Combining transfer learning and hyperspectral reflectance analysis to assess leaf nitrogen concentration across different plant species datasets

Abstract

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.