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• Given to the trend of seasonal variation and monitoring accuracy, area-based LNC was more related to SIF signal than mass-based LNC on two observation scales. • SIF R and SIF N outperformed than other spectral indices for retrieving area-based LNC and PNUE from leaf to canopy scales. • The contribution of SIF to estimate area-based LNC was not only the plant biochemical and structural traits, but also other potential internal characters, like nitrogen proportion in photosynthetic and non-photosynthetic apparatus and nitrogen allocation in photosynthetic apparatus. Leaf nitrogen content (LNC), an indicator for the amount of photosynthetic proteins, plays an important role to understand plant function and status. In previous studies, vegetation indices (VIs) have been demonstrated to monitor LNC non-destructively, but which is influenced by backgrounds, and lacks specificity for nitrogen stress. In this study, sun-induced chlorophyll fluorescence (SIF), a novel technique related to plant physiology state, was proposed to estimate area-based and mass-based LNC at both leaf and canopy scales. In addition, SIF indices were evaluated to retrieve photosynthesis nitrogen use efficiency (PNUE), an important trait of leaf economics and physiology, based on the relationships between SIF, photosynthesis, and LNC. This study was conducted on two field experiments of winter wheat with different nitrogen regimes in Rugao, Jiangsu Province, China during 2016-2017 and 2017-2018 growing seasons. We took measurements of SIF, reflectance, biochemical and growth structural parameters at the leaf and canopy scales. The SIF signal was collected using ASD (Analytical Spectral Devices, Boulder, CO, USA) and QEpro (Ocean Optics, Dunedin, FL, USA) spectrometers at the two observational scales, with a full width at half maximum (FWHM) of 1.4 nm and 0.13 nm, respectively. SIF indices were calculated based on the SIF signal extracted at two oxygen absorption bands. Our results demonstrated that area-based LNC was better related to SIF indices and VIs than mass-based LNC. SIF ratio index (SIF R) and normalized SIF index (SIF N), defined as SIF 761 /SIF 687 and (SIF 761 -SIF 687)/(SIF 761 +SIF 687) separately, performed better in monitoring area-based LNC at the two observation scales than CI red edge , which performed best in VIs group. Compared with CI red edge , the best estimation accuracy of SIF indices for area-based LNC increased by 0.08 and 0.02 at the leaf and canopy scales, separately. And when using SIF R and SIF N to monitor area-based LNC, there is no saturation phenomenon, which occurs using traditional VIs. From the whole range of data, area-based LNC was closely related to several plant traits (leaf: area-based leaf chlorophyll content (LCC) (LCCarea), leaf mass per area (LMA); canopy: area-based canopy LCC (CCCarea), leaf area index (LAI), leaf dry weight (LDW) per unit soil area, and LMA), which was consistent with previous studies. However, in specific group with fixed area-based LCC value, although area-based LNC almost wasn't significantly correlated with these traits, SIF R and SIF N were instead always highly correlated with area-based LNC in each small datasets on two observation scales (leaf scale: R2>0.50, R2>0.46; canopy scale: R2>0.41, R2>0.42). Thus, the contribution of SIF R and SIF N to estimate area-based LNC wasn't only the plant traits listed, but also other internal characters, like nitrogen allocation and proportion. Moreover, SIF R and SIF N were proved to be potential detectors to retrieve PNUE. These findings would provide us a new perspective for understanding plant nitrogen status from remote sensing observations, detecting plant function and managing precise agriculture. [ABSTRACT FROM AUTHOR]

Accurate and non-destructive monitoring of wheat nitrogen nutrition is of great significance for field fertilizer management to ensure crop yield and quality, reduce environmental pollution, and improve economic benefits. Compared with spectral vegetation indices (which are sensitive to greenness and structural parameters), or active fluorescence (which is limited to small-scale studies), solar-induced chlorophyll fluorescence (SIF) provides a direct measure of crop response to environmental stress and photosynthetic characteristics. However, there has been few studies comparing agronomic parameters, photosynthetic parameters, vegetation indices and SIF as an indicator of nitrogen status. In this paper, we therefore explore these measures as tools for monitoring nitrogen nutrition. During the 2016–2017 growing season, we conducted a field experiment in Rugao, Jiangsu Province, China, using winter wheat (Triticum aestivum L.) and different nitrogen treatments. The sensitivity of SIF indices, vegetation indices, photosynthetic parameters and agronomic parameters to crop nitrogen status were compared. Our results demonstrated that, compared with vegetation indices and agronomic parameters, the ratio of SIF emission peaks (FY 687 /FY 761) responded to nitrogen status most rapidly at both the leaf and canopy scales, as soon as the fourth day after treatment (DAT4). A wheat nitrogen nutrition index (NNI), based on FY 687 /FY 761 , was used to construct a leaf dry matter (LDM-based NNI) diagnostic model, which will be beneficial for monitoring and diagnosing the nitrogen nutrition status of wheat leaves. Our results also illuminate the physiological mechanism that enables SIF to be used as a tool to monitor nitrogen nutrient status, primarily through changes in the proportion of light energy distribution. These findings provide theoretical and technical support for monitoring and diagnosing wheat nitrogen nutrition status based on SIF technology. • SIF indices can better trace the change of crop nitrogen status than VIs. • Non-destructive diagnosis of nitrogen was achieved using the index FY 687 / FY 761. • The LDM-based NNI diagnostic model has high accuracy. • The light energy distribution explains the mechanism of SIF to trace N status. [ABSTRACT FROM AUTHOR]