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Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods

Department of Crop, Soil, and Environmental Sciences, 201 Funchess Hall, Auburn University, Auburn, AL 36849, USA
Department of Biosystems Engineering, 207 Corley Building, Auburn University, Auburn, AL 36849, USA
Department of Physics, 206 Allison Laboratories, Auburn University, Auburn, AL 36849, USA
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(16), 1917;
Received: 9 July 2019 / Revised: 9 August 2019 / Accepted: 14 August 2019 / Published: 16 August 2019
(This article belongs to the Section Remote Sensing Image Processing)
PDF [7604 KB, uploaded 16 August 2019]


Unmanned aircraft systems (UAS) allow us to collect aerial data at high spatial and temporal resolution. Raw images are taken along a predetermined flight path and processed into a single raster file covering the entire study area. Radiometric calibration using empirical or manufacturer methods is required to convert raw digital numbers into reflectance and to ensure data accuracy. The performance of five radiometric calibration methods commonly used was investigated in this study. Multispectral imagery was collected using a Parrot Sequoia camera. No method maximized data accuracy in all bands. Data accuracy was higher when the empirical calibration was applied to the processed raster rather than the raw images. Data accuracy achieved with the manufacturer-recommended method was comparable to the one achieved with the best empirical method. Radiometric error in each band varied linearly with pixel radiometric values. Smallest radiometric errors were obtained in the red-edge and near-infrared (NIR) bands. Accuracy of the composite indices was higher for the pixels representing a dense vegetative cover in comparison to a lighter cover or bare soil. Results provided a better understanding of the advantages and limitations of existing radiometric calibration methods as well as the impact of the radiometric error on data quality. The authors recommend that researchers evaluate the performance of their radiometric calibration before analyzing UAS imagery and interpreting the results. View Full-Text
Keywords: aerial imagery; drone; empirical calibration; radiometric error; error propagation; vegetation indices aerial imagery; drone; empirical calibration; radiometric error; error propagation; vegetation indices

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Poncet, A.M.; Knappenberger, T.; Brodbeck, C.; Fogle, M., Jr.; Shaw, J.N.; Ortiz, B.V. Multispectral UAS Data Accuracy for Different Radiometric Calibration Methods. Remote Sens. 2019, 11, 1917.

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