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Sensors onboard meteorological satellites such as MODIS and AVHRR are able to collect information adequate in frequency but with low spatial resolution. The problem can be overcome, if one finds a way to increase the quality of the vegetation indices through searching in each individual pixel of the images, employing concurrent higher spatial resolution images. The objective of this study was to investigate the enhancement of MODIS NDVI products by using NDVI from the ASTER sensor onboard the same platform, as MODIS. The ASTER averaged NDVI values computed using only vegetated pixels were compared to unadjusted MODIS NDVI. Two approaches for the comparison are introduced in this work. In the first one, vegetated ASTER NDVI compared with MODIS NDVI (AMII Model), and in the second one the difference between vegetated ASTER NDVI and MODIS NDVI was modeled against a code representing percentage of vegetation cover (AMDI Model). It is found that the MODIS NDVI index always reads lower as compared to the vegetated ASTER NDVI. It was also found that the difference between vegetated ASTER NDVI and MODIS NDVI for vegetation covers of less than 20% was greater than 0.1 and for vegetation covers of more than 80% as low as 0.01. This could produce erroneous results when introducing uncorrected NDVI values into the climatological models especially in the arid and semi-arid climates where the vegetation covers are sparse. Both AMII and AMDI models produce NDVI values higher than those calculated from MODIS. These models were tested using 10 samples where a RMSE of about 0.028 for AMII and 0.018 for AMDI was found out. It is revealed that AMII model increases the NDVI values up to 87% for pixels containing less than 10% vegetation while 5% for pixels with more than 80% vegetation covers. These increases for AMDI model were 84% and 6%, respectively.

Keywords: Image processing, Remote Sensing, Vegetation

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