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Lin You

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Proceedings Article | 6 December 2022 Paper

Estimation of leaf nitrogen content in winter wheat based on solar-induced chlorophyll fluorescence

Lin You, Jinghao Yang, Heng Dong

Proceedings Volume 12458, 1245802 (2022) https://doi.org/10.1117/12.2659967

KEYWORDS: Nitrogen, Vegetation, Data modeling, Agriculture, Absorption, Reflectivity, Photosynthesis

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Nitrogen is an important nutrient element that affects crop yields. The establishment of a highly applicable and stable leaf nitrogen content hyperspectral inversion model has significant significance for the development of precision agriculture. Based on the canopy spectrum and leaf nitrogen content data of the whole growth period of different nitrogen application levels, the changes of leaf nitrogen content and relative fluorescence intensity with nitrogen concentration gradient were studied, and the inversion was constructed using relative fluorescence intensity. The estimation model of leaf nitrogen content and comparison with three traditional vegetation index models (NDVI, NDRE, NDDA).The results showed that the relative fluorescence intensity and leaf nitrogen content both increased with the increase of nitrogen concentration. When the nitrogen concentration reached N3, the changes of the two were no longer obvious. Compared with the four vegetation indices, the fitting effect of relative fluorescence intensity and leaf nitrogen content is the best, R² is 0.60. The leaf nitrogen content inversion model established with the relative fluorescence intensity as a variable has the highest simulation accuracy, with R² of 0.74 and root mean square error (RMSE) of 0.24%. Solar-induced chlorophyll fluorescence provides a new idea for monitoring crop nitrogen nutrition status.

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