COTTON IRRIGATION SCHEDULING USING REMOTELY SENSED AND FAO-56 BASAL CROP COEFFICIENTS
Multispectral vegetation indices calculated from canopy reflectance measurements have been used to simulate 
real-time basal crop coefficients (Kcb), which have been validated to improve evapotranspiration (ETc) estimation for several 
crops. In this article, an application of the approach was evaluated for cotton using remote sensing observations of the 
normalized difference vegetation index (NDVI) to estimate Kcb as a function of NDVI. The dual crop coefficient procedures 
of FAO Paper 56 (FAO-56) were used to calculate ETc and determine irrigation scheduling using Kcb estimates from remote 
sensing (NDVI treatment) as well as from time-based Kcb curves (FAO treatment), which were developed locally for standard 
crop conditions using FAO-56 procedures. Two cotton experiments, conducted in 2002 and 2003 in central Arizona, included 
sub-treatments of three levels of plant density and two levels of nitrogen management to impose a wide range of crop 
development and water use. The NDVI-Kcb relationships used for 2002, developed from previous data for a different cotton 
cultivar, row orientation, and soil type, substantially underestimated ETc, resulting in significantly less irrigation water 
applied and lower lint yields for NDVI compared to the FAO treatment. The 2002 data were used to recalibrate the NDVI-Kcb 
relationships, which were then used for the NDVI treatments in 2003. The FAO Kcb curve used in 2002 described ETc and 
irrigation scheduling reasonably well for sparse plots, but consistently underestimated water use and soil water depletion 
for the higher plant densities during the first half of the season. Consequently, an adjusted FAO Kcb curve, based on 2002 
results, was employed for the FAO treatment in 2003. For the 2003 experiment, estimated cotton ETc for the NDVI treatment 
resulted in a mean absolute error of 9% compared to 10% for the FAO treatment, where the difference was not significant 
between treatments. However, the NDVI-Kcb relations used in 2003 greatly improved estimates for ETc compared to the 
previous year, where the mean absolute error for the NDVI treatment in 2002 was 22%. Predicted ETc using the FAO Kcb curve 
of 2003 for typical planting density and high nitrogen conditions resulted in a mean absolute error of 10% compared to 15% 
in 2002. Final lint yields for 2003 were not significantly different between the two Kcb methods. Although additional research 
is needed to validate remote sensing Kcb estimation for other conditions than those in these experiments, this study did not 
show significant advantages for the NDVI approach over a carefully derived single FAO Kcb application. However, the NDVI 
approach has the potential to further extend our present crop coefficient estimation capabilities when weather, plant density, 
or other factors cause cotton canopy development and water use patterns to depart from typical conditions.