Journal article

Cotton irrigation scheduling using remotely sensed and FAO-56 basal crop coefficients

Hunsaker D, Barnes E, Clarke T, Fitzgerald G, Pinter Jr P ...see all

Transactions of the ASAE, vol. 48, issue 4 (2005) pp. 1395-1407

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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.

Author-supplied keywords

  • agricultura
  • agriculture
  • agua de riego
  • algodón
  • aménagement hydraulique
  • aprovechamiento hidráulico
  • balance agua
  • besoin hydrique
  • bilan eau
  • calendario de los riegos
  • calendrier des irrigations
  • característica suelo
  • caractéristique sol
  • coefficient cultural
  • coeficiente cultural
  • coeficiente reflexión
  • coton
  • cotton
  • couvert végétal
  • crop canopy reflectance
  • crop coefficient
  • cubierta vegetal
  • eau irrigation
  • evapotranspiración
  • evapotranspiration
  • facteur réflexion
  • fao
  • fao (organización para la alimentación y la agricu
  • humedad suelo
  • humidité sol
  • indice de vegetación
  • indice végétation
  • irrigación
  • irrigation
  • irrigation scheduling
  • irrigation water
  • irrigation water requirements
  • necesidad hidráulica
  • normalized difference vegetation index
  • physical properties
  • plant cover
  • property soil
  • propiedad física
  • propiedad hídrica
  • propriété hydrique sol
  • propriété physique
  • reflectance
  • remote sensing
  • soil moisture
  • soil water balance
  • soil water properties
  • teledetección
  • télédétection
  • vegetation index
  • water balance
  • water engineering
  • water requirement

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  • DJ Hunsaker

  • EM Barnes

  • TR Clarke

  • GJ Fitzgerald

  • PJ Pinter Jr

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