Share Email Print

Proceedings Paper

Building a global, consistent, and meaningful Landsat 7 data archive
Author(s): Theresa J. Arvidson; John R. Gasch; Samuel N. Goward
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

The mission of Landsat 7 is to acquire and periodically refresh a global archive of sun-lit, substantially cloud-free land scenes. For the U.S. archive, Landsat 7 is acquiring every land scene at least once every year, at an average rate of 250 scenes each day or 90,000 scenes each year. This is the first time in the 25 year history of Landsat data acquisitions that there is a deliberate goal to build this archive such that any data of interest to the majority of users will already be in the archive when they go looking for it - at the right gain setting, at the right time, substantially cloud-free, and at the right frequency of acquisition. Anticipating most users’ data needs is the key to achieving this lofty goal. The Long Term Acquisition Plan (LTAP) dictates the optimum acquisition refresh cycle for each scene, based on change detection and special interest inputs. The plan also specifies monthly optimum gain settings to maximize scene data return. Scheduling software automatically schedules acquisitions in accordance with this LTAP, making decisions as to acceptable cloud cover levels, urgency of acquisition, and availability of resources to fulfill the plan.

Paper Details

Date Published: 23 August 2000
PDF: 12 pages
Proc. SPIE 4049, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI, (23 August 2000); doi: 10.1117/12.410359
Show Author Affiliations
Theresa J. Arvidson, Lockheed Martin Corp. and NASA Goddard Space Flight Ctr. (United States)
John R. Gasch, Computer Sciences Corp. (United States)
Samuel N. Goward, Univ. of Maryland/College Park (United States)

Published in SPIE Proceedings Vol. 4049:
Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery VI
Sylvia S. Shen; Michael R. Descour, Editor(s)

© SPIE. Terms of Use
Back to Top