Lecture 3: Remote Sensing. Spectral signatures, VNIR/SWIR, MWIR/LWIR

Lecture 3: Remote Sensing. Spectral signatures, VNIR/SWIR, MWIR/LWIR
Lecture 3: Remote Sensing Spectral signatures, VNIR/SWIR, MWIR/LWIR Radiation models

Video Solar Balance.mp4 Jin: We failed to show this one on class, you can access it from the link above

Much of the previous discussion centered around the
Spectral signature Much of the previous discussion centered around the selection of the specific spectral bands for a given theme In the solar reflective part of the spectrum ( nm), the shape of the spectral reflectance of a material of interest drives the band selection Recall the spectral reflectance of vegetation Select bands based on an absorbing or reflecting feature in the material In the TIR it will be the emissivity that is studied The key will be that different materials have different spectral reflectances As an example, consider the spectral reflectance curves of three different materials shown in the graph Chorophyll-a absorbs red light at 680 nm (and also at 430 nm in the blue). There is no absorption in the green (550 nm) so it reflects (scatters) more light at green wavelengths. Carotenoids absorb broadly between 450 and 520 nm (blue to blue-green). Nothing absorbs in the green from about 520 nm to 630 nm (orange), which is why plants reflect green light.

These divisions are not precise and can vary depending on the publication
Visible-Near IR ( ); Mid-IR (3 - 5); Thermal IR (8 - 14); 4) Microwave ( centimeters) VNIR - visible and near-infrared ~0.4 and 1.4 micrometer (µm) Near-infrared (NIR, IR-A DIN): µm in wavelength, defined by the water absorption Short-wavelength infrared (SWIR, IR-B DIN): 1.4-3 µm, water absorption increases significantly at 1,450 nm. The 1,530 to 1,560 nm range is the dominant spectral region for long-distance telecommunications. Mid-wavelength infrared (MWIR, IR-C DIN) also called intermediate infrared (IIR): 3-8 µm Long-wavelength infrared (LWIR, IR-C DIN): 8–15 µm Far infrared (FIR): 15-1,000 µm

Spectral Signature Spectral signature is the idea that a given material has a spectral reflectance/emissivity which distinguishes it from other materials Spectral reflectance is the efficiency by which a material reflects energy as a function of wavelength The success of our differentiation depends heavily on the sensor we use and the materials we are distinguishing Unfortunately, the problem is not as simple as it may appear since other factors beside the sensor play a role, such as Solar angle View angle Surface wetness Background and surrounding material Also have to deal with the fact that often the energy measured by the sensor will be from a mixture of many different materials This discussion will focus on the solar reflective for the time being

Spectral Signature - geologic
Minerals and rocks can have distinctive spectral shapes based on their chemical makeup and water content For example, chemically bound water can cause a similar feature to show up in several diverse sample types However, the specific spectral location of the features and their shape depends on the actual sample 1 Main Entry: gyp·sum Pronunciation: \ˈjip-səm\ Function: noun Etymology: Latin, from Greek gypsos Date: 14th century 1 : a widely distributed mineral consisting of hydrous calcium sulfate that is used especially as a soil amendment and in making plaster of paris 2 : drywall

Spectral signature - Vegetation
Samples shown here are for a variety of vegetation types All samples are of the leaves only That is, no effects due to the branches and stems is included

Vegetation spectral reflectance
Note that many of the themes for Landsat TM were based on the spectral reflectance of vegetation Show a typical vegetation spectra - KNOW THIS CURVE Also show the spectral bands of TM in the VNIR and SWIR as well as some of the basic physical process in each part of the spectrum Pronunciation: \ˈklȯr-ə-ˌfil, -fəl\ Chorophyll-a absorbs red light at 680 nm (and also at 430 nm in the blue). There is no absorption in the green (550 nm) so it reflects (scatters) more light at green wavelengths. Carotenoids absorb broadly between 450 and 520 nm (blue to blue-green). Nothing absorbs in the green from about 520 nm to 630 nm (orange), which is why plants reflect green light.

