![]() In particular, the use of multispectral (visible and Near-Infrared or NIR) sensors on UAS platforms for vegetation assessment and monitoring has expanded dramatically over the last decade, most notably in the realm of agriculture and precision farming (e.g., Deng et al., 2018, Jin et al., 2017, Stagakis et al., 2012, Zhang and Kovacs, 2012, Laliberte et al., 2011). In addition, UAS can carry a variety of digital sensors, including multispectral, hyperspectral, thermal, and LiDAR, which makes these systems attractive to a wide range of users in both the private and public sectors (Toth and Józków, 2016, Aasen and Bolten, 2018, Webster et al., 2018, Liu et al., 2018). This includes the ability to acquire ultra-high spatial resolution imagery at a relatively low cost in a wide range of different environments and at time steps that are dictated by the user (Berni et al., 2009, Ambrosia et al., 2003, Yuan et al., 2015, Dall’Asta et al., 2017). Compared to traditional airborne or satellite-based platforms, UAS have several unique advantages. Recent advances in Unmanned Aerial Systems (UAS) have made these instruments increasingly popular for on-demand imagery acquisition for a variety of research and commercial applications (Colomina and Molina, 2014). In light of these results, we propose simplified procedures that can be adopted by UAS operators to periodically assess the radiometric fidelity of their multispectral sensors. Results revealed measurement variability over time, suggesting that daily differences in solar illumination and atmospheric conditions may influence derived reflectance values. 3 included image acquisition of ground reference targets using the MicaSense RedEdge sensor over seventeen sequential field surveys. 2 involved a calculation of Normalized Difference Vegetation Index (NDVI) values at field control points using both UAS sensors, and we found a strong linear relationship between the NDVI values and measurements made by a hand-held NDVI sensor, suggesting that the calculation of a normalized band ratio (i.e., NDVI) effectively reduces the reflectance measurement inaccuracy that we observed previously. The extracted values were compared to the reflectance values acquired in the laboratory, and both UAS sensors were found to over-estimate reflectance, with lower accuracy in red-edge and NIR bands. 1, imagery was collected using each UAS sensor and reflectance values were extracted from pixels covering the ground reference targets. A sub-set of the target materials were selected as ground reference targets for three field calibration exercises. We found a strong linear relationship between the measurements made by the MicaSense RedEdge and the spectrometer, while the relationship was much weaker for the Airinov MultiSpec 4C, particularly in the longer wavelength bands (red-edge and NIR). In the laboratory, we measured the reflectance of a number of reference target materials using each UAS sensor, and compared the values to those measured using a calibrated spectrometer. We evaluated the performance of two multispectral sensors – the MicaSense RedEdge and the Airinov MultiSpec 4C – in both a laboratory and field setting. The main objective of this study was to develop and test a framework that can be used by Unmanned Aerial Systems (UAS) operators with varying technical backgrounds to estimate the accuracy and reliability of multispectral (visible and Near-Infrared or NIR) sensor measurements.
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