Field-Based Calibration of Unmanned Aerial Vehicle Thermal Infrared Imagery with Temperature-Controlled References.

Abstract

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 ( R 2 = 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.

Code and data availability

The supplied blocks describe UAV thermal image calibration methods, field experiments, and results, but contain no data availability statement, no public dataset deposit, no author code/scripts, and no trained model release. The only URL present is the article's CC BY license notice, which is not a paper-specific asset

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