Recall the graph presented earlier showing the transmittance
Spectral signature - Atmosphere Recall the graph presented earlier showing the transmittance of the atmosphere Can see that there are absorption features in the atmosphere that could be used for atmospheric remote sensing Also clues us in to portions of the spectrum to avoid so that the ground is visible

Have to keep in mind that a spectral signature is not always enough
A signature is not enough Have to keep in mind that a spectral signature is not always enough Signature of a water absorption feature in vegetation may not indicate the desired parameter Vegetation stress and health Vegetation amount Signatures are typically derived in the laboratory Field measurements can verify the laboratory data Laboratory measurements may not simulate what the satellite sensor would see Good example is the difficult nature of measuring the relationship between water content and plant health Once the plant material is removed from the plant to allow measurement it begins to dry out Using field-based measurements only is limited by the quality of the sensors The next question then becomes how many samples are needed to determine what signatures allow for a thematic measurement

This is a black spruce forest in the BOREAS experimental region in Canada.
Left: backscattering (sun behind observer), note the bright region (hotspot) where all shadows are hidden. Right: forwardscattering (sun opposite observer), note the shadowed centers of trees and transmission of light through the edges of the canopies. Photograph by Don Deering.

A soybean field. Left: backscattering (sun behind observer)
A soybean field. Left: backscattering (sun behind observer). Right: forwardscattering (sun opposite observer), note the specular reflection of the leaves. Photograph by Don Deering.

Signature and resolution
The next thing to be concerned about is the fact that we will not fully sample the entire spectrum but rather use fewer bands In this case, all four bands will allow us to differentiate clay and grass Using bands 1, 3, and 4 would also be sufficient to do this Even using just bands 3 and 4 would allow us to separate clay and grass

Band selection and resolution for spectral signatures should
Signature and resolution Band selection and resolution for spectral signatures should be chosen first based on the shapes of the spectra That is, it is not recommended to rely on the absolute difference between two reflectance spectra for discrimination Numerous factors can alter the brightness of the sample while not impacting the spectral shape Shadow effects and illumination conditions Absolute calibration Sample purity Bands showngive Gypsum - Low, high, lower Montmorillonite - High, high, low Quartz - high, high, not so high

Quantifying radiation
It is necessary to understand the energy quantities that are typically used in remote sensing Radiant energy (Q in joules) is a measure of the capacity of an EM wave to do work by moving an object, heating, or changing its state. Radiant flux (Φ in watts) is the time rate (flow) of energy passing through a certain location. Radiant flux density (watts/m2) is the flux intercepted by a planar surface of unit area. Irradiance (E) is flux density incident upon a surface. Exitance (M) or emittance is flux density leaving a surface. The solid angle (Ω in steradians) subtended by an area A on a spherical surface of radius r is A/r2 Radiant intensity (I in watts/sr) is the flux per unit solid angle in a given direction. Radiance (L in watts/m2/sr) is the intensity per unit projected area. Radiance from source to object is conserved

Radiometric Definitions/Relationships
Radiant flux, irradiance (radiant exitance), radiance The three major energy quantities are related to each other logically by examining their units In this course, we will deal with the special case Object of interest is located far from the sensor (factor of five) Change in radiance from object is small over the view of the sensor Then Φdetector = L object × Areacollector × ΩGIFOV Φdetector = E object × Areacollector E detector = L object × ΩGIFOV ΩGIFOV= AreaGIFOV/H2 ΩGIFOV= Areadetector/f2

Electromagnetic Spectrum: Transmittance, Absorptance, and Reflectance
Any beam of photons from some source passing through medium 1 (usually air) that impinges upon an object or target (medium 2) will experience one or more reactions that are summarized in this diagram

Emissivity, absorptance, and reflectance
Radiometric Definitions/Relationships Emissivity, absorptance, and reflectance All three of these quantities are unitless ratios of energy quanities Emissivity, ε, is the ratio of the amount of energy emitted by an object to the maximum that could possibly emitted at that temperature Absorptance, α, is the ratio of the amount of energy absorbed by an object to the amount that is incident on it Reflectance, ρ, is the ratio of the amount of energy reflected by an object to the is incident on it All three can be written in terms of the emitted, reflected, incident, and absorbed radiance, irradiance, radiant exitance, or radiant flux (but since above three quantities are unitless, numerator and denominator must be identical units) In terms of radiant flux we would have

