Digital Imaging and Remote Sensing Laboratory Spectral Signatures

Digital Imaging and Remote Sensing Laboratory Spectral Sources 2 Hyperspectral Imagery: MISI

Digital Imaging and Remote Sensing Laboratory Spectral Sources 3 Radiation Propagation Energy Paths

Digital Imaging and Remote Sensing Laboratory Spectral Sources 4 Radiation Propagation The spectral radiance reaching an aerial or satellite sensor in the UV through LWIR region can be expressed in simplified form as: uedeTusds S FEDCBA LrLLLrLr E L LLLLLLL   cos  

Digital Imaging and Remote Sensing Laboratory Spectral Sources 5 In the reflective region (0.4-3  m) this can be approximated as: and in the LWIR and the MWIR at night an approximate expression is: CBA LLLL  fED LLLL  Radiation Propagation

Digital Imaging and Remote Sensing Laboratory Spectral Sources 6 The effective radiance (L) reaching a sensor for a given channel can be expressed as: where: ( ) is the peak normalized spectral response of the sensor i.e. R ˆ dRLL)( ˆ 0    max )( ˆ R R R  Radiation Propagation

Digital Imaging and Remote Sensing Laboratory Spectral Sources 7 The effective in band radiance is more commonly used in imaging spectroscopy and is expressed as:   dRdRLLL eff )(/)( Radiation Propagation

Digital Imaging and Remote Sensing Laboratory Spectral Sources 8 Radiation Propagation

Digital Imaging and Remote Sensing Laboratory Spectral Sources 9 Characteristics of Spectral Data

Digital Imaging and Remote Sensing Laboratory Spectral Sources 10 Characteristics of Spectral data solids liquids gasses Grass asphalt roofing Brick 1.0

Digital Imaging and Remote Sensing Laboratory Spectral Sources 11 Characteristics of Spectral data solids liquids gasses Irondequoit Bay Lake Ontario Genesee River 0.50

Digital Imaging and Remote Sensing Laboratory Spectral Sources 12 Characteristics of Spectral data gasses WAVENUMBER [cm -1 ]

Digital Imaging and Remote Sensing Laboratory Often in MWIR and LWIR but particularly when studying gases we use wave numbers as a means of expressing spectral values. The wave number is expressed as: i.e. how many wavelengths fit in 1 cm wave number [cm] 1  v

Digital Imaging and Remote Sensing Laboratory Spectral Sources 14 wave number So 3  m is for 10  m      cm m m m m3 v        cm m m10 v  

Digital Imaging and Remote Sensing Laboratory Spectral Sources 15 Absorption spectra of various atmospheric constituents H2OH2O O3O3 CO

Digital Imaging and Remote Sensing Laboratory Spectral Sources 16 Absorption spectra of various atmospheric constituents CO 2 CH 4 N2ON2O

Digital Imaging and Remote Sensing Laboratory Spectral Sources 17 Absorption spectra of various atmospheric constituents Overall Atmospheric Transmission O2O2

Digital Imaging and Remote Sensing Laboratory Spectral Sources 18 Characteristics of Spectral data: Sources of Absorption Spectra electron transition rotation and vibration harmonics

Digital Imaging and Remote Sensing Laboratory Spectral Sources 19 Signatures Below 1  m In minerals, the absorption features are largely influenced by transition metals, particularly iron which is very common. Charge transfer bands that result from electron exchange between neighboring metal ions create strong absorption features in the UV. The wings of these bands account for the general increase in reflectance with wavelength in the visible for most minerals

Digital Imaging and Remote Sensing Laboratory Spectral Sources 20 Signatures (cont’d) (from Pieters & Englert,1993)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 21 Signatures (cont’d) Combination bending and stretching overtones of the fundamental OH vibration at 2.74  m cause features between 2.1 and 2.4  m. Overtones for H 2 O and CO 3 also occur in this region. As we move through SWIR and into the MWIR, the spectra are rich with overtones and fundamentals of vibrational and rotational transitions. However, the thermal signature begins to mask absorption features and must be dealt with before emissive/absorptive spectra can be clearly observed.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 22 Spectroscopy of Materials Observable spectra in the VIS-SWIR may be due to: – electron transitions in molecules and crystals – vibration transitions in molecules and crystals – electronic transition between atoms Electronic transitions are generally in the VIS-NIR Vibrational transitions are usually further into the IR with overtones and combinations in the NIR and SWIR.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 23 Spectroscopy of Materials (cont’d) Fundamental vibrational modes of simple molecules and molecular ions. (from Pieters & Englert,1993)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 24 Spectroscopy of Materials (cont’d) Overtones occur at approximately linear combinations of the fundamental frequencies, e.g., 1 + 1, or Since these are not perfectly free harmonic oscillations the overtones are usually shifted to slightly longer wavelengths than simple addition would predict.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 25 Concentration of material tends to be proportional to absorption but confusion factors can arise caused by, for example, stronger returns from fine particulates dispersed over the matrix. Particularly when the materials are optically interacting any spectral combination may be highly non-linear (e.g., an intimate mixture). Spectroscopy of Materials (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 26 Characteristics of Spectral data: Changes in absorption features with state changes Vegetation & Snow Spectra Examples of a calculated water vapor transmittance spectrum and measured reflectance spectra of vegetation and snow.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 27 Example reflection spectra Example reflection spectra 32% reflector through different atmospheres DC reflectance observed radiance observed radiance

