Atm. Corr.Scene selection

Image ResamplingGeo-correction

SpectralResponse

AC analysisSurf. Refl.

TOA Rad. Refl.L8_OLI vs S2_MSI

OLI AC MitigationOLI – Planet

Rad. Correction

≈ 𝑨𝒕𝒎 + 𝑺𝒖𝒓𝒇𝒇( , 𝑨𝒕𝒎, 𝝆)

SZA Gases ( . . . , O3 , H2O )VZA Pressure∆AA Aerosol ( Type, AOT )

TOAR

Atm.

Surf.

SR

PlanetScope GEOTIFF SR file information{"atmospheric_correction": {"aerosol_model": "continental""aot_coverage": 0.5"aot_mean_quality": 1.0"aot_method": "fixed""aot_source": "mod09cma""aot_status": "Data Found""aot_std": 0.0021659541055997905"aot_used": 0.01755555470784505"atmospheric_correction_algorithm":"6Sv2.1""atmospheric_model": "water_vapor_and_ozone""luts_version": 3"ozone_coverage": 0.5"ozone_mean_quality": 255.0"ozone_method": "fixed""ozone_source": "mod09cmg""ozone_status": "Data Found""ozone_std": 0.0"ozone_used": 0.2924999925825331

"satellite_azimuth_angle": 0.0"satellite_zenith_angle": 0.0"solar_azimuth_angle": 150.32522363"solar_zenith_angle": 55.4013318347"sr_version": "1.0""water_vapor_coverage": 0.5"water_vapor_mean_quality": 1.0"water_vapor_method": "fixed""water_vapor_source": "mod09cma""water_vapor_status": "Data Found""water_vapor_std": 0.0099380845107"water_vapor_used": 0.51888889736}}

100 x 100( 30 m pixel )

1000 x 1000( 3 m pixel)

L8_OLI

PlanetScope300 x 300 ( 10 m pixel )

S2 MSI

?

Year Month Date

L8 OLI

100 x 100( 30 m pixel )Center : AERONET

1000 x 1000( 3 m pixel)

L8_OLI PlanetScope

E0

N0

E0N0

Image resamplingE0+30

N0+300 100

No Image ShiftL8_OLI30m native

PlanetScopeResampled to 30m

Excessive dispersion Relative georeferencing error

100 x 100( 30 m pixel )Center : AERONET

1000 x 1000( 3 m pixel)

L8_OLI PlanetScope

E0

N0

E0+∆E

N0+∆N

Image Shift( ∆E, ∆N ) E0+∆E+30

N0+∆N+300 100

Easting : -3 pixel ( - 9 m )

Northing : 8 pixel ( 24 m )

Spatial shift : L8_OLI vs Planet ∆E=-30 ∆E=+30

∆N=+30

∆N=-30

-3, 8

-3, 8

-3, 8

-3, 8

Image Shift( 0, 0 ) ( -3, 8 )

+ 𝜕𝑁𝐷𝑉𝐼

𝜕𝜌𝑁𝐼𝑅|𝜌𝑅,𝜌𝑁𝐼𝑅 · σ𝜌𝑁𝐼𝑅

2

= 𝜕𝑁𝐷𝑉𝐼

𝜕𝜌𝑅|𝜌𝑅,𝜌𝑁𝐼𝑅 · σ𝜌𝑅

2σ𝑁𝐷𝑉𝐼

2

Strictly speaking, mismatch in SR will cause error in the downstream application

SR apparent mismatch

Other reasons for the mismatch?

* Spectral Response * Atmospheric Correction* Improper Radiometric Correction

( Stray light, post-launch drift )

* Georeferencing error ? Checked

S R

0

0.2

0.4

0.6

0.8

1

400 450 500 550 600 650 700 750 800 850 900

rela

tive

sp

ect

ral r

esp

on

se [

]

wavelength [nm]

