LAUSR

HighlightsStationary and moving infrared thermometers aboard a center pivot were compared.Comparisons were in terms of directional brightness temperature discrepancies.Discrepancies were within 1.8°C, and many were within 1.0°C.Larger discrepancies tended to occur for sparser vegetation cover.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 root zone 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 (TB) measured by stationary and moving IRTs were compared over two corn and one potato season at the USDA Agricultural Research Service, Bushland, Texas. Moving and stationary TB were compared in terms of root mean square error, mean absolute error, and mean bias error, and were all < 1.8°C, and many were < 1.0°C, and r2 = 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, TB measurements of moving IRTs did not appear to be degraded relative to TB measurements of stationary IRTs for this study. However, interpretation of stationary and moving IRT measurements may be aided by addition of low cost imagers to distinguish vegetation from soil background. Keywords: Canopy temperature, Crop management, Evapotranspiration, Irrigation, Remote sensing, Sensors.