CHAPTER 2: PROCESSING OF THE VNIR-SWIR MASTER DATA
2.4 Data Analysis
2.4.4 Atmospheric Correction
2.4.4.3 Determination of the Best Atmospheric Correction
Based on the data analysis performed on the TVLRVR (using the “Basalt” and
“Pavement” field spectra and their corresponding regions in image 9900303H), it appears that the best atmospheric correction for the TVLRVR is obtained with the ELC. The calibration coefficients derived from the ELC technique appear to atmospherically correct other images within lines 9900302 (collected April 29, 1999) and 9900303 (collected April 30, 1999); however, they work less well for images within line 9900305 (collected May 3, 1999). The system response functions for the MASTER sensor may have shifted at some time between April 30, 1999 and May 3, 1999, causing the ELC coefficients computed for 9900303H not to apply to image 9900305B. The system response functions are only available pre-flight for April, 1999. However, a 0.003 micron shift is observed in pre- and post-flight calibration readings of the system response functions for
port 1 (channels 1 - 11) obtained for MASTER in August of 1998. These data are publicly available at http://masterweb.jpl.nasa.gov/.
Alternatively, the coefficients for line 9900302 may not be accurate for line 9900305 due to a difference in atmospheric conditions. Certainly, within scenes with high topographic relief, there are large differences in atmospheric path distance because of the very low flight elevation. This can adversely affect methods, such as the empirical line method, that use the surface as a reference [Farrand et al., 1994]. Because the TVLRVR is a coastal region, it is very likely that the atmospheric conditions, even within one image, vary greatly with distance from the coast. Unfortunately, the ACORN radiative transfer code and the ELC method do not account for this variation, and it is source of error that needs to be addressed.
Comparison of the mean spectra (corrected using the ELC method) of identical regions visible in two images (9900302ZA and 9900302V or 9900302ZA and 9900305B) reveals that although the ELC works well, the results are superior for images within lines acquired on April 29, 1999 and April 30, 1999 (lines 9900302 and 9900303, respectively) (Fig. 18). The images within line 9900305 show more error, particularly for bands 11, 15 - 19, and 25, all of which fall within water absorption regions (Fig. 13). Image 9900303L, however, which was acquired on the same day and within 20 minutes of image 9900303H, appears to be atmospherically corrected fairly well (Fig. 16).
In order to determine if the system response functions shifted between the flight days of April 30, 1999 and May 3, 1999, the data in images 9900305B (collected May 3, 1999) and 9900302ZA (collected April 29, 1999) were compared. Approximately 30% of image 9900305B overlaps with 9900302ZA (Fig. 8). Two regions that were imaged on both 9900305B and 99002ZA, one spectrally dark and one spectrally light, were chosen within these images. The mean and standard deviation were computed for regions of interest within these matching regions, composed of 5 pixels each. An increase in noise6, which would cause an increase in the standard deviation within a homogeneous region, could indicate that the system response function may have shifted. Inspection of Figure 19, for a spectrally dark region and Figure 20, for a spectrally light region, shows no increase in noise in image 9900305B. Therefore, the errors seen in the atmospheric correction on image 9900305B using the calibration factors from image 9900303H are most likely due to changes in the atmospheric conditions, and not a shift in the system response functions.
6 If the system response function shifts, it can move into absorption wavelengths, regions of low transmittance, and therefore yield a lower signal to noise ratio after the shift.
Statistics for Dark Region
-0.15 -0.1 -0.05 0 0.05 0.1 0.15
0.3 0.8 1.3 1.8 2.3
Wavelength (µm)
Reflectance (1=100%)
Mean of 9900305B Mean of 9900302ZA
Figure 19. Plots of the mean and standard deviations for a matching spectrally dark region within images 9900302ZA and 9900305B. The error bars represent one standard deviation from the mean.
Statistics for Light Region
-0.1 -0.05 0 0.05 0.1 0.15 0.2
0.3 0.8 1.3 1.8 2.3
Wavelength (µm)
Reflectance (1=100%)
Mean of 9900305B Mean of 9900302ZA
Figure 20. Plots of the mean and standard deviations for a matching spectrally light region within images 9900302ZA and 9900305B. The error bars represent one standard deviation from the mean.
Based on the example of field and atmospherically corrected spectra in Figures 16 and 17, the ELC method appears to atmospherically correct the MASTER data most accurately. However, very few well-located matches of field spectra are available to do a rigorous test of both the ACORN and Empirical Line Calibration method. If no field data are available, atmospheric correction with ACORN can also be obtained. ACORN with SSE yields results similar to ELC, except that ELC the calibration coefficients cannot be easily transferred to nearby images. Other radiative transfer models, such as HATCH [Qu et al., 2003] cannot be employed in this case because their algorithms rely on channels in the absorption portions of the spectrum near 1.4 and 2 microns. MASTER does not contain data in these regions because it was designed to avoid these absorption features.
Due to the large errors and high noise in bands 11, 15 - 19, and 25, further processing was done on the 18 well-corrected bands. Using the 18 bands that appear to be well corrected (bands 1 - 10, 12 - 14, and 20 - 24) for all images within the TVLRVR does not forfeit a large amount of spectral information. Figures 2 and 4 show library spectra of minerals containing overtone vibrational and electronic absorption features. What can be seen is that although only 10 bands in the VNIR and 5 bands in the SWIR were used to determine the shape of the spectra, it is still possible to differentiate between many of the minerals’ absorption features. In Figures 3 and 5 the absorption features detected with the MASTER data are highlighted, because the library spectra shown in these figures have been convolved to the MASTER wavelengths available, and the continua of the
spectra are removed. The following absorption features are clearly visible and distinguishable: those caused by the wings of charge transfer bands in the ultraviolet (Fe- O), centered on MASTER bands 2 and 3 (0.4589 µm and 0.5414 µm); those of vegetation, centered on MASTER band 5 (0.6624 µm); and those between 0.85 and 0.92 microns associated with the crystal field effects of Fe, centered on MASTER band 8 (0.803 µm). Less distinguishable are the absorption features caused by Al-OH, centered on MASTER bands 21 and 22 (2.1648 µm and 2.2146 µm); the absorption features caused by Mg-OH, centered on MASTER band 23 (2.2638 µm); the absorption features caused by CO3, centered on MASTER band 22 (2.2146 µm); and the absorption features caused by H20, centered on MASTER band 23 (2.2638 µm). The absorption features for Al-OH and CO3 can appear very similar, as can absorption features for Mg-OH and H20.
Unfortunately, some of the absorption features are located either within band 24 or at longer wavelengths. When the absorption feature occurs at longer wavelengths, such as that for the CO3 feature within calcite, the spectrum may slope toward the longer wavelength, but with the continuum removed, the feature is not highlighted.
Most surface materials have diagnostic absorption features that are 20 to 40 nm wide at Full Width Half Maximum (FWHM) [Hunt, 1977]. MASTER is not able to discriminate such narrow spectral features. It has an approximate FWHM of 50 nm in bands 1 - 25.
(Note the width of the spectral response functions in Figure 13.) Because the bands are not contiguous, the MASTER instrument will not allow for specific mineral identification without previous knowledge of the geology of the region. However, it does appear that enough information is available with the reduced data set of 18 bands to detect the
presence of features in the 0.4 to 1 micron portion of the spectrum, such as the electronic features of Fe3+ (Fig. 2), and overtone vibrational features in the 2.5 micron region of the spectrum, such as Al-O (2.16 to 2.22 µm), Mg-OH (2.3 to 2.35 µm), and CO3 (2.3 to 2.35 µm) (Fig. 4).