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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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 |
work_keys_str_mv |
AT farrahmelissamuharam estimatingcottonnitrogennutritionstatususingleafgreennessandgroundcoverinformation AT stephenjmaas estimatingcottonnitrogennutritionstatususingleafgreennessandgroundcoverinformation AT kevinfbronson estimatingcottonnitrogennutritionstatususingleafgreennessandgroundcoverinformation AT tinadelahunty estimatingcottonnitrogennutritionstatususingleafgreennessandgroundcoverinformation |
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