  Citation: Di Gennaro, S.F.; Toscano, P.; Gatti, M.; Poni, S.; Berton, A.; Matese, A. Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture. Remote Sens. 2022, 14, 449. https://doi.org/10.3390/rs14030449 Academic Editor: Javier J. Cancela Received: 9 December 2021 Accepted: 15 January 2022 Published: 18 January 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). remote sensing Article Spectral Comparison of UAV-Based Hyper and Multispectral Cameras for Precision Viticulture Salvatore Filippo Di Gennaro 1 , Piero Toscano 1, * , Matteo Gatti 2 , Stefano Poni 2 , Andrea Berton 3 and Alessandro Matese 1 1 Institute of BioEconomy National Research Council (CNR IBE), Via Caproni 8, 50145 Florence, Italy; salvatorefilippo.digennaro@cnr.it (S.F.D.G.); alessandro.matese@cnr.it (A.M.) 2 Department of Sustainable Crop Production, Università Cattolica del Sacro Cuore, Via Emilia Parmense 84, 29122 Piacenza, Italy; matteo.gatti@unicatt.it (M.G.); stefano.poni@unicatt.it (S.P.) 3 Institute of Geosciences and Earth Resources, National Research Council (CNR-IGG), Via Moruzzi 1, 56124 Pisa, Italy; andrea.berton@cnr.it * Correspondence: piero.toscano@ibe.cnr.it Abstract: Analysis of the spectral response of vegetation using optical sensors for non-destructive remote monitoring represents a key element for crop monitoring. Considering the wide presence on the market of unmanned aerial vehicle (UAVs) based commercial solutions, the need emerges for clear information on the performance of these products to guide the end-user in their choice and utilization for precision agriculture applications. This work aims to compare two UAV based commercial products, represented by DJI P4M and SENOP HSC-2 for the acquisition of multispectral and hyperspectral images, respectively, in vineyards. The accuracy of both cameras was evaluated on 6 different targets commonly found in vineyards, represented by bare soil, bare-stony soil, stony soil, soil with dry grass, partially grass covered soil and canopy. Given the importance of the radiometric calibration, four methods for multispectral images correction were evaluated, taking in account the irradiance sensor equipped on the camera (M1–M2) and the use of an empirical line model (ELM) based on reference reflectance panels (M3–M4). In addition, different DJI P4M exposure setups were evaluated. The performance of the cameras was evaluated by means of the calculation of three widely used vegetation indices (VIs), as percentage error (PE) with respect to ground truth spectroradiometer measurements. The results highlighted the importance of reference panels for the radiometric calibration of multispectral images (M1–M2 average PE = 21.8–100.0%; M3–M4 average PE = 11.9–29.5%). Generally, the hyperspectral camera provided the best accuracy with a PE ranging between 1.0% and 13.6%. Both cameras showed higher performance on the pure canopy pixel target, compared to mixed targets. However, this issue can be easily solved by applying widespread segmentation techniques for the row extraction. This work provides insights to assist end-users in the UAV spectral monitoring to obtain reliable information for the analysis of spatio-temporal variability within vineyards. Keywords: vegetation indices; precision agriculture; remote sensing; spectral signature; imaging sensor; radiometric calibration 1. Introduction The spectral canopy response to solar radiation analysed through calculation of a wide range of vegetation indices (VIs) is the basis of remote sensing applications in agri- culture. Both structural aspects, biochemical composition, physiological processes and foliar symptoms influence the ways in which vegetation reflects light in different regions of the electromagnetic spectrum [1–3]. Spectral analysis therefore provides important information on the vegetative state and needs of crops, however optimal acquisition of the spectral data must consider the peculiarities of each crop, since there are structure and characteristics that influence the spectral response. Among different kinds of crops, Remote Sens. 2022, 14, 449. https://doi.org/10.3390/rs14030449 https://www.mdpi.com/journal/remotesensing