Early and precise spatio-temporal monitoring of tree vitality is key for steering management decisions in pome fruit orchards. Spaceborne remote sensing instruments face a tradeoff between spatial and spectral resolution, while manned aircraft sensor-platform systems are very expensive. In order to address the shortcomings of these platforms, this study investigates the potential of Remotely Piloted Aircraft Systems (RPAS) to facilitate rapid, low cost, and flexible chlorophyll monitoring. Due to the complexity of orchard scenery a robust chlorophyll retrieval model on RPAS level has not yet been developed. In this study, specific focus therefore lies on evaluating the sensitivity of retrieval models to confounding factors. For this study, multispectral and hyperspectral imagery was collected over pome fruit orchards. Sensitivities of both univariate and multivariate retrieval models were demonstrated under different species, phenology, shade, and illumination scenes. Results illustrate that multivariate models have a significantly higher accuracy than univariate models as the former provide accuracies for the canopy chlorophyll content retrieval of R2
= 0.80 and Relative Root Mean Square Error (RRMSE) = 12% for the hyperspectral sensor. Random forest regression on multispectral imagery (R2
> 0.9 for May, June, July, and August, and R2
= 0.5 for October) and hyperspectral imagery (0.6 < R2
< 0.9) led to satisfactory high and consistent accuracies for all months.
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