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Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients

Kullberg, Emily G., DeJonge, Kendall C., Chávez, José L.
Agricultural water management 2017 v.179 pp. 64-73
Zea mays, canopy, corn, crop coefficient, deficit irrigation, evapotranspiration, irrigation rates, normalized difference vegetation index, reflectance, remote sensing, soil water, soil water deficit, spectral analysis, temperature, transpiration, water stress, wavelengths, Colorado
Remotely sensed data such as spectral reflectance and infrared canopy temperature can be used to quantify crop canopy cover and/or crop water stress, often through the use of vegetation indices calculated from the near-infrared and red bands, and stress indices calculated from the thermal wavelengths. Standardized dual crop coefficient methods calculate both a non-stressed transpiration coefficient (Kcb) that is related to canopy cover, and a stress or transpiration reduction coefficient (Ks) that can be related to soil water deficit or other stress factors (e.g. disease). This study compares several remote sensing methods to determine Kcb and Ks and resulting evapotranspiration (ET) in a deficit irrigation experiment of corn (Zea mays L.) near Greeley, Colorado. Three methods were used to calculate Kcb (tabular, normalized difference vegetation index – NDVI, and canopy cover). Four canopy temperature based methods were used to calculate Ks: Crop Water Stress Index – CWSI, Canopy Temperature Ratio – Tcratio, Degrees Above Non-Stressed – DANS, Degrees Above Canopy Threshold – DACT. Crop ET predicted by these methods was compared to observation and water balance based ET measurements. Thermal indices DANS and DACT were calibrated to convert to Ks. Results showed that stress coefficient methods with less data requirements such as DANS and DACT are responsive to crop water stress as demonstrated by low RMSE of ET calculations, comparable to more data intensive methods such as CWSI. Results indicate which remote sensing methods are appropriate to use given certain data availability and irrigation level, in addition to providing an estimation of the associated error in ET.
  USDA-ARS Colorado Maize Water Productivity Dataset 2012-2013
  USDA-ARS Colorado Maize Water Productivity Dataset 2008-2011