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22 - SeaWiFS operational geolocation assessment system

from PART IV - Applications and Operational Systems

Published online by Cambridge University Press:  03 May 2011

By
Frederick S. Patt
Edited by
Jacqueline Le Moigne ,
Nathan S. Netanyahu  and
Roger D. Eastman
Frederick S. Patt
Affiliation:
NASA Goddard Space Flight Center, Maryland
Jacqueline Le Moigne
Affiliation:
NASA-Goddard Space Flight Center
Nathan S. Netanyahu
Affiliation:
Bar-Ilan University, Israel and University of Maryland, College Park
Roger D. Eastman
Affiliation:
Loyola University Maryland
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Summary

Abstract

An automated method has been developed for performing geolocation assessment on global satellite-based Earth remote sensing data. The method utilizes islands as targets that can be readily located in the sensor data and identified with reference locations. The essential elements are an algorithm for classifying the sensor data according to the source, a reference catalog of island locations, and a robust algorithm for matching viewed islands with the catalog locations. This method was originally developed and tested for the Sea-viewing Wide Field-of-view Sensor (SeaWiFS) before its launch in 1997, and was refined using the flight data after launch. The results have been used for both ongoing assessment of geolocation accuracy and development of improvements to the geolocation processing algorithms. The method has also been applied to other moderate-resolution satellite sensors.

Introduction

The determination of geolocation for global Earth imaging sensors is generally performed in-line with initial (e.g., Level 0–1) data processing, using parametric algorithms based on navigation (satellite orbit and attitude) and telemetry data. The accuracy of the satellite navigation data may be adequate to meet geolocation requirements at the resolution of global sensors (250 to 1000 m) without the need for manual intervention or postprocessing corrections. However, this approach does need verification and feedback using the sensed image data. Both navigation data and sensor geometric models are subject to systematic and time-varying errors, and a means of ongoing assessment of geolocation accuracy is needed over the full range (temporal and geographic) of data collection.

Type
Chapter
Information
Image Registration for Remote Sensing , pp. 456 - 472
Publisher: Cambridge University Press
Print publication year: 2011

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References

Gregg, W. W., Conkright, M. E., O'Reilly, J. E., Patt, F. S., Wang, M. H., Yoder, J. A., and Casey, N. W. (2002). NOAA-NASA coastal zone color scanner reanalysis effort. Applied Optics, 41(9), 1615–1628.CrossRefGoogle ScholarPubMed
Nishihama, M., Wolfe, R. E., Solomon, D., Patt, F. S., Blanchette, J., Fleig, A., and Masuoka, E. (1997). MODIS Level 1A Earth Location Algorithm: Theoretical Basis Document, Version 3.0. NASA Technical Memorandum SDST-092, Laboratory for Terrestrial Physics, NASA Goddard Space Flight Center, Greenbelt, MD.
Patt, F. S. (2002). Navigation algorithms for the SeaWiFS mission. In SeaWiFS Postlaunch Technical Report Series, S. B. Hooker and E. R. Firestone, eds., NASA Technical Memorandum 2002-0206892, Vol. 16, NASA Goddard Space Flight Center, Greenbelt, MD.Google Scholar
Patt, F. S., Gregg, W. W., and Woodward, R. H. (1997). An automated method for navigation assessment for Earth survey sensors using island targets. International Journal of Remote Sensing, 18(16), 3311–3336.CrossRefGoogle Scholar
Wessel, P. and Smith, W. H. F. (1996). A global self-consistent, hierarchical, high-resolution shoreline database. Journal of Geophysical Research, 101(B4), 8741–8743.CrossRefGoogle Scholar

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