Radiometric Laws - Cosine Law
Cosine Law - Irradiance on surface is proportional to cosine of the angle between normal to the surface and incident radiance E = E0cosθ In figures below, if E0 (or L0 converted to irradiance using the solid angle) is normal to the surface, we have a maximum incident irradiance For E0 that is tangent to surface, the incident irradiance is zero

Cosine effect example Graph on this page shows the downwelling total irradiance as a function of time for a single day as measured from a pyranometer

Radiometric Laws - 1/R2 Distance Squared Law or 1/R2 states that the irradiance from a point source is inversely proportional to the square of the distance from the source Only true for a point source, but for cases when the distance from the source is large relative to the size of the source (factor of five gives accuracy of 1%) Sun can be considered a point source at the earth Satellite in terrestrial orbit does not see the earth as a point source Can understand how this law works by remembering that irradiance has a 1/area unit and looking at the cases below In both cases, the radiant flux through the entire circle is same Area of larger sphere is 4 times that of the smaller sphere and irradiance for a point on the sphere is ¼ that of the smaller sphere

Radiometric Laws - Lambertian Surface
Lambertian surface is one for which the surface-leaving radiance is constant with angle It is the angle leaving the surface for which the radiance is invariant Lambertian surface says nothing about the dependence of the surface- leaving radiance on the angle of incidence In fact, from the cosine law, we know that the incident irradiance will decrease with sun angle If the incident irradiance decreases, the reflected radiance decreases as well The radiance can decrease, as long as it does so in all directions equally

Radiometric Laws - Lambertian Surface
Using the integral form of the relationship between radiance and irradiance we can show that Elambertian=¶Llambertian To obtain the irradiance we have to consider the radiance through an entire Hemisphere Because of the large range of angles, we cannot simply use E=LΩ

Radiometric Laws - Planck’s Law
States that the spectral radiant exitance from a blackbody depends only on wavelength and the temperature of the blackbody A blackbody is an object that absorbs all energy incident on it, α=1 Corrollary is that a blackbody emits the maximum of energy possible for an object a given temperature and wavelength

Radiometric Laws - Planck’s Law
Once you are given the temperature and wavelength you can develop a Planck curve Planck curves never cross Curves of warmer bodies are above those of cooler bodies

Radiometric Laws - Wien’s Law
Peaks of Planck Curves get lower and move to longer wavelengths as temperature decreases Maximum wavelength of emission is defined by Wien’s Law λmax=2898/T [μm]

Solar Radiation Sun is the primary source of energy in the VNIR and SWIR Peak of solar curve at approximately 0.45 μm Distance to sun varies from to AU Irradiance (not spectral irradiance) at the top of the earth’s atmosphere for normal incidence is 1367 W/m2 at 1 AU

Terrestrial Radiation
Energy radiated by the earth peaks in the TIR Effective temperature of the earth-atmosphere system is 255 K Planck curves below relate to typical terrestrial temperatures

Solar-Terrestrial Comparison
When taking into account the earth-sun distance it can be shown that solar energy dominates in VNIR/SWIR and emitted terrestrial dominates in the TIR Sun emits more energy than the earth at ALL wavelengths It is a geometry effect that allows us to treat the wavelength regions separately

Solar-Terrestrial Comparison
Plots here show the energy from the sun at the sun and at the top of the earth’s atmosphere Also show the emitted energy from the earth

Vertical Profile of the Atmosphere
Understanding the vertical structure of the atmosphere allows one to understand better the effects of the atmosphere Atmosphere is divided into layers based on the change in temperature with height in that layer Troposphere is nearest the surface with temperature decreasing with height Stratosphere is next layer and temperature increases with height Mesosphere has decreasing temperatures

