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Proceedings Paper

An application of statistical technique to correct satellite data due to orbit degradation
Author(s): Md. Z. Rahman; Leonid Roytman; Runa Jesmin
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Paper Abstract

This paper apply an statistical technique 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 study Normalized Difference Vegetation Index (NDVI) stability in the NOAA/NESDIS Global Vegetation Index (GVI) data for the period 1982-2003. AVHRR weekly data for the five NOAA afternoon satellites NOAA-7, NOAA-9, NOAA-11, NOAA-14, and NOAA-16 are used for the China dataset, for it includes a wide variety or different ecosystems represented globally. GVI has found wide use for studying and monitoring land surface, atmosphere, and recently for analyzing climate and environmental changes. Unfortunately the POES AVHRR data, though informative, can not be directly used in climate change studies because of the orbital drift in the NOAA satellites over these satellites' life time. This orbital drift introduces errors in AVHRR data sets for some satellites. To correct this error of satellite data, this paper implements Empirical Distribution Function (EDF) which is a statistical technique to generate error free long-term time-series for GVI data sets. It allows one to represent any global ecosystem from desert to tropical forest and to correct deviations in satellite data due to orbit degradation. The corrected datasets can be used as proxy to study climate change, epidemic analysis, and drought prediction etc.

Paper Details

Date Published: 25 October 2010
PDF: 12 pages
Proc. SPIE 7831, Earth Resources and Environmental Remote Sensing/GIS Applications, 78310Z (25 October 2010); doi: 10.1117/12.864175
Show Author Affiliations
Md. Z. Rahman, LaGuardia Community College (United States)
Leonid Roytman, The City College of New York (United States)
Runa Jesmin, King's College London (United Kingdom)

Published in SPIE Proceedings Vol. 7831:
Earth Resources and Environmental Remote Sensing/GIS Applications
Ulrich Michel; Daniel L. Civco, Editor(s)

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