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ARS Home » Plains Area » Bushland, Texas » Conservation and Production Research Laboratory » Soil and Water Management Research » Research » Publications at this Location » Publication #362752

Research Project: Precipitation and Irrigation Management to Optimize Profits from Crop Production

Location: Soil and Water Management Research

Title: Comparison of stationary and moving infrared thermometer measurements aboard a center pivot

Author
item Colaizzi, Paul
item O`Shaughnessy, Susan
item Evett, Steven - Steve
item ANDRADE, MANUEL - Orise Fellow

Submitted to: Applied Engineering in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/16/2019
Publication Date: 12/16/2019
Publication URL: https://handle.nal.usda.gov/10113/6811647
Citation: Colaizzi, P.D., O'Shaughnessy, S.A., Evett, S.R., Andrade, M.A. 2019. Comparison of stationary and moving infrared thermometer measurements aboard a center pivot. Applied Engineering in Agriculture. 35(6):853-866. https://doi.org/10.13031/aea.13443.
DOI: https://doi.org/10.13031/aea.13443

Interpretive Summary: In times of low crop prices, farmers need to produce crops as cheaply as possible. One way farmers can decrease input costs is to apply irrigation only as needed. Crop leaf temperature can be easily measured by thermal sensors. Center pivot irrigation machines have been shown to be an excellent method to transport thermal sensors over a cropped field. Used in this way, thermal sensors can help farmers determine crop irrigation needs for an entire center pivot field. But, users of thermal sensors have been concerned that moving thermal sensor measurements may not be as accurate as non-moving (stationary) sensors. Therefore, scientists at the USDA Agricultural Research Service in Bushland, Texas, compared stationary and moving thermal sensors aboard a center pivot. They found there were no differences in accuracy between stationary or moving thermal sensors. They also found that thermal sensor accuracy was the same as that reported in other studies, such as where drones were used. Center pivots are now used on over half (30 million acres) of the irrigated acreage (57 million acres) in the USA, which continues to grow. Installing thermal sensors aboard center pivots and using them for irrigation scheduling could save farmers substantial water and energy.

Technical Abstract: Infrared thermometers (IRTs) can measure canopy temperature, which is useful for irrigation and crop management. Center pivot and lateral move irrigation systems are suitable platforms to transport IRTs across cropped fields at regular intervals. IRTs aboard center pivots, when used in conjunction with irrigation scheduling algorithms, have resulted in crop yield and crop water productivity that is equivalent to or greater than what can be achieved using soil water measurements of the profile with a field-calibrated neutron probe. Irrigation scheduling algorithms perform best when stationary IRT measurements are supplemented with moving IRT arrays, where the former provides time series data and the latter provides spatially distributed data. However, the normal deflection of moving irrigation systems and other confounding factors have caused concern that moving IRT measurements may be degraded relative to stationary IRT measurements. Directional brightness temperatures measured by stationary and moving IRTs were compared over two corn and one potato season at the USDA Agricultural Research Service, Bushland, Texas, USA. Root mean square error, mean absolute error, and mean bias error were all less than 1.8 C, and many were less than 1.0 C, and r2 greater than 0.95. Error terms tended to be larger for potato, which had less vegetation cover compared with corn. However, error terms were similar to previous studies of calibration and spatial variability for IRTs and thermal imagers. Therefore, noise that may be introduced by roll, pitch, or raw of an IRT aboard a center pivot does not appear to be a concern. However, interpretation of stationary and moving IRT measurements may be aided by addition of low cost imagers to distinguish vegetation from soil background.