In-Band Band-Average Relative Spectral Response

0

0.2

0.4

0.6

0.8

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

Blue_0f10 Green_0f10 Red_0f10 NIR_0f10Blue_0c0d Green_0c0d Red_0c0d NIR_0c0d

Landsat 8 OLI

Planet

Sentinel2 MSI

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI vs Planet

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI vs Planet

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI < Planet

𝜌 =∑ℜ𝑖𝜌𝑖∑ℜ𝑖

𝜌 =𝜌 𝜆 ∙ ℜ 𝜆 ∙ 𝑑𝜆

ℜ 𝜆 ∙ 𝑑𝜆

0.201 0.212

0.0940.106

5 %sand

10%veg+soil

20%DDV

0.0390.048

0.0410.042

2 %Dark soil OLI

Planet

DDV

dry V

Sand

No constant conversion! class-dependent

Interoperability ???( hardware protocol )

Asphalt

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI > Planet

0.250 0.236

0.1500.133

7 %

14%

25%0.0860.068

OLI

Planet

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI vs Planet

0.272 0.266

0.1480.146

≈

≈

20%0.0440.053

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.4 0.45 0.5 0.55 0.6 0.65 0.7 0.75 0.8 0.85 0.9

OLI vs Planet

0.3060.299

0.5610.468

≈

20%

≈0.7050.693

Spectral Response Function IssueThe Correction factor should be different for

* varying reflectance (dark to bright) * different classes (spectral shape)* different bands (channels) Ideally, (1) extensive modeling study (2)classification(3) apply varying degree of correction based on (1) & (2)

( it is very difficult, if not impossible ) Let’s leave it uncorrected. This effect definitely adds

substantial uncertainty in SR comparison

Atmospheric Correction

Non-vegetated wet dark soil pixel

Vegetation pixel

Bare soil pixel

Non-vegetated wet dark soil pixel

AOT = 0.01 0.05 0.1 0.15

If AOT SR If AOT SR

AOT = 0.02 AOT = 0.035

AOT (CWV, O3) in ACshould be no issue !

TOAR SR

SR

TOAR

SR

TOARSoil

Veg

Soil

Veg

Root cause isnot AC,but Radiometry.

Radiance Invariance law

TOA Radiance : L8_OLI vs PlanetScope

OLI vs MSI Easting : -1 pixel ( -10 m )

Northing : 2 pixel ( 20 m )

Spatial shift : L8_OLI vs MSI

OLI vs PlanetEasting : -3 pixel ( - 9 m )

Northing : 8 pixel ( 24 m )

(One day ahead,similar local time)

(0, 0) No Image Shift (-1, 2) (-10m, 20m) Image Shift

TOA Radiance TOAR

Pre-launch calibrationOn-orbit monitoring, correctionStraylight correction, …

Earth-Sun distanceSolar zenith angleSolar Flux (irradiance) all, precisely known

~ no uncertainty

SRTOAR

Overall spectral pattern looks decent.It is not easy to really screw up AC !!!

Atm. Corr.

TOARTOAR

𝐿𝑔𝑟𝑜𝑢𝑛𝑑, 𝑜𝑢𝑡𝑔𝑜𝑖𝑛𝑔

𝐿𝑠𝑒𝑛𝑠𝑜𝑟400𝑘𝑚, 𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔

Atmosphere

𝐿𝑠𝑒𝑛𝑠𝑜𝑟700𝑘𝑚, 𝑖𝑛𝑐𝑜𝑚𝑖𝑛𝑔

Radiance Invariance Law

𝑓 ≈ 1/𝑛2

𝐿𝑔2 ∙ 𝑓 = 𝐿400

2

𝐿𝑔2 ∙ 𝑓 = 𝐿700

2

Regardless of* Altitude* Aperture* IFOV

Superb spatial resolutionHigh temporal frequency

But, Poor radiometric quality

Global radiometric correction??

TOAR

175 +

ID=1018

Sep. 8 2018WRS2 (Path=033)

Denver, CO

Correction coefficients derived from Denver imagefor Camera ID 1018

Crestone, CO

Rapid City, SD

Cheyene, WY

LaSRC (OLI AC) :(1) solar & view geometry (θs,θv,Δφ)

(2) image-derived ( τa550 )

(3) external data (P, H2O , O3)

Pressure(DEM)

H2OMODIS

OzoneMODIS

MODIS

Forget about external data (water vapor, ozone)but, focus on the AOT algorithm!

DEM7200 x 3600

GSD = 5.5km (along equator, D = 12800 km)

Lat/Lon gridΔ=0.05o

Denver

Sioux Falls

[ Meter ]𝑃 ℎ = 1013𝑒−ℎ(

𝑔𝑅𝑇

)

= 𝟏𝟎𝟏𝟑𝑒−ℎ/8500

?

