Abstract
The miniaturization of thermal infrared sensors suitable for integration with unmanned aerial vehicles (UAVs) has provided new opportunities to observe surface temperature at ultra-high spatial and temporal resolutions. In parallel, there has been a rapid development of software capable of streamlining the generation of orthomosaics. However, these approaches were developed to process optical and multi-spectral image data and were not designed to account for the often rapidly changing surface characteristics inherent in the collection and processing of thermal data. Although radiometric calibration and shutter correction of uncooled sensors have improved, the processing of thermal image data remains difficult due to (1) vignetting effects on the uncooled microbolometer focal plane array; (2) inconsistencies between images relative to in-flight effects (wind-speed and direction); (3) unsuitable methods for thermal infrared orthomosaic generation. Here, we use thermal infrared UAV data collected with a FLIR-based TeAx camera over an agricultural field at different times of the day to assess inconsistencies in orthophotos and their impact on UAV-based thermal infrared orthomosaics. Depending on the wind direction and speed, we found a significant difference in UAV-based surface temperature (up to 2 °C) within overlapping areas of neighboring flight lines, with orthophotos collected with tail wind being systematically cooler than those with head wind. To address these issues, we introduce a new swath-based mosaicking approach, which was compared to three standard blending modes for orthomosaic generation. The swath-based mosaicking approach improves the ability to identify rapid changes of surface temperature during data acquisition, corrects for the influence of flight direction relative to the wind orientation, and provides uncertainty (pixel-based standard deviation) maps to accompany the orthomosaic of surface temperature. It also produced more accurate temperature retrievals than the other three standard orthomosaicking methods, with a root mean square error of 1.2 °C when assessed against in situ measurements. As importantly, our findings demonstrate that thermal infrared data require appropriate processing to reduce inconsistencies between observations, and thus, improve the accuracy and utility of orthomosaics.
Highlights
As the global population continues to grow, food demand is expected to increase by50% in 2050
Our research results clearly demonstrated that the existing blending modes (e.g., Disable, Average and Mosaic in Agisoft PhotoScan) for orthomosaic generation were not suited for the thermal data acquired with the flight planning configurations used especially during windy conditions and with the wind direction aligned within 50◦ of the flight direction
With a difference of up to 2 ◦ C between inbound and outbound swaths (Figure 7), we demonstrated the effect of the wind speed and direction and its impacts on the ability to retrieve accurate surface temperature from orthomosaics
Summary
As the global population continues to grow, food demand is expected to increase by50% in 2050. Precision agriculture has emerged as a potential solution to this problem and aims to optimize management practices by providing needed farm inputs of the right amount, at the right time, and in the right place [2]. Central to this concept is the dynamic monitoring and response to intra-field variability in both plants and soil. While satellite systems have become a useful tool for various agricultural applications [3], they are often limited by orbital configuration, leading to a compromise between the achievable spatial and temporal resolution. With recent advances in nearEarth observation [4], unmanned aerial vehicles (UAVs) offer an appealing compromise for obtaining data with ultra-high spatial resolution in real-time and on-demand [5,6]
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