Digital Imaging and Remote Sensing Laboratory Spectral Sources 28 Example reflection spectra Example reflection spectra 32% reflectance through different atmospheres reflectance observed radiance observed radiance DC

Digital Imaging and Remote Sensing Laboratory Spectral Sources 29 Scattering Theory The shape of the absorption feature when expressed as reflectance vs. energy or apparent absorption is approximately Gaussian. The continuum must be removed by dividing the reflectance spectrum by an estimate of the continuum or subtracting an estimate of the log of the continuum from the log (lnr) of the reflectance spectrum.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 30 Scattering Theory (cont’d) The spectra of pure montmorillonite (top) and mixtures of montmorillonite plus carbon black (0.5 wt % carbon black, middle; 2.0 wt % carbon black, bottom) (from Clark & Rousch1984)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 31 Scattering Theory (cont’d) The absorption spectra can then be characterized by fitting a Gaussian to the specific absorption feature. Can estimate source of other absorption features, curve fit and divide them out or simply curve fit (often straight line) locally and divide to estimate the absorption feature.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 32 Band depth defined as: D = __________ where is reflectance of continuum at band center and is reflectance at band center Scattering Theory (cont’d) D - D C B D C D C D B

Digital Imaging and Remote Sensing Laboratory Spectral Sources 33 Scattering Theory

Digital Imaging and Remote Sensing Laboratory Spectral Sources 34 Characteristics of Spectral data: Sample Spectra

Digital Imaging and Remote Sensing Laboratory Spectral Sources 35 Spectroscopy of Minerals Figure 4a. Wavelength

Digital Imaging and Remote Sensing Laboratory Spectral Sources 36 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 37 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 38 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 39 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 40 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 41 Spectroscopy of Minerals (cont’d)

Digital Imaging and Remote Sensing Laboratory Spectral Sources 42 Spectroscopy of Minerals (cont’d) Figure 5a. The reflectance spectra of talc as a function of spectral resolution in 1.4 micro-meter region.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 43 Hyperspectral Notes Some sample spectra of organic compounds are shown in Figure 3.17 and absorption lines associated with transitions listed in Table 3.2.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 44 Hyperspectral Fig Spectra of organic compounds sample spectra of organic compounds and absorption lines associated with transitions listed in next table.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 45 Hyperspectral Notes (cont’d) Table 3.2. NIR absorptions due to vibrational transitions of organic molecules

Digital Imaging and Remote Sensing Laboratory Spectral Sources 46 Hyperspectral Notes (cont’d) Effect of particle size: Reflection can be thought of as a combination of surface (specular) reflection and volume (diffuse or scattered) reflection. In the diffuse case, some of the flux penetrates medium and is partially absorbed before being scattered back to the surface.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 47 Hyperspectral Notes (cont’d) In general, for a highly reflecting (weakly absorbing) material: increasing grain size will decrease the reflectance (increase transmissive interactions and absorption line strength).

Digital Imaging and Remote Sensing Laboratory Spectral Sources 48 Hyperspectral Notes (cont’d) Fig Variation in reflectance and absorption band depth with variations in particle size of clacite (Iceland spar), a high albedo mineral.

Digital Imaging and Remote Sensing Laboratory Spectral Sources 49 Hyperspectral Notes (cont’d) In strongly absorbing materials, surface reflection may dominate. Depth of penetration is very shallow (little diffuse reflection) reflectivity decreases with decreasing particle (more absorbing centers available). Absorption band strength is deepest when particle size is approximately equal to the optical depth (which is, of course, wavelength dependent).

Digital Imaging and Remote Sensing Laboratory Spectral Sources 50 Hyperspectral Notes (cont’d) Fig Variation in reflection properties with particle size for a strongly absorbing material (pyrite, FeS 2 ).

Digital Imaging and Remote Sensing Laboratory Spectral Sources 51 gaseous absorption and emission spectra sample spectra ASTER Spectral Library Example Emission Spectra Methyl Chloride Absorbance Curve

Digital Imaging and Remote Sensing Laboratory Spectral Sources 52 Hyperspectral Notes (cont’d) In mixtures, small, highly absorbing particles may disproportionately dominate composite spectra (intimate non linear mixing occurs).