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Proceedings Paper

Enhancing remotely sensed BT data for environmental analysis
Author(s): Md. Z. Rahman; Leonid Roytman; AbdelHamid Kadik; Dilara A. Rosy
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Paper Abstract

With the growing use of the Vegetation Index in many remote sensing applications, it was imperative to examine the Brightness Temperature (BT) 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 BT 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 BT over time and shifts of BT 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 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 BT time series that show significant improvement of BT data for the affected years so that the dataset is useful for environment monitoring.

Paper Details

Date Published: 10 May 2019
PDF: 11 pages
Proc. SPIE 11007, Advanced Environmental, Chemical, and Biological Sensing Technologies XV, 110070T (10 May 2019); doi: 10.1117/12.2519223
Show Author Affiliations
Md. Z. Rahman, LaGuardia Community College (United States)
Leonid Roytman, The City College of New York (United States)
AbdelHamid Kadik, LaGuardia Community College (United States)
Dilara A. Rosy, Univ. of Dhaka (Bangladesh)

Published in SPIE Proceedings Vol. 11007:
Advanced Environmental, Chemical, and Biological Sensing Technologies XV
Tuan Vo-Dinh, Editor(s)

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