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

Environmental Science(all)
Engineering(all)
Agricultural and Biological Sciences(all)
Earth and Planetary Sciences(all)

Access to Document

10.1201/b11863

Cite this

@inbook{a0cabc4f635c4c29953a54fea5050bff,

title = "Vegetation outlook (vegout): Predicting remote sensing-based seasonal greenness",

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).",

author = "Tsegaye Tadesse and Wardlow, {Brian D.} and Svoboda, {Mark D.} and Hayes, {Michael J.}",

note = "Publisher Copyright: {\textcopyright} 2012 by Taylor & Francis Group, LLC.",

year = "2012",

month = jan,

day = "1",

doi = "10.1201/b11863",

language = "English (US)",

isbn = "9781439835579",

pages = "75--94",

booktitle = "Remote Sensing of Drought",

publisher = "CRC Press",

}

TY - CHAP

T1 - Vegetation outlook (vegout)

T2 - Predicting remote sensing-based seasonal greenness

AU - Tadesse, Tsegaye

AU - Wardlow, Brian D.

AU - Svoboda, Mark D.

AU - Hayes, Michael J.

PY - 2012/1/1

Y1 - 2012/1/1

N2 - 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).

AB - 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).

UR - http://www.scopus.com/inward/record.url?scp=84922990707&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84922990707&partnerID=8YFLogxK

U2 - 10.1201/b11863

DO - 10.1201/b11863

M3 - Chapter

AN - SCOPUS:84922990707

SN - 9781439835579

SP - 75

EP - 94

BT - Remote Sensing of Drought

PB - CRC Press

ER -

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

Abstract

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this