Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information

Assessing nitrogen (N) status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using statistical analysis approach where they are often subject to site-specific problems. This study describ...

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Main Authors: Farrah Melissa Muharam, Stephen J. Maas, Kevin F. Bronson, Tina Delahunty
Format: Article
Language:English
Published: MDPI AG 2015-05-01
Series:Remote Sensing
Subjects:
Online Access:http://www.mdpi.com/2072-4292/7/6/7007
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spelling doaj-9626169bd8c04fb59e60b6df861da9a22020-11-24T23:06:24ZengMDPI AGRemote Sensing2072-42922015-05-01767007702810.3390/rs70607007rs70607007Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover InformationFarrah Melissa Muharam0Stephen J. Maas1Kevin F. Bronson2Tina Delahunty3Department of Plant and Soil Science, MS42122, Texas Tech University, Lubbock, TX 79409, USADepartment of Plant and Soil Science, MS42122, Texas Tech University, Lubbock, TX 79409, USAArid-Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ 85138, USADepartment of Environmental, Geographical, and Geological Sciences, Bloomsburg University of Pennsylvania, 400 East 2nd Street, Bloomsburg, PA 17815, USAAssessing nitrogen (N) status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using statistical analysis approach where they are often subject to site-specific problems. This study describes and tests a novel method of utilizing physical characteristics of N-fertilized cotton and combining field spectral measurements made at different spatial scales as an approach to estimate in-season chlorophyll or leaf N content of field-grown cotton. In this study, leaf greenness estimated from spectral measurements made at the individual leaf, canopy and scene levels was combined with percent ground cover to produce three different indices, named TCCLeaf, TCCCanopy, and TCCScene. These indices worked best for estimating leaf N at early flowering, but not for chlorophyll content. Of the three indices, TCCLeaf showed the best ability to estimate leaf N (R2 = 0.89). These results suggest that the use of green and red-edge wavelengths derived at the leaf scale is best for estimating leaf greenness. TCCCanopy had a slightly lower R2 value than TCCLeaf (0.76), suggesting that the utilization of yellow and red-edge wavelengths obtained at the canopy level could be used as an alternative to estimate leaf N in the absence of leaf spectral information. The relationship between TCCScene and leaf N was the lowest (R2 = 0.50), indicating that the estimation of canopy greenness from scene measurements needs improvement. Results from this study confirmed the potential of these indices as efficient methods for estimating in-season leaf N status of cotton.http://www.mdpi.com/2072-4292/7/6/7007spectroradiometerground covernitrogenleafcanopyscene
collection DOAJ
language English
format Article
sources DOAJ
author Farrah Melissa Muharam
Stephen J. Maas
Kevin F. Bronson
Tina Delahunty
spellingShingle Farrah Melissa Muharam
Stephen J. Maas
Kevin F. Bronson
Tina Delahunty
Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
Remote Sensing
spectroradiometer
ground cover
nitrogen
leaf
canopy
scene
author_facet Farrah Melissa Muharam
Stephen J. Maas
Kevin F. Bronson
Tina Delahunty
author_sort Farrah Melissa Muharam
title Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
title_short Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
title_full Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
title_fullStr Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
title_full_unstemmed Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information
title_sort estimating cotton nitrogen nutrition status using leaf greenness and ground cover information
publisher MDPI AG
series Remote Sensing
issn 2072-4292
publishDate 2015-05-01
description Assessing nitrogen (N) status is important from economic and environmental standpoints. To date, many spectral indices to estimate cotton chlorophyll or N content have been purely developed using statistical analysis approach where they are often subject to site-specific problems. This study describes and tests a novel method of utilizing physical characteristics of N-fertilized cotton and combining field spectral measurements made at different spatial scales as an approach to estimate in-season chlorophyll or leaf N content of field-grown cotton. In this study, leaf greenness estimated from spectral measurements made at the individual leaf, canopy and scene levels was combined with percent ground cover to produce three different indices, named TCCLeaf, TCCCanopy, and TCCScene. These indices worked best for estimating leaf N at early flowering, but not for chlorophyll content. Of the three indices, TCCLeaf showed the best ability to estimate leaf N (R2 = 0.89). These results suggest that the use of green and red-edge wavelengths derived at the leaf scale is best for estimating leaf greenness. TCCCanopy had a slightly lower R2 value than TCCLeaf (0.76), suggesting that the utilization of yellow and red-edge wavelengths obtained at the canopy level could be used as an alternative to estimate leaf N in the absence of leaf spectral information. The relationship between TCCScene and leaf N was the lowest (R2 = 0.50), indicating that the estimation of canopy greenness from scene measurements needs improvement. Results from this study confirmed the potential of these indices as efficient methods for estimating in-season leaf N status of cotton.
topic spectroradiometer
ground cover
nitrogen
leaf
canopy
scene
url http://www.mdpi.com/2072-4292/7/6/7007
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