.

Combal, B., et al. "Retrieval of canopy biophysical variables from bidirectional reflectance: Using prior information to solve the ill-posed inverse problem."  Remote sensing of environment  84.1 (2003): 1-15.

Banskota, A., Wynne, R. H., Thomas, V. A., Serbin, S. P., Kayastha, N., Gastellu-Etchegorry, J. P., & Townsend, P. A. (2013). Investigating the utility of wavelet transforms for inverting a 3-D radiative transfer model using hyperspectral data to retrieve forest LAI.  Remote Sensing ,  5 (6), 2639-2659. Continuum wavelength transformation is similar to CNN in deep learning. Pooling layer of CNN adjusts the resolution of 2D image. On the image, we see objects ranging from infinitesimal one to broad one. CNN imitates how human being sees. Similarly, CWT well captures signals from microscopic signal, Chlorophyll, to wide signal, such as LMA and water.

Wang, Z., et al. (2018). "Mapping forest canopy nitrogen content by inversion of coupled leaf-canopy radiative transfer models from airborne hyperspectral imagery." Agricultural and Forest Meteorology 253-254: 247-260. Nitrogen map was validated by ground data and general pattern. The known pattern can validated remote sensing products spatially.

Richter, K., Hank, T. B., Mauser, W., & Atzberger, C. (2012). Derivation of biophysical variables from Earth observation data: validation and statistical measures.  Journal of Applied Remote Sensing ,  6 (1), 063557.

Skidmore, A. K., et al. (2010). "Forage quality of savannas — Simultaneously mapping foliar protein and polyphenols for trees and grass using hyperspectral imagery." Remote Sensing of Environment 114(1): 64-72. Canopy N was calculated just multiplying leaf N to LAI. In this paper, more precise estimate of canopy N was attributed to higher estimate of LAI. On leaf scale, the leaf structure itself did not account for leaf spectroscopy better than foliar traits. However, on UAV level, canopy structure affects to the spectroscopy directly.

Yang, X., et al. (2016). "Seasonal variability of multiple leaf traits captured by leaf spectroscopy at two temperate deciduous forests." Remote Sensing of Environment 179: 1-12. Collection covers all the seasons. Summer is expected to have less variations on the plant's phonology. However, when the model was trained by a season data and then tested to the other seasons, the summer model showed the least RMSE. It means summer was the best season to collect leaves with divers status. It's harsh light must affect various level of stress to sunltis and shaded leaves.

Wang, Z., Skidmore, A. K., Wang, T., Darvishzadeh, R., & Hearne, J. (2015). Applicability of the PROSPECT model for estimating protein and cellulose+ lignin in fresh leaves.  Remote sensing of environment ,  168 , 205- Physical models also include empirical parts. PROSPECT does so, too. In the paper, leaf potein and lignin + cellulose were decoupled from LMA.

Gitelson, A.A., Zur, Y., Chivkunova, O.B. and Merzlyak, M.N., 2002. Assessing carotenoid content in plant leaves with reflectance spectroscopy. Photochem Photobiol, 75(3): 272-81. Carotenoid content is lower than leaf chlorophyll content. But the seasonal decrease is much lower than that of chlorophyll. Namely, in green leaves, the strong chlorophyll effect in visible range obscures carotenoid signal. On the other hand, in yellowish leaves, the effect is little. Before estimating carotenoid content, it can be useful to classify leaves by carotenoid proportion.

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.

Bousquet, L., Lachérade, S., Jacquemoud, S. and Moya, I., 2005. Leaf BRDF measurements and model for specular and diffuse components differentiation. Remote Sensing of Environment, 98(2-3): 201-211. Specular reflectance originates from leaf surface. On the other hand, diffuse reflectance originates from leaf intrastructure. To focus on the target traits, the two reflectance needs to be separated.

Kim, H. S., et al. (2008). "Actual and potential transpiration and carbon assimilation in an irrigated poplar plantation." Tree Physiol 28(4): 559-577. As for environment, its mechanisms and their causing factors have internal relationship. Data with diverse condition can appear with the boundary which limited by causing factors.

Kim, H. S., Palmroth, S., Thérézien, M., Stenberg, P., & Oren, R. (2011). Analysis of the sensitivity of absorbed light and incident light profile to various canopy architecture and stand conditions.  Tree Physiology ,  31 (1), 30-47. In this paper, they analyzed canopy light absorption, considering leaf angle distribution, leaf clumping and stand density. To compare canopy properties, they set models and compare how well each model estimates measured canopy light absorption. Canopy properties are controlled in 7 models (v1 – v7). The simplest model (v1), only considering Beer-Lambertian gap fraction, was developed to most complex model (v7) by gradually adding another properties. They are compared in respect of various LAI and sky condition.

Farquhar, G. D., von Caemmerer, S. V., & Berry, J. A. (1980). A biochemical model of photosynthetic CO 2 assimilation in leaves of C 3 species.  Planta ,  149 (1), 78-90. Carbon assimilation is mainly controlled by electron, light which is reducing electron from water molecule, O2 and CO2. They consist of the model.

Jacquemoud, S., et al. "Estimating leaf biochemistry using the PROSPECT leaf optical properties model."  Remote sensing of environment  56.3 (1996): 194-202. Leaf reflectance is affected by leaf biochemicals as well as by pigments or water. Inspecting NIR reflectance, N which is highly correlated to protein and C which are highly correlated to  cellulose and lignin  can be detected.

Jacquemoud, Stéphane, and F. Baret. PROSPECT A model of leaf optical properties spectra. Remote sensing of environment 34.2 (1990) 75-91. Light travels to a leaf and scatters within the leaf. Interacting with the leaf, the beam could represent the intra-structure. NIR, where absorption would minimized, is the best range.

Asner, Gregory P., and Roberta E. Martin. Spectral and chemical analysis of tropical forests Scaling from leaf to canopy levels. Remote Sensing of Environment 112.10 (2008) 3958-3970. When up-scaling leaf level to canopy level, canopy structure may obstruct leaf traits to fully appear as the form of spectra. Except LAI, however, most canopy structural features had a negligible effect on the spectra created by canopy modeling.