Atmospheric composition
Atmosphere is composed of dust and molecules which vary spatially and in concentration Dust also referred to as aerosols Also applies to liquid water, particulate matter, airplanes, etc. Primary source of aerosols is the earth's surface Size of most aerosols is between 0.2 and 5.0 micrometers Larger aerosols fall out due to gravity Smaller aerosols coagulate with other aerosols to make larger particles Both aerosols and molecules scatter light more efficiently at short wavelengths Molecules scatter very strongly with wavelength (blue sky) Molecular scattering is proportional to 1/(wavelength)4 Aerosols typically scatter with 1/(wavelength) Both aerosols and molecules absorb Molecular (or gaseous absorption is more wavelength dependent Depends on concentration of material

default ozone 60-degree zenith angle and no scattering
Absorption MODTRAN3 output for US Standard Atmosphere, 2.54 cm column water vapor, default ozone 60-degree zenith angle and no scattering

Same curve as previous page but includes molecular scatter
Absorption Same curve as previous page but includes molecular scatter

More material, lower transmittance Longer path, lower transmittance
Angular effect Changing the angle of the path through the atmosphere effectively changes the concentration More material, lower transmittance Longer path, lower transmittance

having complete absorption
At longer wavelengths, absorption plays a stronger role with some spectral regions having complete absorption

Absorption

The MWIR is dominated by water vapor and carbon dioxide absorption

In the TIR there is the “atmospheric window” from 8-12 μm
Absorption In the TIR there is the “atmospheric window” from 8-12 μm with a strong ozone band to consider

Radiative Transfer Easier to consider the specific problem of the radiance at a sensor at the top of the atmosphere viewing the surface

There will be three components of greatest interest in the
Radiation components There will be three components of greatest interest in the solar reflective part of the spectrum Unscattered, surface reflected radiation Lλsu Down scattered, surface reflected Lλsd skylight Up scattered path Lλsp radiance Radiance at the sensor is the sum of these three

Radiative transfer is basis for understanding how sunlight
and emitted surface radiation interact with the atmosphere For the atmospheric scientist, radiative transfer is critical for understanding the atmosphere itself For everyone else, it is what atmospheric scientists use to allow others to get rid of atmospheric effects Discussion here will be to understand the effects the atmosphere will have on remote sensing data Start with some definitions Zenith Angle Elevation Angle Nadir Angle Airmass is 1/cos(zenith) Azimuth angle describes the angle about the vertical similar to cardinal directions

Optical depth describes the attenuation along a path in the atmosphere
Depends on the amount of material in the atmosphere and the type of material and wavelength of interest Soot is a stronger absorber (higher optical depth) than salt Molecules scatter better (higher optical depth) at shorter wavelengths Aerosol optical depth is typically higher in Los Angeles than Tucson Total optical depth is less on Mt. Lemmon than Tucson due to fewer molecules and lower aerosol loading Optical depth can be divided into absorption and scattering components which sum together to give the total optical depth δtotal = δ scatter + δabsorption Scattering optical depth can be broken into molecular and aerosol δscatter = δmolec + δaerosol Absorption can be written as sum of individual gaseous components δabsorption = δ H2 O + δO3 + δCO

Beer’s Law relates optical depth to transmittance
Optical Depth and Beer’s Law Beer’s Law relates optical depth to transmittance Increase in optical depth means decrease in transmittance Assuming that optical depth does not vary horizontally in the atmosphere allows us to write Beer’s Law in terms of the vertical optical depth 1/cosθ=m for airmass is valid up to about θ=60 (at larger values must include refractive corrections) Recalling that optical depth is the sum of component optical depths Beer’s Law also relates an incident energy to the transmitted energy

First consider the directly transmitted solar beam,
Directly-transmitted solar term First consider the directly transmitted solar beam, reflected from the ground, and transmitted to the sensor - the unscattered surface-reflected radiation, Lλsu

Need to account for the path length of the sun due to solar zenith
Solar irradiance at the ground Can also write the transmittance as an exponential in terms of optical dept Beer’s law Need to account for the path length of the sun due to solar zenith angle of the sun in computing transmittance Account for the cosine incident term to get the irradiance on the surface Recall m=1/cosθsolar Eλground, solar is the solar irradiance at the bottom of the atmosphere normal to the ground surface (shown here to be horizontal) Requires a 1/r2 to account for earth-sun distance

