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Class 8: Radiometric Corrections

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Presentation on theme: "Class 8: Radiometric Corrections"— Presentation transcript:

1 Class 8: Radiometric Corrections
Sensor Corrections Atmospheric Corrections Conversion from DN to reflectance BRDF Corrections

2 Two types of corrections: Geometric Corrections (class 5)
Radiometric Corrections (class 8) Remote sensing images are contaminated by various radiative processes. The need to correct them varies with the applications and sensors used. Every time two images need to be combined (e.g., in a mosaic) or compared, the corrections become obviously important.

3 Radiometric Correction
Correction is made on the brightness (gray level) values of the image. Source of errors to be corrected: atmospheric degradation sensor malfunctions Illumination-view geometry Corrections are usually different for each band, and in theory for each pixel Attempts to correct data may themselves introduce errors Campbell 10.4

4 Radiometric Corrections
Correction for detector errors Line drop Destriping 2. Atmospheric corrections Histogram adjustment Atmospheric radiative transfer models 3. Conversion from DN to radiance 4. Conversion from radiance to reflectance 5. BRDF corrections

5 Sensor corrections Line Dropout Solution: Or use other spectral band
43 47 51 57 40 46 50 54 38 42 Mean from above and below pixels Solution: 43 47 51 57 40 46 50 54 38 42 39 52 Or use other spectral band Images: Lillesand-Kiefer Campbell 10.4

6 Sensor corrections Striping Local averaging Normalization
Images: Lillesand-Kiefer Campbell 10.4

7 Atmospheric Corrections
Histogram adjustment Clear sky Hazy sky 2) Physical Models Campbell 10.4

8 Simple Atmospheric Corrections – Histogram Adjustment
Clear Atmosphere Cloud shadowed region and water bodies have very low reflectance in infrared bands. This should give a peak near zero on the histogram. The shifted peak is due to the low reflectance regions with atmospheric scattering. A correction can be obtain by removing this value from all pixels. This method is called the Histogram Minimum Method (HMM) Narrow range of brightness values Small atmospheric contribution to brightness Brightness values Darkest values near zero Campbell 10.4

9 Simple Atmospheric Corrections – Histogram Adjustment
Hazy Atmosphere Wide range of brightness values In this case, the minimum value is higher, and the histogram shape has changed Added brightness of atmosphere Brightness values Darkest values far from zero Campbell 10.4

10 Atmospheric Correction Models
Physical models simulate the physical process of scattering at the level of individual particles and molecules Absorption by gases scattering by aerosols LOWTRAN 7 MODTRAN CAM5S, 6S Complex models that need many meteorological data as input. The data may not always be available Campbell 10.4

11 Atmospheric Correction Models
Second Simulation of the Satellite Signal in the Solar Spectrum: 6S Input file example (Saskatchewan study site; Landsat imagery): (landsat TM) (month,day,hour,long,lat) (mid lat summer) (continental) (visibility, km) (TARGET ALTITUDE IN KM) (SATELLITE CASE) (Landsat band 1) (HOMOGENEOUS CASE) (NO BRDF effect) (uniform target = vegetation) (no atm. correction)

12 Atmospheric Correction Models ASAS Konza prairie reflectance spectrum
6S corrected reflectance Top of atmosphere reflectance ASAS Konza prairie reflectance spectrum ASAS band central wavelength (nm) Vermote et al., 1997

13 From DN to Radiance to Reflectance
Calibration Gain Coefficient (counts/(W/m2/sr/mm)) Characteristic Wavelength (mm) Solar Irradiance (W/m2/mm) LANDSAT TM Spectral Band 1 G=(-3.58E-05)*D 2 G=(-2.10E-05)*D 3 G=(-1.04E-05)*D 4 G=(-3.20E-06)*D 5 G=(-2.64E-05)*D 7 G=(-3.81E-04)*D D = days since launch The revised gain coefficient equations incorporating 1994 calibration updates not included in the IGARSS '94 paper are as follows, where D = days since launch (01-March-1984), L* = (DSL - Offset)/G, and DSL = digital signal level. E (irradiance) = EoCos(SZA)/d^2 Radiance = (DN - Offset)/Gain Reflectance = p.Radiance/Incident Solar Irradiance Incident Solar Irradiance=Solar Irradiance *cos(SZA) Source: CCRS Web site

14 This is a common occurrence in land remote sensing systems
If the input signal exceeds the amount for which the sensor was designed, the system response will become non-linear or reach the saturation level. This is a common occurrence in land remote sensing systems when they image bright clouds and/or snow cover, for example. Saturation Non-Linear Region y (DN) Linear Region y = a.x + b (DN = gain*Radiance + offset) Offset b Input Value x (radiance) Source: CCRS Web site

15 Atmospheric Corrections
Ltot= radiance measured by the sensor r = reflectance of the target E = irradiance on the target T = transmissivity of the atmosphere Lp= path radiance (radiance due to the atmosphere) L & K 7.2

16 Atmospheric Corrections
E0 coss E = d2 E0 = solar irradiance at the mean Earth-Sun distance s =solar zenith angle d = relative deviation of Earth-Sun distance from the mean distance at the time of imaging L & K 7.2

17 Bidirectional Reflectance Distribution Function (BRDF) Correction
Structures like trees cast shadows that change the amount of light that reaches a sensor depending on its view zenith angle To compare pixel reflectance from different images, or even different part of an image, the target (pixel) reflectance must be measured under the same view and solar geometry. Sensor Solar Zenith Angle (SZA) View Zenith Angle (VZA)

18 CCRS uses a modification of Roujean’s model
Some BRDF models CCRS uses a modification of Roujean’s model for BRDF corrections of AVHRR data (Roujean + hotspot from 4-Scale, Chen and Cihlar, 1997) GORT (Li and Strahler) 4-Scale (Chen and Leblanc)

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