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Remote Sens. 2015, 7(6), 7007-7028; doi:10.3390/rs70607007

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

1
Department of Plant and Soil Science, MS42122, Texas Tech University, Lubbock, TX 79409, USA
2
Department of Agriculture Technology, Faculty of Agriculture, Universiti Putra Malaysia, UPM Serdang, Selangor 43400, Malaysia
3
Arid-Land Agricultural Research Center, USDA-ARS, 21881 North Cardon Lane, Maricopa, AZ 85138, USA
4
Department of Environmental, Geographical, and Geological Sciences, Bloomsburg University of Pennsylvania, 400 East 2nd Street, Bloomsburg, PA 17815, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Eyal Ben-Dor and Prasad S. Thenkabail
Received: 27 February 2015 / Revised: 19 May 2015 / Accepted: 22 May 2015 / Published: 29 May 2015
(This article belongs to the Special Issue Field Spectroscopy and Radiometry)
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Abstract

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. View Full-Text
Keywords: spectroradiometer; ground cover; nitrogen; leaf; canopy; scene spectroradiometer; ground cover; nitrogen; leaf; canopy; scene
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Muharam, F.M.; Maas, S.J.; Bronson, K.F.; Delahunty, T. Estimating Cotton Nitrogen Nutrition Status Using Leaf Greenness and Ground Cover Information. Remote Sens. 2015, 7, 7007-7028.

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