Performance of different Sentinel-2A vegetation indices to estimate soybean yield in precision farming
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Abstract
Precision agriculture (PA) aims to identify crop and soil variability to improve management and optimize the use of inputs. Yield maps become a relevant tool for PA planning. Vegetation indices (VI) from remote sensing allow monitoring the spatio-temporal variation of crops in the growing season. The objective of this work was to evaluate the performance of different Sentinel-2A VIs to estimate soybean (Glycine max (L.) Merril) yield within the framework of PA. The study was carried out using a soybean yield map during the 2017/2018 season from a plot located to the south of Córdoba city, Argentina. Every two bands were taken from a Sentinel-2A image from 4/Feb/2018 and VI were calculated using differences, ratios and normalized differences. The absolute value of the Pearson linear correlation coefficient (|r|) between the different IVs and soybean yield was computed. The highest value of |r| (0.726) corresponded to the difference between bands 8 (NIR) and 12 (SWIR), allowing to reproduce with sufficient precision the spatial variability of yields in the plot.
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