Post on 20-Jun-2015
description
transcript
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
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
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 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
AERONET
MO
DIS
C006
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 10
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
AERONET
MO
DIS
C005
Clear Biases seen in the products but enhanced at 3km
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.
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.
TOAm
TOAm
TOAm
TOAmMVI
µµ
µµ
ρρ
ρρ
12.224.1
12.224.1
+
−=
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.
Obtaining surface albedos using combined MODIS – Aeronet Data
( )( )
( ) albedo spherical ic Atmospherontransmissi total downward and Upward ,
ereflectancpath,,,
,
=
=
=Δ
λ
θλ
φθθλρ
sT ud
ivatm
g
udgatmTOA s
TTρ
ρρρ
−+=1
Aeronet Op6cal Depth + MODIS Aerosol Phase Func6on consistent with AOD
Once this is done, we can Isolate Lamber6an albedo
)(
atmTOAud
atmTOAg sTT ρρ
ρρρ
−+−
=⇒
Use Aeronet AOD to fix the MODIS Aerosol Phase func6on model From this, we can get all relevant atmospheric scaPering parameters
[⌧550aer
]aeronet
! [Paer
(⇥scat
, ⌧550aer
,�)]urban-nonabs
80 90 100 110 120 130 140 150 1600
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Rh
o 0
.66
um
/ R
ho
2.1
2 u
mScattering angle
y = 1.2e-005*x + 0.77
data 1 linear
80 90 100 110 120 130 140 150 1600
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Rh
o 0
.47
um
/ R
ho
2.1
2 u
m
Scattering angle
y = 0.00059*x + 0.45
data 1 linear
Band Correla6on with ScaPering Angle
( ) ( ) ( )2:1
2120
=
Θ=
if 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
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.
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
Land Surface Spectral Ra6o
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
croplandmixed forresturban/builtdeciduous broadleaf
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
croplandmixed forresturban/builtdeciduous broadleaf
Regional Surface Spectral Ra6o C006 Surface Spectral Ra6o
Spectral Ra6os by land class
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
cropland
regionalC006
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
mixed forrest
regionalC006
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
urban/built
regionalC006
0 0.2 0.4 0.6 0.8 10
0.2
0.4
0.6
0.8
1
MVI Index
Ref
660
/ R
ef 2
120
deciduous broadleaf
regionalC006
C006 generally does bePer than C005 (correct trend) but urban land class is completely underes6mated at low MVI
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 %
AO
D C
006 -
AE
RO
NE
T A
OD
100 120 140 160 180-0.2
0
0.2
0.4
0.6
Single Scattering Angle%A
OD
C00
6 -
AE
RO
NE
T A
OD
1 1.5 2 2.5-0.2
-0.1
0
0.1
0.2
0.3
0.4
0.5
Angstrom Coefficient%
AO
D C
006
- A
ER
ON
ET
AO
D
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
AO
D C
006 -
AE
RO
NE
T A
OD
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 10
0.2
0.4
0.6
0.8
1
AERONET
MO
DIS
3km
AO
D
July 29 AQUA 1740 UTC
C006Regional
DragonNET AOD retrieval comparison
-0.5 -0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3 0.4 0.50
10
20
30
40
50
60
70
80
90
100
AOD Bias
Fre
quen
cy
AERONET AOD - C006 AODAERONET AOD - Derived AOD
Significant improvement in BIAS and negligible change in correla6on coefficient
mean_bias_C006 -‐0.0815 mean_bias_TunedAOD -‐0.0491
Bias Correc6on using Machine-‐ Learning 17
Target
Compare Machine-‐Learning:
Neural nets, SVM, RF, GP etc.
Input
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
Tes6ng Various Combina6ons
AOD+Surf_470+Surf_660_Surf2100+ScaPering AOD+Surf_470+Surf_660_Surf2100
Tes6ng Various Combina6ons
AOD + Lat+Lon+Land class AOD +Surf047_066_213+ScaPAngle+LC
• Improved correla6on observed arer bias correc6on • Correc6on on the overes6ma6on
Bias Corrected AOD show good Correla6on
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.
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
Thank you!