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The use of small unmanned aircraft systems (sUAS) for applications in thefield of precision agriculture has demonstrated the need to produce temporallyconsistent imagery to allow for quantitative comparisons. In order for theseaerial images to be used to identify actual changes on the ground, conversionof raw digital count to reflectance, or to an atmospherically normalized space,needs to be carried out. This paper will describe an experiment that comparesthe use of reflectance calibration panels, for use with the empirical linemethod (ELM), against a newly proposed ratio of the target radiance and thedownwelling radiance, to predict the reflectance of known targets in the scene.We propose that the use of an on-board downwelling light sensor (DLS) mayprovide the sUAS remote sensing practitioner with an approach that does notrequire the expensive and time consuming task of placing known reflectancestandards in the scene. Three calibration methods were tested in this study:2-Point ELM, 1-Point ELM, and At-altitude Radiance Ratio (AARR). Our studyindicates that the traditional 2-Point ELM produces the lowest mean error inband effective reflectance factor, 0.0165. The 1-Point ELM and AARR producemean errors of 0.0343 and 0.0287 respectively. A modeling of the proposed AARRapproach indicates that the technique has the potential to perform better thanthe 2-Point ELM method, with a 0.0026 mean error in band effective reflectancefactor, indicating that this newly proposed technique may prove to be a viablealternative with suitable on-board sensors.

An initial exploration of vicarious and in-scene calibration techniques for small unmanned aircraft systems