Association of spectral reflectance indices with plant growth and lint yield in upland cotton

Mario Gutierrez, Elbert R Norton, Kelly R. Thorp, Guangyao Wang

Research output: Contribution to journalArticle

17 Citations (Scopus)

Abstract

Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices with cotton (Gossypium hirsutum L.) biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during the 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices (normalized difference vegetation index [NDVI], simple ratio [SR], near-infrared index [NIR], and ratio vegetation index [RVI]) were determined at different growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87% variation in cotton biomass (all R 2 > 0.87) and LAI (R 2 > 0.93). Indices SR, NIR, and RVI all had higher coefficients of determination (R 2) compared to NDVI because these indices were not saturated at late growth stages. There was no significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom showed significant correlations with lint yield. Indices SR, NIR, and RVI explained 56, 60, and 58% of variations in cotton lint yield, respectively, while NDVI only explained 47% of variation in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.

Original languageEnglish (US)
Pages (from-to)849-857
Number of pages9
JournalCrop Science
Volume52
Issue number2
DOIs
StatePublished - Mar 2012

Fingerprint

lint yield
Gossypium hirsutum
reflectance
plant growth
leaf area index
canopy
cotton
lint cotton
developmental stages
crop yield
biomass
prediction
crop management
deserts
growing season
vegetation index
normalized difference vegetation index

ASJC Scopus subject areas

  • Agronomy and Crop Science

Cite this

Association of spectral reflectance indices with plant growth and lint yield in upland cotton. / Gutierrez, Mario; Norton, Elbert R; Thorp, Kelly R.; Wang, Guangyao.

In: Crop Science, Vol. 52, No. 2, 03.2012, p. 849-857.

Research output: Contribution to journalArticle

Gutierrez, Mario ; Norton, Elbert R ; Thorp, Kelly R. ; Wang, Guangyao. / Association of spectral reflectance indices with plant growth and lint yield in upland cotton. In: Crop Science. 2012 ; Vol. 52, No. 2. pp. 849-857.
@article{ca81d8388f7246648122aa0ad33db1a8,
title = "Association of spectral reflectance indices with plant growth and lint yield in upland cotton",
abstract = "Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices with cotton (Gossypium hirsutum L.) biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during the 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices (normalized difference vegetation index [NDVI], simple ratio [SR], near-infrared index [NIR], and ratio vegetation index [RVI]) were determined at different growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87{\%} variation in cotton biomass (all R 2 > 0.87) and LAI (R 2 > 0.93). Indices SR, NIR, and RVI all had higher coefficients of determination (R 2) compared to NDVI because these indices were not saturated at late growth stages. There was no significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom showed significant correlations with lint yield. Indices SR, NIR, and RVI explained 56, 60, and 58{\%} of variations in cotton lint yield, respectively, while NDVI only explained 47{\%} of variation in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.",
author = "Mario Gutierrez and Norton, {Elbert R} and Thorp, {Kelly R.} and Guangyao Wang",
year = "2012",
month = "3",
doi = "10.2135/cropsci2011.04.0222",
language = "English (US)",
volume = "52",
pages = "849--857",
journal = "Crop Science",
issn = "0011-183X",
publisher = "Crop Science Society of America",
number = "2",

}

TY - JOUR

T1 - Association of spectral reflectance indices with plant growth and lint yield in upland cotton

AU - Gutierrez, Mario

AU - Norton, Elbert R

AU - Thorp, Kelly R.

AU - Wang, Guangyao

PY - 2012/3

Y1 - 2012/3

N2 - Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices with cotton (Gossypium hirsutum L.) biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during the 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices (normalized difference vegetation index [NDVI], simple ratio [SR], near-infrared index [NIR], and ratio vegetation index [RVI]) were determined at different growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87% variation in cotton biomass (all R 2 > 0.87) and LAI (R 2 > 0.93). Indices SR, NIR, and RVI all had higher coefficients of determination (R 2) compared to NDVI because these indices were not saturated at late growth stages. There was no significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom showed significant correlations with lint yield. Indices SR, NIR, and RVI explained 56, 60, and 58% of variations in cotton lint yield, respectively, while NDVI only explained 47% of variation in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.

AB - Canopy reflectance plays an increasingly important role in crop management and yield prediction at large scale. The relationship of four spectral reflectance indices with cotton (Gossypium hirsutum L.) biomass, leaf area index (LAI), and crop yield were investigated using three cotton varieties and five N rates in the irrigated low desert in Arizona during the 2009 and 2010 growing seasons. Biomass, LAI, and canopy reflectance indices (normalized difference vegetation index [NDVI], simple ratio [SR], near-infrared index [NIR], and ratio vegetation index [RVI]) were determined at different growth stages. The commonly used NDVI and the other three canopy reflectance indices explained over 87% variation in cotton biomass (all R 2 > 0.87) and LAI (R 2 > 0.93). Indices SR, NIR, and RVI all had higher coefficients of determination (R 2) compared to NDVI because these indices were not saturated at late growth stages. There was no significant relationship between lint yield and the spectral indices measured at early growth stages. However, the spectral indices determined at peak bloom showed significant correlations with lint yield. Indices SR, NIR, and RVI explained 56, 60, and 58% of variations in cotton lint yield, respectively, while NDVI only explained 47% of variation in lint yield. This study suggests canopy reflectance indices can be used to predict cotton lint yield at peak bloom and the accuracy of yield prediction can be significantly improved when SR, NIR, and RVI are used.

UR - http://www.scopus.com/inward/record.url?scp=84863137046&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84863137046&partnerID=8YFLogxK

U2 - 10.2135/cropsci2011.04.0222

DO - 10.2135/cropsci2011.04.0222

M3 - Article

AN - SCOPUS:84863137046

VL - 52

SP - 849

EP - 857

JO - Crop Science

JF - Crop Science

SN - 0011-183X

IS - 2

ER -