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Proceedings Paper

The most effective statistical approach to correct environmental satellite data
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Paper Abstract

The proposed paper apply novel statistical approach to correct radiometric data measured by Advanced Very High Resolution Radiometers(AVHRR) onboard the National Oceanic and Atmospheric Administration (NOAA) Polar Orbiting Environmental Satellites(POES). This paper investigates Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data during 1982-2003. AVHRR weekly data for the five NOAA afternoon satellites for the China dataset is studied, for it includes a wide variety of different ecosystems represented globally. It was found that data for the years 1988, 1992, 1993, 1994, 1995 and 2000 are not stable enough compared to other years because of satellite orbit drift, and AVHRR sensor degradation. It is assumed that data from NOAA-7 (1982, 1983), NOAA-9 (1985, 1986), NOAA-11 (1989, 1990), NOAA-14 (1996, 1997), and NOAA-16 (2001, 2002) to be standard because these satellites equator crossing time fall within 1330 and 1500, and hence maximizing the value of coefficients. The crux of the proposed correction procedure consists of dividing standard years data sets into two subsets. The subset 1 (standard data correction sets) is used for correcting unstable years and then corrected data for this years compared with the standard data in the subset 2 (standard data validation sets). In this paper, we apply empirical distribution function (EDF) to correct this deficiency of data for the affected years. It allows one to represent any global ecosystem from desert to tropical forest and to correct deviations in satellite data due to satellite technical problems. The corrected data set can be used for climatological research.

Paper Details

Date Published: 7 October 2009
PDF: 12 pages
Proc. SPIE 7478, Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX, 74782V (7 October 2009); doi: 10.1117/12.830257
Show Author Affiliations
Md Z. Rahman, LaGuardia Community College, CUNY (United States)
Leonid Roytman, City College, CUNY (United States)

Published in SPIE Proceedings Vol. 7478:
Remote Sensing for Environmental Monitoring, GIS Applications, and Geology IX
Ulrich Michel; Daniel L. Civco, Editor(s)

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