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Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients

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  • Kullberg, Emily G.
  • DeJonge, Kendall C.
  • Chávez, José L.

Abstract

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.

Suggested Citation

  • Kullberg, Emily G. & DeJonge, Kendall C. & Chávez, José L., 2017. "Evaluation of thermal remote sensing indices to estimate crop evapotranspiration coefficients," Agricultural Water Management, Elsevier, vol. 179(C), pages 64-73.
    • Handle: RePEc:eee:agiwat:v:179:y:2017:i:c:p:64-73
    DOI: 10.1016/j.agwat.2016.07.007
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    1. Alderfasi, Ali Abdullah & Nielsen, David C., 2001. "Use of crop water stress index for monitoring water status and scheduling irrigation in wheat," Agricultural Water Management, Elsevier, vol. 47(1), pages 69-75, February.
    2. O'Shaughnessy, S.A. & Evett, S.R., 2010. "Canopy temperature based system effectively schedules and controls center pivot irrigation of cotton," Agricultural Water Management, Elsevier, vol. 97(9), pages 1310-1316, September.
    3. DeJonge, Kendall C. & Taghvaeian, Saleh & Trout, Thomas J. & Comas, Louise H., 2015. "Comparison of canopy temperature-based water stress indices for maize," Agricultural Water Management, Elsevier, vol. 156(C), pages 51-62.
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