Skip to main content
ARS Home » Pacific West Area » Maricopa, Arizona » U.S. Arid Land Agricultural Research Center » Water Management and Conservation Research » Research » Publications at this Location » Publication #316090
Title: Estimating cotton nitrogen nutrition status using leaf greenness and ground cover information

Author
item MUHARAM, F - Universiti Putra Malaysia
item MASS, S - Texas Tech University
item Bronson, Kevin
item DELAHUNTY, T - University Of Arizona

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/22/2015
Publication Date: 5/29/2015
Citation: Muharam, F.M., Mass, S.J., Bronson, K.F., Delahunty, T. 2015. Estimating cotton nitrogen nutrition status using leaf greenness and ground cover information. Remote Sensing. 7:7007-7008.

Interpretive Summary: In-season estimation of Nitrogen (N) content of crops like cotton is important from economic and environmental standpoints. To date, many spectral indices have been research for this purpose. In this a novel method of utilizing physical characteristics of N-fertilized cotton and combining field spectral measurements made at different spatial scales was used in field-grown cotton. 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. The use of green and red-edge wavelengths derived at the leaf scale is best for estimating leaf greenness. The relationship between TCCScene and leaf N was not strong. Results from this study confirmed the potential of these indices as efficient methods for estimating in-season leaf N status of cotton.

Technical 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.