We have provided some available MATLAB codes for the estimation of LNC. These methods include PLSR,SVR, and TCA. We also provided a measured dataset collected from oilseed rape leaves, and LNC and leaf reflectance at 350-2500 were measured. To run these codes, we recommend the use of MATLAB 2019b. Note that the LIBSVM available in the MATLAB Library should be required to install. https://www.csie.ntu.edu.tw/~cjlin/libsvm/ Reference: Chang, C.-C., Lin, C.-J., 2011. LIBSVM: A library for support vector machines. ACM transactions on intelligent systems and technology (TIST), 2, 1¨C27. To implement the TCA, the model parameters should be optimized firstly, and the details about the TCA can be found in previous stduies. Reference: Pan, S.J., Kwok, J.T., Yang, Q., 2008. Transfer Learning via Dimensionality Reduction. In, Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence, AAAI 2008, Chicago, Illinois, USA, July 13-17, 2008. Pan, S.J., et al. Domain Adaptation via Transfer Component Analysis. TNN 2011. PCA can be used to analysis leaf spectral diversity, which can illustate the difference between plant species. Reference: Wold, S., Esbensen, K., Geladi, P., 1987. Principal component analysis. Chemometrics and intelligent laboratory systems, 2, 37¨C52.