Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high-and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68◦ C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0◦ C in the un-calibrated image to 1.01◦ C in the calibrated image. Furthermore, a multiple linear regression model (R2 = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.
CITATION STYLE
Han, X., Thomasson, J. A., Swaminathan, V., Wang, T., Siegfried, J., Raman, R., … Neely, H. (2020). Field-based calibration of unmanned aerial vehicle thermal infrared imagery with temperature-controlled references. Sensors (Switzerland), 20(24), 1–15. https://doi.org/10.3390/s20247098
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