Abstract

ABSTRACT Foliar nitrogen (N) plays a central role in photosynthetic machinery of plants, regulating their growth rates. However, field-based methods for monitoring plant N concentration are costly and limited in their ability to cover large spatial extents. In this study, we had two objectives: (1) assess the capability of unoccupied aerial system (UAS) and non-imaging spectroscopic data in estimating sorghum and corn N concentration and (2) determine the impact of spatial and spectral resolution of reflectance data on estimating sorghum and corn N concentration. We used a UAS and an ASD spectroradiometer to collect canopy- and leaf-level spectral data from sorghum and corn at experimental plots located in Stillwater, Oklahoma, U.S. We also collected foliage samples in the field and measured foliar N concentration in the lab for model validation. To assess the impact of spectral scale on estimating N concentration, we resampled our leaf-level ASD data to generate datasets with coarser spectral resolutions. To determine the impact of spatial scale on estimating N concentration, we resampled our UAS data to simulate five datasets with varying spatial resolutions ranging from 5 cm to 1 m. Finally, we used a suite of vegetation indices (VIs) and machine learning algorithms (MLAs) to estimate N concentration. Results from leaf-level ASD spectral data showed that the resampled data matching the spectral resolution of our UAS-based data at five spectral bands ranging from 360 to 900 nm provided sufficient spectral information to estimate plot-level sorghum and corn N concentration. Regarding spatial resolution, canopy-level UAS data resampled at multiple pixel sizes, ranging from 1 cm to 1 m were consistently capable of estimating N concentration. Overall, our findings indicate the possibility of developing monitoring instruments with optimal spectral and spatial resolution for estimating N concentration in crops.

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