Introduction: Detecting and monitoring crop stress is crucial for ensuring sufficient and sustainable crop production. Recent advancements in unoccupied aerial vehicle (UAV) technology provide a promising approach to map key crop traits indicative of stress. While using single optical sensors mounted on UAVs could be sufficient to monitor crop status in a general sense, implementing multiple sensors that cover various spectral optical domains allow for a more precise characterization of the interactions between crops and biotic or abiotic stressors. Given the novelty of synergistic sensor technology for crop stress detection, standardized procedures outlining their optimal use are currently lacking. Materials and methods: This study explores the key aspects of acquiring high-quality multi-sensor data, including the importance of mission planning, sensor characteristics, and ancillary data. It also details essential data pre-processing steps like atmospheric correction and highlights best practices for data fusion and quality control. Results: Successful multi-sensor data acquisition depends on optimal timing, appropriate sensor calibration, and the use of ancillary data such as ground control points and weather station information. When fusing different sensor data it should be conducted at the level of physical units, with quality flags used to exclude unstable or biased measurements. The paper highlights the importance of using checklists, considering illumination conditions and conducting test flights for the detection of potential pitfalls. Conclusion: Multi-sensor campaigns require careful planning not to jeopardise the success of the campaigns. This paper provides practical information on how to combine different UAV-mounted optical sensors and discuss the proven scientific practices for image data acquisition and post-processing in the context of crop stress monitoring.

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.