From the 1970s on, satellite-based thermal infrared (TIR) remote sensing has been applied widely to regional-scale investigations. In this century, the technological development in easy-to-use low-cost miniaturized TIR cameras has enabled their application on unmanned aerial vehicles (UAV) in addition to proximal platforms for local-scale studies.
This approach has started to be adopted in a variety of agricultural applications, e.g., for crop water stress (CWS) detection. The detection of CWS’s development requires detecting subtle temperature changes, which might not be as crucial for other applications. To enhance the actual performance of uncooled TIR cameras in the field practices, the multiple sources of errors in measurements should be clarified beforehand. Different models of uncooled TIR detectors may influence the cameras’ performance to a large extent. Their intrinsic characteristics restrict the accuracy and sensitivity of the sensors. A series of external influencing factors can also influence the camera performance, such as the emissivity of the target object, the atmospheric conditions, and so forth.
As part of his PhD research, Quanxing Wan has been working with Lammert Kooistra, Benjamin Brede, and Magdalena Smigaj on exploring the feasibility of applying different types of miniaturized TIR cameras to field practices requiring high accuracy, such as crop water stress mapping. The team practiced with various models of miniaturized TIR cameras and presented the laboratory-based measurement results of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. The observations indicated that either internal or environmental factors could vary the measured temperature up to several degrees. Notably, the automated non-uniformity corrections (NUCs) for certain TIR camera models might bring seemingly random changes to measurements. Accordingly, we provided attention points in the actual field practices for deriving more accurate temperature values from radiometric miniaturized TIR cameras. Follow-up studies will focus on testing the recommended list of suggestions in crop fields in their growing seasons. The workflow of acquiring trustworthy temperature measurements outdoors will be optimized and further applied to water stress mapping which demands high precision.
The results of the study were published in a paper entitled ‘Factors Influencing Temperature Measurements from Miniaturized Thermal Infrared (TIR) Cameras: A Laboratory-Based Approach’ in Sensors.
The workflow for estimating the temperature in agricultural fields from multiple sensors needs to be optimized upon testing each type of sensor’s actual user performance. In this sense, readily available miniaturized UAV-based thermal infrared (TIR) cameras can be combined with proximal sensors in measuring the surface temperature. Before the two types of cameras can be operationally used in the field, laboratory experiments are needed to fully understand their capabilities and all the influencing factors. We present the measurement results of laboratory experiments of UAV-borne WIRIS 2nd GEN and handheld FLIR E8-XT cameras. For these uncooled sensors, it took 30 to 60 min for the measured signal to stabilize and the sensor temperature drifted continuously. The drifting sensor temperature was strongly correlated to the measured signal. Specifically for WIRIS, the automated non-uniformity correction (NUC) contributed to extra uncertainty in measurements. Another problem was the temperature measurement dependency on various ambient environmental parameters. An increase in the measuring distance resulted in the underestimation of surface temperature, though the degree of change may also come from reflected radiation from neighboring objects, water vapor absorption, and the object size in the field of view (FOV). Wind and radiation tests suggested that these factors can contribute to the uncertainty of several Celsius degrees in measured results. Based on these indoor experiment results, we provide a list of suggestions on the potential practices for deriving accurate temperature data from radiometric miniaturized TIR cameras in actual field practices for (agro-)environmental research.
Keywords: UAV; thermal infrared; FLIR; radiometric; calibration; temperature; non-uniformity correction; stabilization; sensor temperature; ambient environment