Realistic sea-level pressure range ( 1010 – 1030 mb)

P = 1000 mbP = 1050 mbSZA = 20o

P = 1000 mbP = 1050 mbSZA = 70o

SZA, VZA ?

𝑃 ℎ = 𝟏𝟎𝟏𝟑𝑒−ℎ(𝑔𝑅𝑇

)

OZONE

Scale factor: 400

[cm atm]

300 DU

340 DU

200 DU( 0.2 atm cm )

500 DU( 0.5 atm cm )

Chappuis absorption

200 DU

400 DU

200 DU300 DU400 DU

Ozone Sensitivity Analysis : 200 - 400 DU

Vegetation Dark soil Brighter soil

Strong CHL signature

Strong Ozone absorptionLow Ozone Transmission

Weak CHL signature

Weak Ozone absorptionHigh Ozone Transmission

Varying vegetation signature strength (spatio-temporal)& varying ozone amount mutually compensate!

No unique ozone solution ! infinite solutionsVegetation pixel is not useful for ozone inversion !

Underestimated Ozone

Overestimated Ozone

Sand (soil, road, asphalt, … ) with straight spectral feature

may provide a chance to estimate ozone!

The question is : Does a (sand, soil-like) spectra have a straight or constant universal spectral curvature ?

4 known bands : B1, B2, G, R2 unknowns : AOT, ozone

Non-liner optimization

Simultaneous solution ofAOT & ozone

2018

May

June

July

August

July 7, 2018

July 23, 2018

AOT = 0.07

AOT = 0.08

CWV = 2.2

Ozone= 300

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

0.229

0.261

0.331

0.360

0.308

+ 7.6 %

210310410

O3

Ground truth ozone = 300 DUEM-based estimation = 310 DU

If spectral signature is (pseudo) invariant Accurate ozone estimation is possible!

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

5-Jul 23-Jul 15-Aug 2-Sep

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

28-May 14-Jun 26-Jul 8-Aug

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

11-Apr 30-Apr 22-May 1-Jun

Surface Reflectance

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

RVUS_avg

BTCN_avg

GONA_avg

TOAR = 𝒇( 𝑨𝑶𝑻,𝑶𝒛𝒐𝒏𝒆, 𝑪𝒔)

4 known bands : B1, B2, G, RNon-liner optimization

( Levenberg-Marquardt )

Ozone = 347(regular pixel)

Ozone = 420(brighter pixel)

0.15

0.2

0.25

0.3

0.35

400 450 500 550 600 650 700

5-Jul 23-Jul 15-Aug 2-Sep

RVUS yearly Ozone range : 280 – 320

negligible difference

Uncertainty due to using global average

200 - ( 300 ) – 400 DU 32.3 - (33) - 33.7

MODISOzone

305 DU

320 DU

200 ( 300 ) 400 DU32.3 (33%) 33.7 ( 2%)

270 (350) 430 DU: less than 1% error !!!

T2-UncertaintySummaryReport-RVUS.pdf

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

5-Jul 23-Jul

15-Aug 2-Sep

BOA Reflectance (Railroad Valley Playa)(Intra-day & daily variation combined)

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

400 450 500 550 600 650 700

Pseudo-Invariant ?

Summary : Ozone mitigation

(1) Sand-like EM-based approach allows ozone estimation(2) Unique spectral curvature does not exist (only site specific PI)(3) Blind use of global average ( 300-350 DU) creates

acceptable error ( ~ 1% relative error)(4) Considering TOA & BOA measurement uncertainty ( ~4-5%)

1% relative reflectance error is definitely acceptable!(5) No worry, there always will be ozone satellite!

Water vapor (CMG)

Scale factor : 200

[ g /cm2 ]

0.3

2.5

0

0.2

0.4

0.6

0.8

1

1.2

0.35 0.45 0.55 0.65 0.75 0.85 0.95

column water vapor 0.3 cm

column water vapor 3.0 cm

[g/cm2]

[g/cm2]

H2O [g/cm2]0.51.02.03.04.0

1 g/cm2 5 g/cm2

L8 OLI bands immune to the water vapor !!

SWIR2.1 bandH2O sensitivity

Need for 940 nm H2O bandin future L10