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 #204164
Title: Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI)

Author
item EL-SHIKHA, D - USDA-ARS,USALARC - AZ
item BARNES, EDWARD - RALEIGH NC
item Clarke, Thomas
item Hunsaker, Douglas - Doug
item HABERLAND, J - UNIV OF CHILE, BRAZIL
item Pinter Jr, Paul
item WALLER, P - UNIV OF AZ, TUCSON
item THOMPSON, T - TEXAS TECH UNIV

Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 5/15/2007
Publication Date: 5/1/2008
Citation: El-Shikha, D.M., Barnes, E.M., Clarke, T.R., Hunsaker, D.J., Haberland, J.A., Pinter Jr, P.J., Waller, P.M., Thompson, T.L. 2008. Remote Sensing of Cotton Nitrogen Status Using the Canopy Chlorophyll Content Index (CCCI). Transactions of the ASABE. 51(1):73-82.

Interpretive Summary: Remote sensing may provide an effective way to determine when cotton plants need nitrogen fertilizer. This is important because applying fertilizer too early can result in unused nitrogen that remains in the soil. In irrigated fields, any nitrogen unused by the crop becomes a potential source of groundwater contamination. On the other hand, cotton plants become nutrient-stressed if nitrogen applications are made too late. Remote sensing instruments measure the amount of visible and near infrared light that is reflected from the plant canopy and soil within a field. Ratios of particular reflected light, such as near infrared to red, form what are called vegetation indexes. Various vegetation indexes have been successfully used in crop management to “see” differences in growth among plants, but these have been usually undependable for sensing differences in the nitrogen needs of plants. This study indicated that a fairly new remote sensing index, called the Canopy Chlorophyll Content Index (CCCI) provided more consistent and earlier detection of cotton nitrogen needs than several of the more commonly used vegetation indexes. However, the CCCI detected nitrogen needs better after the cotton was fully grown than before. One way to improve the CCCI detection of nitrogen needs during early cotton growth would be to take the reflectance measurements close to the plants so that soil reflectance is minimal. This research will be of interest to farmers, farm fertilizer and irrigation consultants, and government agencies.

Technical Abstract: Several remotely sensed indices have been used to infer crop nitrogen status for the purpose of generating variable rate application maps. Some of these indices could falsely indicate nitrogen stress if there is a decrease in crop cover that is due to other factors such as water stress or pest infestations. The Canopy Chlorophyll Content Index (CCCI) uses reflectance in the near-infrared (NIR) and red spectral regions to compensate for changes in canopy density, while reflectance factors in the far red (720 nm) and NIR reflectance factors detect relative changes in canopy chlorophyll or N content. The objective was to evaluate CCCI as an index of nitrogen status for cotton (Gossypium hirsutum L.) grown during 1998 and 1999 at the Maricopa Agricultural Center in south central Arizona. The experiments included treatments of optimum and low levels of nitrogen and irrigation. The CCCI was able to detect the low nitrogen treatments earlier than the normalized difference vegetation index or ratio of NIR to red (RVI). For any given date of measurement, the CCCI consistently provided better estimates (r>0.80) of total leaf N in cotton than did the SPAD chlorophyll readings (using SPAD-502 Chlorophyll Meter) of leaves. Although the CCCI was poorly correlated with cotton leaf N (r = 0.30) across the entire season, the correlation coefficient rose to 0.86 when dates with acute water stress were excluded. Currently the CCCI shows promise for variable rate N management if calibrated on a site- and time-specific basis. Further study is required to determine if a relationship can be formulated that will allow a single calibration relationship to be applied for a majority of the season.