radiative transfer, modis and viirs, and the aeronet system north larsen north.larsen@lmco

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ASU MAT 591: Opportunities in Industry!

Radiative Transfer, MODIS and Radiative Transfer, MODIS and VIIRS, and the AERONET systemVIIRS, and the AERONET system

North LarsenNorth [email protected]@lmco.com

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Todays Presentation

The Radiative Transfer Equation has evolved into a powerful expression which is used by the community for understanding problems from global warming to the development of sensors and data products.

Today an overview of the radiative transfer equation will be presented and a discussion into some of the NASA MODIS and NPOESS VIIRS sensors data products. There will also be a discussion of the AERONET data and the suite of sun photometers globally and how they are used by NASA.

These topics are at the forefront and cutting edge of the remote sensing commmunity currently.

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Understanding the Earth’s Atmosphere

CloudsClouds

Aerosols

Water vapor

78% N2

21% O2

0.8% Ar< 0.2% trace gases

99% of Atmosphere

O3 (0-400 DU)

Temp

trop

osp

her

es t

rato

sph

ere

8–20 km

20-30 km

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Radiative Transfer is the study of the transport of radiant energy through a scattering, absorbing, and scattering medium.

The Atmosphere is divided into numerous homogeneous layers each possessing its own optical depth, single scattering albedo, temperature, and phase function.

The boundary conditions specified at the top of the atmosphere are solar input, thermal emissivity, solar zenith angle. And at the bottom of the atmosphere are surface albedo, surface temperature, and the surface emissive properties.

Radiative Transfer

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The Radiative Transfer Equation has evolved into a powerful expression which is used by the community for understanding problems from global warming to the development of sensors and data products.

Radiative Transfer

IO I

d

dI/d=Ioe-

Earth’s Surface

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The Plane Parallel Atmosphere for Radiative Transfer

sensor

o

Fs=0 a=a() T=T()

=*

P()=P()

T=T(*)

Lambertian Surface

= cos()= ’’

A=Albedo

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Radiative Transfer Equation

oePFa

TBa

IPdda

Id

dI

oos

),;,,(4

)())(())(1(

),,(),;,,(4

)(),,(

),,( 1

1

2

0

In Which: I Radiance being solved for (W/m2str-1) a Single Scattering Albedo (no units)P Phase Function (no units) Optical Depth (no units) Cosine of zenith angle (no units)B Planck function of temperature T (W/m2str-1) Fs Solar Flux input at the top of atmosphere (W/m2)

The Radiative Trnsfer Equation in Plane Parallel Atmospheres

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Atmospheric Transmittance, Surface Reflectance, Solar Irradiance, and Imaginary Part of Refractive Index for Water and Ice, Visible (VIS) and Near Infrared

(NIR)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Wavelength (microns)

Tra

ns

mit

tan

ce

/Re

fle

cta

nc

e/Ir

rad

ian

ce

1.00E-09

1.00E-08

1.00E-07

1.00E-06

1.00E-05

T_atm R_Veg R_Soil R_Snow R_Water Solar Irr K_Water K_Ice

H2O

Ima

gin

ary P

art o

f Re

frac

tive In

de

x

O3

O2H2O

O2H2O

O2

H2O H2O

H2O

O3

Visible to NIR Spectra

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Atmospheric Transmittance, Surface Reflectance, Solar Irradiance,and Imaginary Part of Refractive Index for Water and Ice, Short Wave Infrared (SWIR)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1 1.2 1.4 1.6 1.8 2 2.2 2.4 2.6 2.8 3


Tra

ns

mit

tan

ce

/Re

fle

cta

nc

e

1.00E-06

1.00E-05

1.00E-04

1.00E-03

1.00E-02

1.00E-01

1.00E+00

T_Atm R_Veg R_Soil R_Snow R_Water Solar Irr K_Water K_Ice

H2O

Irrad

ian

ce

(W c

m-2

um

)/Ima

gin

ary P

art o

f Re

frac

tive In

de

x

H2O

H2O H2OCO2

CO2

H2O

CH4

N2O

SWIR Spectra

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Atmospheric Transmittance, Surface Reflectance, Solar/Emissive Radiance Fraction for Cloud, and Imaginary Part of Refractive Index for Water and Ice, Mid Wave Infrared

