2 May 2018 Improving AVHRR-based NDVI data using a statistical technique for global climate studies
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Abstract
The main objective of this report is to examine the Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data, which was collected from five NOAA series satellites. An empirical distribution function (EDF) was developed to decrease the long-term inaccuracy of the NDVI data derived from the AVHRR sensor on NOAA polar orbiting satellite. The instability of data is a consequence of orbit degradation, and from the circuit drifts over the life of a satellite. Degradation of NDVI over time and shifts of NDVI between the satellites were estimated using the China data set, because it includes a wide variety of different ecosystems represented globally. It was found that the data for six particular years, four of which were consecutive, are not stable compared to other years because of satellite orbit drift, AVHRR sensor degradation, and satellite technical problems, including satellite electronic and mechanical satellite systems deterioration. The data for paired years for the NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 were assumed to be standard because the crossing time of the satellite over the equator (between 13:30 and 15:00 hours) maximized the value of the coefficients. These years were considered the standard years, while in other years the quality of satellite observations significantly deviated from the standard. The deficiency of data for the affected years were normalized or corrected by using the EDF method and compared with the standard years. These normalized values were then utilized to estimate new NDVI time series that show significant improvement of NDVI data for the affected years so that the dataset is useful in climate studies.
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Md. Z. Rahman, Md. Z. Rahman, Leonid Roytman, Leonid Roytman, Abdel Hamid Kadik, Abdel Hamid Kadik, Dilara A. Rosy, Dilara A. Rosy, } "Improving AVHRR-based NDVI data using a statistical technique for global climate studies", Proc. SPIE 10642, Degraded Environments: Sensing, Processing, and Display 2018, 106420V (2 May 2018); doi: 10.1117/12.2305390; https://doi.org/10.1117/12.2305390
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