The surface topography will play a critical role in
Incident solar irradiance The surface topography will play a critical role in determining the incident irradiance Two effects to consider Slope of the surface Lower optical depth because of higher elevation Good example of the usefulness of a digital elevation model (DEM) and assumption of a vertical atmospheric model

model can be used to predict energy at sensor Given
Example: Shaded Relief Surface elevation model can be used to predict energy at sensor Given Solar elevation angle local topography (slope, aspect) from DEM Simulate incident angle effect on irradiance Calculate incident angle for every pixel Determine cos[θ(x,y)] Creates a “shaded- relief” image TM: Landsat thematic mapper

Reflect the transmitted solar energy from the surface
Directly-transmitted solar term Reflect the transmitted solar energy from the surface within the field of view of the sensor Once the solar irradiance is determined at the ground in the direction normal to the surface it is reflected by the surface The irradiance is converted to a radiance Conversion from irradiance to radiance is needed because we want to use the nice features of radiance Recall the relationship between irradiance and radiance derived earlier for a lambertian surface - E=¶L There is a similar relationship between incident irradiance and reflected radiance from a Lambertian surface

Last step is to transmit the radiance from the surface to
Directly-transmitted solar term Last step is to transmit the radiance from the surface to the sensor along the view path Simply Beer’s law again, except now we use the view path instead of the solar path

Reflected downwelling atmospheric
Atmosphere scatters light towards the surface and this scattered light is reflected at the surface to the sensor Compute an incident irradiance from the incident radiance due to atmospheric scattering This incident irradiance is reflected from our lambertian surface to give Still need to transmit this through the atmosphere to get the at-sensor radiance

In the shadows Image below is three-band mix of ETM+ bands 1, 4, and 7
Note that there is still energy coming from the shadows Scattered skylight - which will have a blue dominance to it

Path Radiance Term Path radiance describes the amount of energy scattered by the atmosphere into the sensor’s view Basically, any photon for which the last photon scattering event occurred in the atmosphere is a path radiance term Can include or exclude an interaction with the ground If it includes a surface interaction then this can be affected by atmospheric adjacency effects The intrinsic path radiance is the radiance at the sensor that would be measured if there were zero surface reflectance Contribution only from the atmosphere Depends only on atmospheric parameters No simple formulation Requires radiative transfer code Use Lλsp

Over water A similar effect can be seen over water
Images here are also bands 3, 4, and 7 of ETM+ (LANDSAT) Water is highly absorbing at these wavelengths thus almost all of the signal is due to atmospheric scattering Landsat 7 Enhanced Thematic Mapper (ETM)

Summing the previous three at-sensor radiances will give
At-sensor radiance in solar reflective Summing the previous three at-sensor radiances will give the total radiance at the sensor There is a huge amount of buried information in the above This is a simplified way of looking at the problem Phase function effects from scattering and single scatter albedo are contained in Edown and the path radiance Optical depths due to scattering and absorption are combined in the transmittance terms Also assumes lambertian surface!!!

Model output shows the spectral dependence of the at-
Path radiance Model output shows the spectral dependence of the at- sensor radiance for path radiance and reflected radiance

TOA radiance, VNIR/SWIR
MISR data showing the effect of view angle on TOA radiance with brightening and blue dominance at large views

Comparison between measured spectra of RRV Playa
Model versus measured Comparison between measured spectra of RRV Playa using AVIRIS and predicted radiance based on ground measurements The Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) is a facility consisting of a flight system, a ground data system, a calibration facility, and a full-time operations team. The facility was developed by the Jet Propulsion Laboratory (JPL) under funding from the National Aeronautics and Space Administration (NASA). NASA also provides funding for operations and maintenance. The flight system is a whisk-broom imager that acquires data in 224 narrow, contiguous spectral bands covering the solar reflected portion of the electromagnetic spectrum. It is flown aboard the NASA high altitude ER-2 research aircraft. The ground data system is a facility dedicated to the processing and distribution of data acquired by AVIRIS. It operates year round at JPL. The calibration facility consists of a calibration laboratory at JPL and a suite of field instruments and procedures for performing inflight calibration of AVIRIS. A small team of engineers, technicians and scientists supports a yearly operations schedule that includes 6 months of flight operations, 6 months of routine ground maintenance of the flight system, and year-round data processing and distribution. Details of the AVIRIS system, its performance history, and future plans are described. The work described in this article was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under funding from the National Aeronautics and Space Administration. The airborne visible/infrared imaging spectrometer (AVIRIS)

