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Breeders and researchers widely use remote sensing by UAS as a high-throughput phenotyping tool. However, the experiment condition, such as time of day, season, crop type, plant structure, and illumination setting, can influence the measured data, impact the analytical outcomes and lead to uncertain conclusions. In this study, we investigated the effects of Sun-camera geometry on plants' reflectance measured by multispectral cameras. For this purpose, over two years, we collected an extensive dataset of UAS-based imagery from several citrus, almond, and grape orchards in various environmental conditions. We measured and documented the significant effect of sun-camera geometry on the reflectance data. We realized that an off-nadir view angle of 25 degrees within the field of view in a single image could lead to a 50% variation in measured reflectance compared to the nadir view. The results indicate that the object's reflectivity could be a function of its position within an image frame independent of its inherent spectral characteristics. The variations emerge from direct solar radiations (as opposed to diffused radiations) and climax in clear sky conditions around the solar noon. We proposed a model that estimates and correct the reflectance variations due to sun-camera geometry. The results showed that the proposed model could estimate reflectance from other view angles by r2 of up to 0.88 depending on the spectral band, crop type, and a few other variables. Comparing these results with the data simulated by the 4Sail model showed a reasonable consistency in the outcome. This study's findings help determine the optimum flight time as a function of the camera's FOV, date, and the site's latitude, which will facilitate the correction of the direct solar radiation effect and assist in avoiding unreliable data collection.
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