Pixel-Based Calibration and Atmospheric Correction of a UAS-Mounted Thermal Camera for Land Surface Temperature Measurements

Abstract

HighlightsA novel pixel-based calibration algorithm and an atmospheric correction method are developed.Application of the calibration methods reduces the RMSE of measurements to less than 1.32°C.The calibrations facilitate stitching of images together to form whole-field mosaics.Abstract. Thermal imagery can be used to provide insight into the water stress status and evapotranspiration demand of crops, but satellite-based sensors are generally too coarse spatially and too infrequent temporally to provide information of use for the management of specific fields. Thermal cameras mounted on small unmanned aerial systems (UAS) have potential to provide canopy temperature information at high spatial and temporal resolutions useful for crop management; however, without appropriate camera corrections, the measurement biases of these uncooled thermal cameras can be larger than ±5°C. Such uncertainty can render such camera measurements useless. In this research, a pixel-based (non-uniformity) calibration algorithm and an atmospheric correction method based on in-field approximate blackbody sources (water targets) were developed for a thermal camera. The objective was to improve the temperature measurement accuracy of the thermal camera on various land surfaces including soil and vegetation. With sufficient accuracy, temperature measurements can be used for the estimation of latent heat flux of field crops in the future. The thermal camera was first calibrated in a laboratory setting where the camera and environmental conditions were controlled. The results indicated that in the range between 10°C and 45°C, the calibrated temperatures were accurate, with an average bias of 1.76°C, and had a high linear correlation with reference temperatures (water target temperatures) (R2 > 0.99). Variability of measurements was also better constrained. In-field atmospheric correction is also important for obtaining high-accuracy thermal imagery. By applying both pixel-based calibration and atmospheric corrections, the RMSE (root mean square error) of validation targets from two dates in 2017 was reduced from 4.56°C and 6.36°C before calibration to 1.32°C and 1.24°C after calibration. The calibration process also increased the range of temperatures in the imagery, which enhanced contrast and may help with identification of tie-points and stitching of images together to form whole-field mosaics. Keywords: Atmospheric correction, Pixel-based calibration, Thermal remote sensing, UAS, Water targets.