Accurate estimation of leaf nitrogen concentration (LNC) is critical to characterize ecosystem and plant physiological processes for example in carbon fixation. Remote sensing can capture LNC, while interrelated traits and spectral diversity across plant species prevent development of transferable LNC assessment models based on leaf reflectance. Here, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression, namely TCA-SVR, to transfer LNC assessment models across different plant species. We benchmarked the performance of TCA-SVR against a well-established partial least squares regression (PLSR) model with five remote sensing datasets on 60 plant species measured from three spectroradiometers with varied spectral resolutions and illumination and viewing angles. The result showed that leaf reflectance presented the high spectral diversity in different spectral regions, plant species, and growth stages. The combination of visible (VIS), near infrared (NIR), and shortwave infrared (SWIR) reflectance (e.g. 550–2300 nm) achieved the optimal LNC assessment across all datasets. Results on the testing datasets showed that the transferability of the PLSR models highly depended on the LNC distribution and spectral features, which were associated with the differences in plant species, spectral measurements, and growth conditions between datasets. These differences led to the large variations in LNC and leaf reflectance, which thus produced the overestimations and underestimations of LNC. Compared to the PLSR model, TCA-SVR greatly improved the transferability of the LNC assessment model by reducing the average root mean square error by 36.76%. Further, the implementation of modeling updating can help TCA-SVR learn the features related to the difference in plant species and LNC ranges by transferring samples from the target dataset to the source dataset. Our model updating approach improved the performance of TCA-SVR and only needed 5% of the off-site samples to supplement the source dataset to achieve an effective assessment of LNC. Refining the proposed method with new remote sensing datasets will aid rapid monitoring of plant nitrogen status and may improve carbon‑nitrogen interactions in existing ecosystem models.

叶氮浓度 (LNC) 的准确估计对于表征生态系统和植物生理过程（例如在碳固定中）至关重要。遥感可以捕获 LNC，而植物物种之间的相互关联性状和光谱多样性阻止了基于叶片反射率的可转移 LNC 评估模型的发展。在这里，我们通过将迁移分量分析与支持向量回归相结合，开发了一种新的迁移学习方法，即 TCA-SVR，以在不同植物物种之间迁移 LNC 评估模型。我们根据一个完善的偏最小二乘回归 (PLSR) 模型对 TCA-SVR 的性能进行了基准测试，该模型具有 60 种植物的五个遥感数据集，这些数据集是从具有不同光谱分辨率、照明和视角的三个光谱仪测量的。结果表明，叶片反射率在不同光谱区域、植物种类和生长阶段表现出较高的光谱多样性。可见光 (VIS)、近红外 (NIR) 和短波红外 (SWIR) 反射率（例如 550–2300 nm）的组合实现了所有数据集的最佳 LNC 评估。测试数据集的结果表明，PLSR 模型的可转移性高度依赖于 LNC 分布和光谱特征，这与数据集之间植物物种、光谱测量和生长条件的差异有关。这些差异导致 LNC 和叶片反射率的巨大变化，从而产生了对 LNC 的高估和低估。与 PLSR 模型相比，TCA-SVR 通过将平均均方根误差降低 36.76%，大大提高了 LNC 评估模型的可迁移性。此外，模型更新的实现可以通过将样本从目标数据集转移到源数据集，帮助 TCA-SVR 学习与植物物种和 LNC 范围差异相关的特征。我们的模型更新方法提高了 TCA-SVR 的性能，并且只需要 5% 的异地样本来补充源数据集即可实现对 LNC 的有效评估。使用新的遥感数据集改进所提出的方法将有助于快速监测植物氮状况，并可能改善现有生态系统模型中的碳氮相互作用。模型更新的实现可以帮助 TCA-SVR 通过将样本从目标数据集转移到源数据集来学习与植物物种和 LNC 范围差异相关的特征。我们的模型更新方法提高了 TCA-SVR 的性能，并且只需要 5% 的异地样本来补充源数据集即可实现对 LNC 的有效评估。使用新的遥感数据集改进所提出的方法将有助于快速监测植物氮状况，并可能改善现有生态系统模型中的碳氮相互作用。模型更新的实现可以帮助 TCA-SVR 通过将样本从目标数据集转移到源数据集来学习与植物物种和 LNC 范围差异相关的特征。我们的模型更新方法提高了 TCA-SVR 的性能，并且只需要 5% 的异地样本来补充源数据集即可实现对 LNC 的有效评估。使用新的遥感数据集改进所提出的方法将有助于快速监测植物氮状况，并可能改善现有生态系统模型中的碳氮相互作用。