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Poster Session 2
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A new Composite Method for NOAA/AVHRR GAC Global Product
Histograms of DIFF, defined in figure 2, are shown in figure 6. DIFF of the N4SC is greater than the MVC in NDVI and lower in reflectance. That is to say, Image by the MVC has high local variance in reflectance as shown in figure 7, although it looks "smooth" in NDVI.

Figure 6. Histograms of DIFF for NDVI and reflectance of channel 1 and 2. The vertical axis is frequency in percent. -: N4SC -: MVC
 (a) N4SC
 (b) MVC
Figure 7. Composite images around India (channel 1:21 = 1:2L1 = R:G:B)
Histogram of number of day in adjacent pixels is shown in figure 8. This means that, in adjacent eight pixels, how many different day from center pixel is selected (if all pixels selected from same day as center pixel, the number is zero. The maximum number is eight in case that all adjacent pixels and center pixel are selected from different day each other). In the N4SC, neighboring pixels are selected from same day or another day, therefore image is looks like patches of daily image. On the other hand, larger number of day is used in the MVC and that's the reason of high local variance and speckle-like image as figure 7(b).
Histogram of scan angle is shown in figure 9. Distribution of can angle in the N4SC is clustered about nadir, while that in the MVC is spread in all angle with a little biased in forward-scatter region. It is evident that Influence of atmosphere and bi-directional reflection and size of footprint is less in the N4SC than in the MVC.
4. Conclusions
A New composite method, which is based on three consecutive criteria with maximum NDVI, maximum brightness temperature derived from channel 4, and minimum scan angle, is proposed and evaluated by comparing with the MVC method using AVHRR GAC global product. The results show several improvements. First, clouds over low vegetated area is eliminated due to brightness temperature. Secondly, image of reflectance looks patch-like image of daily images rather than speckle-like image. Finally, scan angle distributes closer to nadir, which makes the influence of atmosphere and bi-directional reflection weak and makes the size of footprint small.
Acknowledgement
This study has been supported by CREST of JST (Japan Science and Technology).
Reference
- Cihlar, J., Manak, D., and D'Iorio, M., 1994. Evaluation of Compositing Algorithms for AVHRR Data Land. IEEE Transactions on Geoscience and Remote Sensing, 32(2), pp. 427-437.
- Gutman, G., 1989. On the relationship between monthly mean and maximum -value composite normalized vegetation indices. International Journal of Remote Sensing, 10(8), pp. 1317-1325.
- Holben, B.N., 1986. Characteristics of maximum-value composite images from temporal AVHRR data. International Journal of Remote of Remote Sensing, 7(11), pp. 1417-1434.
- Stoms, D.,M., Bueno, M.,J., and Davis, F., W., 1997. Photogrammetric Engineering & Remote Sensing, 63(6), pp. 681-689.
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