(MWIR)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

3 3.2 3.4 3.6 3.8 4 4.2 4.4 4.6 4.8 5


Tra

ns

mit

tan

ce

/Re

fle

cta

nc

e/F

rac

tio

n o

f R

ad

ian

ce

1.0E-03

1.0E-02

1.0E-01

1.0E+00

T_Atm R_Veg R_Soil R_Snow R_Water Solar Emissive K_Water K_Ice

H2O

Ima

gin

ary P

art o

f Re

frac

tive In

de

x

CH4

CH4

H2O

N2O

CO2

H2O

MWIR Spectra

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Atmospheric Transmittance, Surface Emissivity, Blackbody Emittance (300 K),and Imaginary Part of Refractive Index for Water and Ice, Long Wave Infrared (LWIR)

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

5 6 7 8 9 10 11 12 13 14 15


Tra

ns

mit

tan

ce

/Em

iss

ivit

y

1.0E-04

1.0E-03

1.0E-02

1.0E-01

1.0E+00

T_Atm E_Veg E_Soil E_Snow E_Water BB 300K K_Water K_Ice

Em

ittan

ce

(W c

m-2

um

)/Ima

gin

ary P

art o

f Re

frac

tive In

de

x

H2OH2O

O3

H2O

CO2

Thermal Spectra

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Then and Now

First Image from TIROS-1 First Image from EOS-Terra

New Brunskwick and Nova Scotia (40 Years ago)

Mississippi Delta from MODISFeb 24, 2000

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MODIS Sensor (2000-2006)

MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (EOS AM) and Aqua (EOS PM) satellites. Terra's orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth's surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (0.4 – 15 microns). These data are improving our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.

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MODIS Sensors Data Products

Radiance Aerosol Product Total Precipitable Water Atmospheric Profiles Gridded Atmospheric Product Cloud Mask and coverSurface Reflectance Land Surface Temperature and EmissivityLand Cover/Land Cover ChangeGridded Vegetation Indices (Max NDVI and Integrated MVI) Thermal Anomalies, Fires, and Biomass BurningLeaf Area Index, and FPAREvapotranspirationNet Photosynthesis and Primary Productivity

Surface ReflectanceVegetation CoverSnow CoverSea and Lake Ice CoverNormalized Water-leaving RadiancePigment ConcentrationChlorophyll FluorescenceChlorophyll a Pigment ConcentrationPhotosynthetically Available Radiation (PARSuspended-Solids ConcentrationOrganic Matter ConcentrationCoccolith ConcentrationOcean Water Attenuation CoefficientOcean Primary ProductivitySea Surface TemperaturePhycoerythrin ConcentrationTotal Absorption CoefficientOcean Aerosol PropertiesClear water Epsilon

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The Great Barrier Reef (09/15/03)

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Dust Storm Southern Afganistan (09/20/03)

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Hurricane Isabel (09/17/03)

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Forest Fires in Portugal (09/16/03)

Hurricane Isabel 09/17/2003

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AERONET Network

The AERONET (Aerosol Robotic Network) is a global network of sunPhotometers which measure local the atmospheric properties hourly.

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AERONET Network

Measurements are takenOf the AOT in 7 bands, andTotal Column water vaporIs measured also.

With this data validation ofMODIS and future VIIRSAlgorithms is performed andData products are improved

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The VIIRS Sensor (2008-2018)

Visible/Infrared Imager Radiometer Suite VIIRS

DescriptionCollects visible/infrared imagery and radiometric data. Data types include atmospheric, clouds, earth radiation budget, clear-air land/water surfaces, sea surface temperature, ocean color, and low light visible imagery. Primary instrument for satisfying 26 EDRs.

