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  Zarco-Tejada, P. J., Miller, J. R., Noland, T. L., Mohammed, G. H., & Sampson, P. H. (2001). Scaling-up and model inversion methods with narrowband optical indices for chlorophyll content estimation in closed forest canopies with hyperspectral data.  IEEE Transactions on Geoscience and Remote Sensing ,  39 (7), 1491-1507. In this paper, the author uses vegetation index as a merit function. The method is more stable than using all channels. Integrating empirical method, physics based model inversion could be improved. 

Féret, J.B. et al., 2019. Estimating leaf mass per area and equivalent water thickness based on leaf optical properties: Potential and limitations of physical modeling and machine learning. Remote Sensing of Environment, 231. Physical model has more ability to generalize itself then empirical or statistical models. But the calibration and the inversion algorithm can be essential and complicated step.