EVAPOTRANSPIRATION


provided by local extension agents or calculated according to FAO-56 methodology (see fig. 1).


Estimating ETC for deficit-irrigated crops requires additional information on either soil moisture or canopy temperature to calculate a water stress coefficient, but the same basic framework can be applied.


Although this approach is not without flaws, it has been utilized since the mid-1970s, in part due to its practicality. Alternative methods have been proposed, such as the Matt-Shuttleworth approach and satellite-based energy budget approaches, but they have failed to gain significant traction within the ag irrigation community due to more complex model parameterization requirements.


Utilizing satellite images


Several recent technological advances offer promis- ing ways to refine the basic ETO  KC approach. For example, crop coefficients can now be estimated from airborne- and satellite-based normalized difference vegetation index (known as NDVI) measurements using one of several empirical relationships (see fig. 2).


Whether you are providing the full crop water requirement  knowing the ET for your crop provides a powerful irrigation management tool.


Historically, satellite NDVI data were of limited use in irrigation management due to the lengthy return times resulting from a


limited number of satellites in orbit. But with the increase in the number of commercial satellite constellations and airborne platforms over the last several years, NDVI products are now available with greater spatial and temporal resolution. As a result, some companies have begun integrating ground and satellite data sources to provide near real-time maps of ETC (see fig. 3).


ETO forecasting In another recent advance, the National Oceanic and Atmospheric Administration’s National Weather


Service now provides a seven-day reference ETO forecast, known as FRET, for the contiguous United States. Ground-based ETO monitoring networks have proven invaluable to growers and the irrigation management community, but generally they have only provided access to current and historical ETO data. In many situations, forecasted ETO is also of value.


The NOAA’s FRET forecast addresses this need using a dynamical weather model. Dynamical models, which have been in use since the 1950s, are gridded,


24 Irrigation TODAY | Winter 2020


KC


1.4 1.2 1.0 0.8 0.6 0.4 0.2 0


Kc mid


Kc ini


Kc end time (days) initial crop development mid season


FAO-56 approach Source: University of Nebraska Extension Service, 2009


Converting NDVI to KC: Kamble et al. (2013) vs. NASA SIMS


1.4 1.2 1.0 0.8 0.6 0.4 0.2 0


0 0.2 0.4 Kamble et al. (2013) 0.6 NDVI NASA SIMS


Figure 2. Relationship between NDVI and KC from Kamble et al. (2013) and the NASA Satellite Irrigation Management Support program


0.8 1.0 1.2 late season Figure 1. Estimating crop coefficients (KC) using the


Figure 3. Left to right: NDVI from the ESA Sentinel-2 satellite platform (10 m resolution), NDVI-derived block


averaged KC, and ETC (mm) for a California vineyard Source: L. Pierce


Figure 4. Image shows a schematic of a gridded numerical weather model. Agricultural fields are denoted with


red dots. Source: NOAA


irrigationtoday.org


KC


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