Monitoring leaf nitrogen in almond orchards using airborne hyperspectral and Sentinel-2 imagery
2024
Wang, Yue | Suárez, Lola | Poblete, Tomás | Hornero, Alberto | González-Dugo, Victoria | Ryu, Dongryeol | Zarco-Tejada, Pablo J. | Zarco-Tejada, Pablo J. [0000-0003-1433-6165]
An accurate assessment of leaf nitrogen (N) status is essential for developing sustainable agricultural management strategies and implementing precision agriculture practices. Through imaging spectrometers installed onboard drones, aircraft, or satellite platforms, remote sensing techniques can provide accurate, timely, and spatially explicit information on leaf nutrient status. However, even though leaf chlorophyll-sensitive and structural vegetation indices have been traditionally used as valuable plant nutrient indicators, their sensitivity to leaf N may be limited when assessing dense and well-fertilized canopies. New methods based on hyperspectral remote sensing can provide physiological indicators for explaining leaf N variability based on physiological traits. The present study demonstrates that chlorophyll a+b content derived from radiative transfer models presents superior performance for mapping leaf N variability in comparison with standard vegetation indices used in precision agriculture of almond orchards. Furthermore, this study illustrates the feasibility of using Sentinel-2 imagery to explain leaf N content, despite the coarser resolution compared to airborne imagery. Based on model-retrieved plant traits (e.g., Cab, Cw, Cdm) obtained from Sentinel-2, this study evaluated the performance in estimating leaf nitrogen concentration. Results showed a reasonable level of reliability, yielding r<sup>2</sup>=0.79 in 2020 and r<sup>2</sup>=0.72 in 2021.
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