Evaluation of stress coefficient methods to estimate crop evapotranspiration

Abstract

Increased competition for water resources is placing pressure on the agricultural sector to remain profitable while reducing water use. Remote sensing techniques have been developed to monitor crop water stress and produce information for evapotranspiration (ET) based irrigation scheduling decisions. Use of stress detection methods allows producers to avoid exceeding set crop water stress levels and keep operations sustainable under limited irrigation despite some yield reduction. Remote sensing data such as spectral reflectance and infrared canopy temperature can be used to quantify crop water stress, often through the use of vegetation indices calculated from the near-infrared and red bands and temperature indices calculated from the thermal wavelength, respectively. Reference ET methods estimate water use based on crop characteristics and climactic parameters assuming optimum soil water conditions. In order to adjust crop ET for water limited conditions such as drought or water allocation restrictions, ET scaling techniques that are sensitive to crop development and stress are necessary. The performance of five remote sensing techniques to estimate corn ET under drought conditions in Northern Colorado were evaluated: one method based on air temperature, canopy temperature and relative humidity (Crop Water Stress Index (CWSI)), three methods based strictly on canopy temperature including Degrees Above Non-Stress (DANS), Degrees above Canopy Threshold (DACT), and Temperature Ratio, and one method based on multispectral vegetation indices (NDVI Ratio). Data were collected during 2010 through 2013 growing seasons at the USDA-ARS Limited Irrigation Research Farm near Greeley, CO. Varying water deficit levels were imposed on corn (Zea mays L.) under pressurized drip irrigation. ET estimates from the five remote sensing techniques were compared to soil water balance (via neutron probe) and ET calculations. 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 (CWSI = 0.77 mm/day, DANS = 0.80 mm/day, DACT = 0.80 mm/day, Tc Ratio = 0.83 mm/day, NDVI Ratio = 0.85 mm/day). Detailed tables 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. Using the most appropriate stress coefficient method has the potential to improve irrigation scheduling and therefore allow crops to reach the maximum possible yield given the level of deficit irrigation. Methods with fewer data requirements, such as DACT with only a single canopy temperature measurement requirement, may be more appropriate to improve on-farm water management in certain situations. Results justify use of simplified measures of stress to improve deficit irrigation water management with limited data.