COTTON IRRIGATION SCHEDULING USING REMOTELY SENSED AND FAO-56 BASAL CROP COEFFICIENTS

摘要

Multispectral vegetation indices calculated from canopy reflectance measurements have been used to simulate real-time basal crop coefficients (K{sub}(cb)), which have been validated to improve evapotranspiration (ET{sub}c) 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 K{sub}(cb) as a function of NDVI. The dual crop coefficient procedures of FAO Paper 56 (FAO-56) were used to calculate ET{sub}c and determine irrigation scheduling using K{sub}(cb) estimates from remote sensing (NDVI treatment) as well as from time-based K{sub}(cb) 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-K{sub}(cb) relationships used for 2002, developed from previous data for a different cotton cultivar, row orientation, and soil type, substantially underestimated ET{sub}c, 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-K{sub}(cb) relationships, which were then used for the NDVI treatments in 2003. The FAO K{sub}(cb) curve used in 2002 described ET{sub}c 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 K{sub}(cb) curve, based on 2002 results, was employed for the FAO treatment in 2003. For the 2003 experiment, estimated cotton ET{sub}c 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-K{sub}(cb) relations used in 2003 greatly improved estimates for ET{sub}c compared to the previous year, where the mean absolute error for the NDVI treatment in 2002 was 22%. Predicted ET{sub}c using the FAO K{sub}(cb) 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 K{sub}(cb) methods. Although additional research is needed to validate remote sensing K{sub}(cb) 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 K{sub}(cb) 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.