Abstract
Precision agriculture involves the integration of new technologies including Geographic Information Systems (GIS), Global Navigation Satellites Systems (GNSS) and Remote Sensing (RS) platforms and sensors to allow farmers to maximize the cost-benefit ratio, rather than using the traditional whole-field approach. MAIA S2 is a recent multispectral aerial sensor in strong expansion in the agricultural sector. In this work, MAIA S2 spectral properties were compared with the correspondent Sentinel-2 ones, focusing on possible effects that differences could induce onto agriculture related deductions. The reference dataset was acquired by aerial survey and radiometric and geometric pre-processing achieved to generate the correspondent at-the-ground reflectance multispectral orthomosaic by ordinary workflow as suggested by sensor suppliers. A comparison was achieved at single band level to test spectral consistency of the two data. It showed a low correlation in the red-edge and infrared bands (r < 0.5); oppositely, a higher correlation was found for the visible bands (r > 0.8). To test the effects of found discrepancies between the two data, the correspondent prescription maps were generated using the same clustering criterion. They were then compared to test consistency of deductions.
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Sarvia, F., De Petris, S., Orusa, T., Borgogno-Mondino, E. (2021). MAIA S2 Versus Sentinel 2: Spectral Issues and Their Effects in the Precision Farming Context. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2021. ICCSA 2021. Lecture Notes in Computer Science(), vol 12955. Springer, Cham. https://doi.org/10.1007/978-3-030-87007-2_5
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