Vegetation outlook (vegout): Predicting remote sensing-based seasonal greenness

Tsegaye Tadesse; Brian D. Wardlow; Mark D. Svoboda; Michael J. Hayes

doi:10.1201/b11863

Vegetation outlook (vegout): Predicting remote sensing-based seasonal greenness

Tsegaye Tadesse, Brian D. Wardlow, Mark D. Svoboda, Michael J. Hayes

Daugherty Water for Food Global Institute

Research output: Chapter in Book/Report/Conference proceeding › Chapter

3 Scopus citations

Abstract

Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing-based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006).

Original language	English (US)
Title of host publication	Remote Sensing of Drought
Subtitle of host publication	Innovative Monitoring Approaches
Publisher	CRC Press
Pages	75-94
Number of pages	20
ISBN (Electronic)	9781439835609
ISBN (Print)	9781439835579
DOIs	https://doi.org/10.1201/b11863
State	Published - Jan 1 2012

ASJC Scopus subject areas

General Environmental Science
General Engineering
General Agricultural and Biological Sciences
General Earth and Planetary Sciences

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10.1201/b11863

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abstract = "Accurate and timely prediction of vegetation conditions enhances knowledge-based decision making for drought planning, mitigation, and response. This is very important in countries that are highly dependent on rainfed agriculture. For example, studies show that remote sensing-based observations and vegetation condition prediction have great potential for estimating crop yields (Verdin and Klaver, 2002; Ji and Peters, 2003; Seaquist et al., 2005; Tadesse et al., 2005a, 2008; Funk and Brown, 2006).",

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AU - Svoboda, Mark D.

AU - Hayes, Michael J.

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Vegetation outlook (vegout): Predicting remote sensing-based seasonal greenness

Abstract

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