Results below model the at-sensor radiance compared to
Model versus measured Results below model the at-sensor radiance compared to the sensor output A raw AVIRIS spectrum (measured in digital numbers or. DN's)

TIR paths There will also be three components of greatest interest in
the emissive part of the spectrum (or TIR) Unattenuated, surface emitted radiation Lλeu Downward emitted, surface reflected skylight L λed Upward emitted path radiance Lλep Radiance at the sensor is the sum of these three Lλe = Lλeu + Lλed + Lλep

In the TIR, the problem is similar in philosophy as the
Thermal infrared problem In the TIR, the problem is similar in philosophy as the reflective Still have a path radiance, and reflected downwelling Direct reflected term in reflective is analogous to the surface emitted term in the TIR Difference is that we are now dealing primarily with emission and absorption rather than scattering Reflective we are most concerned with how much stuff is in the atmosphere and what it is Aerosol loading (Gives aerosol optical depth) Atmospheric pressure (Gives molecular optical depth) Types of aerosols (Phase function and absorption properties) Amount of gaseous absorbers (Water vapor, ozone, carbon dioxide) In the TIR we must also worry about where these things are vertically Temperature depends on altitude Emission depends on temperature Need vertical profile of termperature, pressure, and amounts of absorbers

Surface emitted term will depend upon the emissivity and
temperature of the surface attenuated along the view path Easiest assumption is to assume that the surface is a blackbody but then the temperature obtained will not correspond to the actual temperature Better assumption is to assume the emissivity and temperature are known and use Planck’s law to obtain the emitted radiance Transmitting this through the atmosphere gives

Here, the equations are identical to the reflective case
Reflected downwelling and path radiance Here, the equations are identical to the reflective case The downwelling radiance depends on atmospheric temperature and composition Equations are the same Path radiance term is same as in reflective Must be computed from radiative transfer Depends heavily on atmospheric , Use Lλsp Sum is same approach as reflective

Concepts work in the other direction as well
TOA Radiance, TIR Concepts work in the other direction as well Radiance at the sensor will depend mostly upon where the layer is that is emitting the energy seen by the sensor Location of the layer affects the temperature The warmer the layer, the higher the radiance that is emitted

ETM+ Band 6 of Tucson showing temperature effects
TIR Imagery examples ETM+ Band 6 of Tucson showing temperature effects This image is from July Note the hot roads and cool vegetation

emissivity (nearly unity)
Bright and dark water Water is dark in reflective bands but can be bright in LWIR Warm water relative to surround Water is also high emissivity (nearly unity)

Example of New Orleans shown here points out the High temperatures
Bright and dark land Example of New Orleans shown here points out the High temperatures of the urban area Water in this case is much colder than the land Little contrast in the reflective

Wavelength Interval (µm)
the LANSSAT TM consists of 7 bands that have these characteristics: Band No. Wavelength Interval (µm) Spectral Response Resolution (m) 1 Blue-Green 30 2 Green 3 Red 4 Near IR 5 Mid-IR 6 Thermal IR 120 7

Clouds seen in the TIR (band 6 left) and visible (band 3
CLASS Part.: WHY? TIR Imagery Clouds seen in the TIR (band 6 left) and visible (band 3 right) of ETM+ from July

TIR Imagery TIR “Shadows” seen in the ETM+ band 6 image left are of
far different nature than those of the band 3 shadows

TIR Imagery Canyons act as blackbody as well as have higher
temperatures due to lower elevations GOES image here shows low radiance as bright Note the Grand Canyon is plainly Visible Also evident are land- water boundaries (and not just because of the lines drawn to show them)

Lecture 3: Remote Sensing. Spectral signatures, VNIR/SWIR, MWIR/LWIR

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Lecture 3: Remote Sensing. Spectral signatures, VNIR/SWIR, MWIR/LWIR

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