Recent Developments in the Hyperspectral Environment and Resource Observer (HERO) Mission

被引:15
作者
Hollinger, A. [1 ]
Bergeron, M. [1 ]
Maskiewicz, M. [1 ]
Qian, S. E. [1 ]
Othman, H. [1 ]
Staenz, K. [2 ]
Neville, R. A. [2 ]
Goodenough, D. G. [3 ]
机构
[1] Canadian Space Agcy, 6767 Rte Aeroport, St Hubert, PQ J3Y 8Y9, Canada
[2] Nat Resource Canada, Canada Ctr Remote Sensing, Ottawa, ON K1A0Y7, Canada
[3] Nat Resources Canada, Pacific Forestry Ctr, Victoria, BC V8Z1M5, Canada
来源
2006 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, VOLS 1-8 | 2006年
关键词
hyperspectral; applications; HERO mission & payload; data compression; instrument performance analyses; international collaboration;
D O I
10.1109/IGARSS.2006.418
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
In 1997, the Canadian Space Agency (CSA) and Canadian industry began developing enabling technologies for hyperspectral satellites. Since then, the CSA has conducted mission and payload concept studies in preparation for launch of the first Canadian hyperspectral earth observation satellite. This Canadian hyperspectral remote sensing project is now named the Hyperspectral Environment and Resource Observer (HERO) Mission. In 2005, the Preliminary System Requirement Review (PSRR) and the Phase A (Preliminary Mission Definition) were concluded. Recent developments regarding the payload include an extensive comparison of potential optical designs. The payload uses separate grating spectrometers for the visible near-infrared and short-wave infrared portions of the spectrum. The instrument covers a swath of > 30 km, has a ground sampling distance of 30m, a spectral range of 400-2500 nm, and a spectral sampling interval of 10 nm. Smile and keystone are minimized. Recent developments regarding the mission include requirements simplification, data compression studies, and hyperspectral data simulation capability. In addition, a Prototype Data Processing Chain (PDPC) has been defined for 3 key hyperspectral applications. These are: geological mapping in the arctic environment, dominant species identification for forestry, and leaf area index for estimating foliage cover as well as forecasting crop growth and yield in agriculture.
引用
收藏
页码:1620 / +
页数:2
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