Assessing MODIS C006 urban corrections using the High Resolution Dragon AERONET Network
- 1. Assessing MODIS C06 Urban Correc6ons Using the High Resolu6on Dragon AERONET Network Nabin Malakar, Adam A/a, Barry Gross, Fred Moshary Op#cal Remote Sensing Lab, CCNY Min Oo CIMSS / UW-‐Madison
- 2. Mo6va6on l Aerosol Retrieval over land is greatly affected by land surface albedo (if bright enough). l MODIS land surface compensa6on algorithms for global applica6ons were trained using non urban land surface types (mixtures) such as vegeta6ons/ clays. l As urbaniza6on con6nues to increase, the differences in land surface behavior need to be bePer understood. l These issues become even more significant as higher resolu6on aerosol products such as C006 3km Aerosol Retrievals become available Single scaPering Mul6ple ScaPering Photons hit land surface And reflected back to space
- 3. AOD Bias (Dragon Network) 3km product 10km product 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 AERONET MODISC006 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 AERONET MODISC005 Clear Biases seen in the products but enhanced at 3km
- 4. Approach l We previously inves6gated the existence of high bias in AOD retrievals in C005 for significantly urbanized areas such as New York City l By combining AERONET with MODIS observa6ons over sufficiently “clean” days, it is possible to improve on the exis6ng land surface model needed to correct for land reflec6on l Applying this approach over a region is complicated by the fact that only a single AERONET sta6on is available and an assump6on that the AOD and phase func6on proper6es are homogeneous on a regional scale are clearly an issue l Using the Dragon Network allows for the poten6al of using bePer AERONET informa6on in “tuning” the surface while also providing bePer sta6s6cal valida6on. l We also inves6gate the neural network approach to correct the bias.
- 5. Opera6onal satellites retrieval over land l MODIS aerosol retrieval uses three wavelength channels (470, 660, 2120nm) l Mul6 wavelength measurements help separate fine / coarse components. l However, the surface reflec6on contaminates the signals. l To es6mate this, MODIS does the following l Assumes the long wavelength channel is insensi6ve to the atmosphere so the signal must be due only to the ground reflec6on (Rg_2120) l Once the long wavelength reflec6on is es6mated, use semi-‐empirical models taking into account how vegeta6ve the surface is to es6mate the VIS to SWIR ra6os (Rg_470)/ (Rg_2120), (Rg_660)/ (Rg_2120) l MODIS uses an index called the Modified Vegeta6on Index (MVI), which combines NIR and SWIR to es6mate vegeta6on class. l We demonstrate that these ra6os are not well represented in opera6onal algorithms and need refinement which allows bePer aerosol retrieval. TOA m TOA m TOA m TOA m MVI µµ µµ ρρ ρρ 12.224.1 12.224.1 + − =
- 6. Retrieving Land Surface Band Spectral Ra6os l The Collect 5/6 approach allows the VIS-‐SWIR ground albedo correla6on coefficients to be a func6on of surface type (urban/vegeta6on MVI) and observa6on angles (scaPering angle). l In our case, we ingest AOD from Aeronet to atmospherically correct the MODIS images l To ensure that the best surface retrieval is made, the following filters are applied – AOD < 0.2, – angstrom exponent > 1 to assure minimal aerosol contamina6on at 2.1 um – Homogeneous condi6ons (variability of AERONET AOD for +/-‐ 3 hours < 20%) which helps us extrapolate AOD over en6re domain – Mask all water pixels – For Dragon Network, we use Aeronet averages when possible to improve quality of land surface reflec6on and remove homogeneity assump6on.
- 7. Obtaining surface albedos using combined MODIS – Aeronet Data ( ) ( ) ( ) albedosphericalicAtmospher ontransmissitotaldownwardandUpward, ereflectancpath,,, , = = =Δ λ θλ φθθλρ s T ud ivatm g udg atmTOA s TT ρ ρ ρρ − += 1 Aeronet Op6cal Depth + MODIS Aerosol Phase Func6on consistent with AOD Once this is done, we can Isolate Lamber6an albedo )( atmTOAud atmTOA g sTT ρρ ρρ ρ −+ − =⇒ Use Aeronet AOD to fix the MODIS Aerosol Phase func6on model From this, we can get all relevant atmospheric scaPering parameters [⌧550 aer ]aeronet ! [Paer(⇥scat, ⌧550 aer , )]urban-nonabs
- 8. 80 90 100 110 120 130 140 150 160 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Rho0.66um/Rho2.12um Scattering angle y = 1.2e-005*x + 0.77 data 1 linear 80 90 100 110 120 130 140 150 160 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Rho0.47um/Rho2.12um Scattering angle y = 0.00059*x + 0.45 data 1 linear Band Correla6on with ScaPering Angle ( ) ( ) ( ) 2:1 2120 = Θ= i f gsiig ρλρ Mean=0.5153 std=0.0858 Mean= 0.7734 std=0.0729 Rho 0.470/ Rho2.12 Rho 0.660/ Rho 2.120 Once new correla6ons are found, we can replace the COO5 Correla6on procedures and assess retrieval of AOD (for all cases) Band Correla6on with ScaPering Angle (water mask included) shows minimal angular dependence valida/ng lamber/an assump/ons
- 9. General Rela6onship between Surface Type and the VIS/SWIR reflec6on ra6os in urban areas Regional surface data retrievals (50km x 50km) around different ci6es with AERONET at center. Note that VIS/SWIR ra6os decrease with MVI index in contradic6on to the MODIS C005 opera6onal models. (Later, we see that C006 trend is improved over C005) • When MVI is low (i.e urban), SRC’s are significantly underes6mated • The C005 model actually shows an opposite trend indica6ve of the differences between low MVI soils and urban materials • NYC is by far the most biased region over other urban areas in comparisons to other urban centers.
