Estimation of foliar nitrogen in Zea mays L. using vegetation indices and multispectral sensors
DOI:
https://doi.org/10.19136/era.a13n2.4580Keywords:
Precision agriculture, Predictive models, Parrot Sequoia, Sentinel-2, Remote SensingAbstract
Maize (Zea mays L.) is a globally important crop for food security and livestock production, and its yield strongly influenced by nitrogen (N) availability, a key nutrient involved photosynthesis and grain formation. This study aimed to evaluate the relationship between different vegetation indices (VIs) and N content in hybrid forage maize (variety N83N5), to optimize crop monitoring using remote sensing. The research was conducted at Granja Palestina, Francisco I. Madero, Coahuila, Mexico, using multispectral imagery from the Sentinel-2A and 2B satellites and a Parrot Sequoia sensor mounted on an unmanned aerial vehicle (UAV). A total of 21 VIs were calculated and analyzed using regression models and multivariate analysis. The results showed that spatial resolution and VI selection are critical factors in improving the accuracy of N content estimation models. For Sentinel-2, the best performing model (R2 = 0.908) included CCCI, TCARI/OSAVI RE, TCARI, MSAVI, and MTCI. The Sequoia sensor showed strong performance with simpler model based on GNDVI (R2 = 0.814), SAVI (R2 = 0.829), MSAVI (R2 = 0.826), and NDVI (R2 = 0.834). These findings indicate that selecting appropriate sensors and spectral indices enables the development of accurate methodologies for monitoring N content in maize, supporting precision agriculture applications.
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