Data quality control for stationary infrared thermometers viewing crops

The increased adoption of infrared thermometers (IRTs) for irrigation management of crops has resulted in increasingly large surface temperature datasets, resulting in a need for data quality assurance and control (QA/QC) procedures similar to those developed for agricultural weather station data. A QC procedure was developed to test for seven common data conditions, including sensor not deployed, missing, too high, too low, upward spike, downward spike, or stuck. The conditions of "too high" or "too low" used a simple energy balance procedure similar to the crop water stress index, where the theoretical lower and upper temperature limits of a surface were calculated, accounting for the vegetation view factor appearing in the IRT field-of-view. After passing the seven tests, data were assigned as Plausible, and further tested as Confirmed or Confirmed+. The Confirmed test compared each IRT to the median of the other IRTs during 2 h before sunrise and applied a threshold of 1-0.5 degrees C. The Confirmed+ test compared each IRT to the median of the other IRTs during 1-2 h of solar noon and applied a threshold of 1-2.0 degrees C. The set of tests was applied to an IRT dataset that included six seasons of crops and fallow periods with 15-min time steps. Temperature differences greater than the thresholds (i.e., assigned Plausible but not Confirmed or Confirmed+) could detect anomalies including ice, dirty or obscured lenses, or biased data that other tests did not detect. Of all time intervals when 20 IRTs viewing a crop were deployed, 80% resulted in Plausible, 61% resulted in Confirmed, and 56% resulted in Confirmed+. The procedure can be easily customized and can increase the value of IRT datasets used for irrigation management.