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34 2. Remote Sensing with MODIS Figure 2.12 – Three types of stripe noise on Terra MODIS Level 1B geolocated calibrated radiances (Left) Detector-to-detector stripes (Band 27) (Center) Mirror side stripes (Band 9) (Right) Random stripes (Band 33). All the images are 200×200 pixels in size with a resolution of 1km mode. Typical cases of mirror banding can be seen in homogeneous areas contaminated with sun glint or high concentrations of aerosol. The analysis of oceanographic data indicates that mirror banding reaches a maximal amplitude in the specular direction, (highest level of sun glint) and tends to fade away as the sun glint intensity decreases. This shows that mirror side stripes are dependent on the signal level and can be correlated with the scan angle. Detector-to-detector stripes take the form of a periodic pattern of stripes and unlike mirror banding they cover entire MODIS swaths. Studies related to other sensors Horn and Woodham [1979], attribute the presence of these periodic stripes to a poor gain/offset calibration of the indivual detectors composing the sensor. Furthermore, detectors responses display strong non linear effects in the low radiance range (figure 3.4). The third type of stripes are random and appear clearly on Terra MODIS band 33 as black and white stripes with a limited lenght over a given scan line. Noisy stripes are presumably due to errors in the internal system and random noise. The processing of level 1B data to level 2 geophysical products does not include a correcting algorithm for striping. The most recent version of MODIS data (collection 5), includes a destriping procedure [] limited only to land surface reflectances. As atmospheric correction and bio-optical algorithms combine the information from multiple spectral bands to generate level 2 data, striping tends to be emphazised in the derived geophysical products. Mirror banding clearly affects ocean colour products such as normalized water leaving radiance and chlorophyll concentration (figure 2.13). Level 2 daytime Sea Surface Temperature (SST) is computed from bands 31 and 32, and shows evidence of detector-to-detector stripes. Going further in MODIS processing chain, striping appears to persist even in level 3 products, altough this effect migh also be originating from the poor resolution of level 3 data (4km). It is clear, that stripe noise on level 1B data impacts the quality of higher level products and needs to be corrected efficiently before the gene-
35 ration of level 2 and level 3 products. Because the stripe noise does not affect similarly all spectral bands, the derivation of geophysical variables often results in a stripe noise amplification. For instance, striping can hardly be seen on bands 31 and 32. Yet, SWIR and LWIR SST products display stripe noise. MODIS SST is obtained with an NLSST (Non linear SST) algorithm that can be expressed as : SST = a 0 + a 1 .BT 11 + a 2 .(BT 11 − BT 12 )+a 3 .(BT 11 − BT 12 ).( 1 − 1) (2.10) µ where a 0 , a 1 , a 2 , a 3 are time dependent coefficients determined by the Rosentiel School of Marine and Atmospheric Science from in-situ measurements. BT 11 and BT 12 are brightness temperatures of bands 31 and 32, and µ is the cosine of the sensor zenith angle. The difference between bands 31 and 32 is used to correct atmospheric effects due to waper vapor absorption and is responsible for the introduction of striping in the SST. The amplification of striping can be stronger on products generated from algorithms that include multiplicative operations between spectral bands, which is the case of many bio-optical models used to estimate oceanographic variables.
Page 1: École Doctorale d’Informatique,
Page 5: 5 Résumé Nou abordons dans cette
Page 8 and 9: 8 TABLE DES MATIÈRES 3.5 Frequency
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- Alaska Labrador Siberia Restorati
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128 Conclusion
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130 BIBLIOGRAPHIE A. Chambolle. An
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132 BIBLIOGRAPHIE E. Hochberg, S. A
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134 BIBLIOGRAPHIE P. Perona and J.
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