SpecificationsSpecificationsMultiple VIS and IR channels between 0.3 and 12 Multiple VIS and IR channels between 0.3 and 12

micronsmicrons

Imagery Spatial Resolution: 350m @ NADIR / 700m @ Imagery Spatial Resolution: 350m @ NADIR / 700m @ EOSEOS

Heritage and Risk ReductionHeritage and Risk Reduction

POES - POES - Advanced Very High Resolution RadiometerAdvanced Very High Resolution Radiometer(AVHRR/3)(AVHRR/3)

DMSP -DMSP - Operational Linescan System (OLS) - MOLS onOperational Linescan System (OLS) - MOLS onF18-F20F18-F20

EOS - EOS - Moderate Resolution Imaging Moderate Resolution Imaging SpectroradiometerSpectroradiometer

(MODIS)(MODIS)NPP - NPP - Early validation of operational instrument andEarly validation of operational instrument and

algorithmsalgorithms

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VIIRS data products

RadianceImageryCloud Cover ImageryCloud Type ImageryIce Edge Location ImageryIce Concentration ImagerySoil MoistureAerosol Optical ThicknessAerosol Size ParameterSuspended MatterCloud Base HeightCloud Cover/LayersCloud Effective Particle SizeCloud Optical ThicknessOcean Color/ChlorophyllSea Ice Age and Motion

Cloud Top HeightCloud Top PressureCloud Top TemperatureAlbedo (Surface)Land Surface TemperatureVegetation IndexSnow Cover/DepthSurface TypeCurrentsFresh Water IceIce Surface TemperatureLittoral Sediment TransportNet Heat FluxMass LoadingActive FiresPrecipitable Water

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Why?

National Importance Civilian CommunityCivilian Community

– Timely, accurate, and cost-effective public Timely, accurate, and cost-effective public warnings and forecasts of severe weather events, warnings and forecasts of severe weather events, reduce the potential loss of human life and reduce the potential loss of human life and property and advance the national economyproperty and advance the national economy

– Support of general aviation, agriculture, and Support of general aviation, agriculture, and maritime communities aimed at increasing U.S. maritime communities aimed at increasing U.S. productivityproductivity

Military CommunityMilitary Community– Shift tactical and strategic focus from “coping Shift tactical and strategic focus from “coping

with weather” to anticipating and exploiting with weather” to anticipating and exploiting atmospheric and space environmental conditionsatmospheric and space environmental conditions

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Why?

Protect Safety of Life and PropertyImprove Accuracy of Severe Weather Warnings

Increase in hurricane Increase in hurricane landfall forecast skill landfall forecast skill will save an estimated will save an estimated $1 million per mile of $1 million per mile of coastline that does coastline that does not have to be not have to be evacuatedevacuated

Improved early Improved early warnings mitigate the warnings mitigate the devastating effects of devastating effects of floods through floods through disaster planning and disaster planning and responseresponse

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Why?

Maritime industry - Ocean Maritime industry - Ocean winds, waves, currents, and winds, waves, currents, and marine warnings and forecasts marine warnings and forecasts improve vessel routing for improve vessel routing for safety, fuel savings, and safety, fuel savings, and efficient operationsefficient operations

Commercial Commercial fishing industry fishing industry - knowledge of - knowledge of sea surface sea surface winds is critical winds is critical to shrimp to shrimp yields in the yields in the gulfgulf

Benefits to Industry

Agricultural industry - Agricultural industry - Fire monitoring, Fire monitoring, vegetation index, frost, vegetation index, frost, hail, and flood warnings hail, and flood warnings critical to productioncritical to production

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Why?Other Benefits to the World Community

Ice Ice monitorinmonitoring for g for shipping shipping and oil and oil exploratioexplorationn

NPOESS will improve ability to predict El Niño. A 60% increase in El Niño forecast skill will save $183 million per year over 12 year period

Snow cover mapping - Snow cover mapping - spring flood predictionspring flood prediction

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Thanks

• MODIS Images and information presented is from the NASA Goddard MODIS website

• NPOESS information is from the NPOESS IPO

• AERONET information is from the NASA Goddard AERONET Sun Photometer website

radiative transfer, modis and viirs, and the aeronet system north larsen north.larsen@lmco

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