Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets

Leaf mass per area (LMA) and leaf nitrogen concentration (LNC) are both essential parameters in plant ecology, which can reflect the growth status of plants. The features of LMA and LNC can be captured using spectral reflectance in a remote sensing approach. While the relationships between spectra and leaf trait variance across different species with estimation performance are unclear, the development of assessment and transferable models to predicate LMA and LNC are prevented. Hence, we analyzed the variance of raw spectra and spectral data difference with four pretreated approaches (SG—Savitzky–Golay filter, SNV—Standard Normalized Variate, MSC—Multiplicative Scatter Correction analysis, and normalize), LMA, and LNC over six remote sensing datasets by a transfer component analysis (TCA) approach. Spectra combined with the Successive Projections Algorithm (SPA) were also presented to extract wavelengths with higher important coefficients to minimize the redundancy of datasets. The variance of normalized spectra between different datasets showed a minor degree of variance, and LNC spectra variance was decreased by the SPA. The results also showed that a smaller LMA and LNC variance is presented over different datasets when the trait values with higher distribution probabilities are close to each other. The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. The relationships between spectra and leaf trait variance and estimation performance in RFR transfer models over different datasets were evaluated. LMA distance has a significant influence on estimation performance in the transfer model, and the variance of spectra with all pretreated approaches showed a very significant effect on LNC accession performance. Furthermore, we proposed a weight coefficient of spectral data updating combined with the TCA and RFR approach (WDT-RFR) transfer model to improve transferability between datasets and promote estimation performance in the transfer model. Compared to the RFR transfer model using spectra without updating, the root mean square error ( RMSE ) of the WDT-RFR transfer model with 5% samples transferred to estimate LMA and LNC increased by 7.9% and 4.8% on average, respectively. The estimation results showed that our transfer model showed a superior estimation performance.