Accurate estimation of leaf nitrogen concentration (LNC) is critical for characterizing ecosystem and plant physiological processes for example in carbon fixation. Remote sensing can capture LNC but interrelated traits and spectral diversity across plant species prevent development of transferable LNC assessment models based on leaf reflectance.
In this work led by Haiyan Cen from Zhejiang University and now accepted at Remote Sensing of Environment, we developed a new transfer learning method by coupling transfer component analysis with the support vector regression (TCA-SVR) to transfer LNC assessment models across different plant species. The performance of TCA-SVR was evaluated with five remote sensing datasets on 60 plant species measured from three spectroradiometers with varied spectral resolutions and illumination and viewing angles, which was also compared with a well-established partial least squares regression (PLSR) model. Compared to the PLSR model, TCA-SVR greatly improved the transferability of the LNC assessment model.
Stay tuned for more details in the online publication.
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