Abstract
Unmanned aerial system (UAS) remote sensing has rapidly expanded in recent years, leading to the development of several multispectral and thermal infrared sensors suitable for UAS integration. Remotely sensed thermal infrared imagery has been used to detect crop water stress and manage irrigation by leveraging the increased thermal signatures of water stressed plants. Thermal infrared cameras suitable for UAS remote sensing are often uncooled microbolometers. This type of thermal camera is subject to inaccuracies not typically present in cooled thermal cameras. In addition, atmospheric interference also may present inaccuracies in measuring surface temperature. In this study, a UAS with integrated FLIR Duo Pro R (FDPR) thermal camera was used to collect thermal imagery over a maize and soybean field that contained twelve infrared thermometers (IRT) that measured surface temperature. Surface temperature measurements from the UAS FDPR thermal imagery and field IRTs corrected for emissivity and atmospheric interference were compared to determine accuracy of the FDPR thermal imagery. The comparison of the atmospheric interference corrected UAS FDPR and IRT surface temperature measurements yielded a RMSE of 2.24 degree Celsius and a R2 of 0.85. Additional approaches for correcting UAS FDPR thermal imagery explored linear, second order polynomial and artificial neural network models. These models simplified the process of correcting UAS FDPR thermal imagery. All three models performed well, with the linear model yielding a RMSE of 1.27 degree Celsius and a R2 of 0.93. Laboratory experiments also were completed to test the measurement stability of the FDPR thermal camera over time. These experiments found that the thermal camera required a warm-up period to achieve stability in thermal measurements, with increased warm-up duration likely improving accuracy of thermal measurements.
Highlights
Unmanned aerial system (UAS) remote sensing has gained significant traction in the last decade, leading to the development of various UAS and sensor payloads
Thermal infrared remote sensing is used in various agricultural applications and models for determining water stress and plant health
Satellite and manned aircraft remote sensing platforms have been the primary means for collecting remotely sensed imagery while UAS have recently gained a greater foothold in remote sensing due to their flexibility and lower cost
Summary
Unmanned aerial system (UAS) remote sensing has gained significant traction in the last decade, leading to the development of various UAS and sensor payloads. Typical remote sensing platforms like satellite and manned aircraft have limitations due to the lack of spatiotemporal resolution and high cost. UASs provide a less expensive method of remote sensing and offer greater opportunity and flexibility to collect high resolution data usable in various applications. Satellite and manned aircraft remote sensing have previously provided data shown to be beneficial to agronomic applications. These data have been used to predict various crop biophysical characteristics such as leaf area index (LAI), crop height, fraction of vegetative cover, crop coefficient, crop evapotranspiration (ET) and phenotyping. Multispectral reflectance and vegetation indices have been used to model LAI, canopy height and fraction of vegetative cover [1,2,3,4,5]. Neale et al [6] used canopy reflectance measured with portable radiometers and the normalized difference
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