- 10. Anomalies in Spectral Ra6os Tuned Surface Reflec6on Ra6o Strong correla6on between urban frac6on and regionally tuned surface reflec6on ra6o Urban Land Cover Deciduous broadleaf forest
- 11. Land Surface Spectral Ra6o 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 cropland mixed forrest urban/built deciduous broadleaf 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 cropland mixed forrest urban/built deciduous broadleaf Regional Surface Spectral Ra6o C006 Surface Spectral Ra6o
- 12. Spectral Ra6os by land class 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 cropland regional C006 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 mixed forrest regional C006 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 urban/built regional C006 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 MVI Index Ref660/Ref2120 deciduous broadleaf regional C006 C006 generally does bePer than C005 (correct trend) but urban land class is completely underes6mated at low MVI
- 13. Bias Dependence on Different Factors l Small but posi6ve bias/RMSE dependence on % urban and scaPering angle l Negligible bias on C006 surface reflec6on ra6o and angstrom Coefficient. l Urban classifica6on should be ingested into high resolu6on algorithms 0 20 40 60 80 -0.2 0 0.2 0.4 0.6 Urban % AODC006-AERONETAOD 100 120 140 160 180 -0.2 0 0.2 0.4 0.6 Single Scattering Angle% AODC006-AERONETAOD 1 1.5 2 2.5 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 Angstrom Coefficient% AODC006-AERONETAOD 0.4 0.45 0.5 0.55 0.6 -0.2 -0.1 0 0.1 0.2 0.3 0.4 660 /2120 Reflectance Ratio AODC006-AERONETAOD
- 14. Case Scenario July 29 1740 UTC l Strongest correc6ons occur in urban zones l Best agreement seen when correc6on is applied l No significant correc6on in non urban area (green circle) 0 0.2 0.4 0.6 0.8 1 0 0.2 0.4 0.6 0.8 1 AERONET MODIS3kmAOD July 29 AQUA 1740 UTC C006 Regional
- 15. DragonNET AOD retrieval comparison -0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.5 0 10 20 30 40 50 60 70 80 90 100 AOD Bias Frequency AERONET AOD - C006 AOD AERONET AOD - Derived AOD Significant improvement in BIAS and negligible change in correla6on coefficient mean_bias_C006 -‐0.0815 mean_bias_TunedAOD -‐0.0491
- 17. Bias Correc6on using Machine-‐ Learning 17 Target Compare Machine-‐Learning: Neural nets, SVM, RF, GP etc. Input
- 18. Neural network 18 yk = 0 @ nX j=1 wkjxj 1 A • Also referred to as mul6 layer perceptron method, • Used widely for classifica6on or func6on approxima6on. Where ø: is the transfer func6on wkj: weight from unit j to unit k, xj : n input variables The output of the kth neuron: Inputs Hidden layer Outputs
- 19. Tes6ng Various Combina6ons AOD+Surf_470+Surf_660_Surf2100+ScaPering AOD+Surf_470+Surf_660_Surf2100
- 20. Tes6ng Various Combina6ons AOD + Lat+Lon+Land class AOD +Surf047_066_213+ScaPAngle+LC • Improved correla6on observed arer bias correc6on • Correc6on on the overes6ma6on
- 21. Bias Corrected AOD show good Correla6on
- 22. Conclusions l Assessment of 3km resolu6on products using Dragon Network shows somewhat enhanced bias in comparison to 10km l We find that the regionally tuned surface spectral ra6o model is highly correlated to several dis6nguishing land classes (Urban / deciduous broadleaf forest) l The current MVI parameter used to get the VIS channel surface albedo es6mate is qualita6vely and quan6ta6vely insufficient to separate urban land areas from other land classes (deciduous broadleaf forest) l Significant Improvement can be seen in bias reduc6on using regional land surface model with negligible differences in correla6on l Adding land classifica6on with MVI should help remove anomalies for urban retrievals. l We used the MODIS 3 km AOD products from AQUA and TERRA, and developed a machine-‐learning framework to compare and correct the remote sensing product with respect to the ground-‐based AERONET observa6ons. l We also constructed a neural network es6mator to obtain bias-‐corrected AOD product.
- 23. Future Work l Es6mate PM2.5 from the bias-‐corrected AOD l Par6culates with a diameter of 2.5 microns or less l Can have adverse health effects l Once in the body may lead to oxida6ve inflamma6on in the organs. Ref: hPp://www.airnow.gov
- 24. Thank you!