Revolutionizing nitrogen monitoring in ginkgo with spectral modeling

This technique corrects for specular reflection and highlights nitrogen-associated protein signals, yielding high-precision LNC estimates.

Nitrogen plays a pivotal role in plant metabolism, influencing photosynthesis, growth, and the synthesis of secondary metabolites like flavonoids in Ginkgo biloba. Overuse or underuse of nitrogen fertilizer can damage plant health, reduce yields, and harm the environment. Traditional nitrogen assessment methods, including visual diagnostics and lab analyses, are often imprecise or delayed. Remote sensing techniques offer a promising alternative by detecting physiological changes before visual symptoms emerge. However, accurate LNC retrieval using reflectance data is hindered by complex leaf structures and overlapping spectral signals from other biochemicals like water. To address these challenges, a more robust and generalizable retrieval method is urgently needed.

A study (DOI: 10.34133/plantphenomics.0282) published in Plant Phenomics on 13 December 2024 by Lin Cao’s team, Nanjing Forestry University, offers a scalable, efficient solution for monitoring tree nutrient status and supports more sustainable nitrogen management in forestry and agriculture.

To enhance the retrieval accuracy of leaf nitrogen content (LNC) in Ginkgo biloba, researchers employed the PROSPECT-PRO radiative transfer model with bidirectional reflectance factor (BRF) spectra, introducing three modified ratio indices—mPrior_800, mPrior_1131, and mPrior_1365—to improve prior estimation of the leaf structure parameter (Nstruct). These indices were tested across multiple inversion methods, including PROCWT and PROSDM variants. Modified indices showed strong resistance to specular reflection interference, unlike standard indices (e.g., Prior_800, Prior_1131), and exhibited significantly higher correlations with Nstruct (R² = 0.66–0.93). Leaf-level analysis revealed that saplings had higher LNCarea, leaf mass per area (LMA), and protein content (Cp) than mature trees. LNCarea and LNCmass peaked under moderate nitrogen application (675 kg/hm²) and plateaued at higher levels. Spectral reflectance patterns shifted with rising LNCarea, marked by declining visible and SWIR reflectance and rising NIR reflectance. Among inversion strategies, combining mPrior_1365 with PROSDM_FMD yielded the highest accuracy for LMA and carbon-based content (CBC), while PROCWT_S3 paired with mPrior_1131 or mPrior_1365 excelled in LNC estimation. These combinations reduced normalized root mean square error (NRMSE) for LNCarea and LNCmass to 12.94–14.49% and 10.11–10.75%, respectively. Five optimal spectral domains (1440–1539, 1580–1639, 1900–1999, 2020–2099, and 2120–2179 nm) were identified, enhancing estimation precision when selectively applied. Performance declined with the inclusion of water-dominated bands, confirming the importance of waveband optimization. Overall, the integration of prior Nstruct estimation and spectral selection proved essential for accurate, nondestructive nitrogen assessment in Ginkgo leaves.

This new approach offers a timely, nondestructive, and scalable method for assessing nitrogen status in economically and pharmacologically valuable species like Ginkgo biloba. By enabling precise monitoring of LNC during key growth stages, it supports optimized fertilizer application, reduces environmental risk, and enhances the production of nitrogen-dependent medicinal compounds. The integration of modified ratio indices into mechanistic models represents a leap forward in remote sensing of plant health. Moreover, the method’s compatibility with field-deployable BRF devices broadens its applicability in precision agriculture and forest management, offering a valuable tool for researchers, growers, and policymakers.

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References

DOI

10.34133/plantphenomics.0282

Original Source URL

https://doi.org/10.34133/plantphenomics.0282

Funding information

This work was funded by the National Natural Science Foundation of China (32101521), the Jiangsu Agriculture Science and Technology Innovation Fund (CX(23)1027), and the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (21KJB220003).

About Plant Phenomics

Science Partner Journal Plant Phenomics is an online-only Open Access journal published in affiliation with the State Key Laboratory of Crop Genetics & Germplasm Enhancement, Nanjing Agricultural University (NAU) and distributed by the American Association for the Advancement of Science (AAAS). Like all partners participating in the Science Partner Journal program, Plant Phenomics is editorially independent from the Science family of journals. Editorial decisions and scientific activities pursued by the journal's Editorial Board are made independently, based on scientific merit and adhering to the highest standards for accurate and ethical promotion of science. These decisions and activities are in no way influenced by the financial support of NAU, NAU administration, or any other institutions and sponsors. The Editorial Board is solely responsible for all content published in the journal. To learn more about the Science Partner Journal program, visit the SPJ program homepage.