Needle-leaf chlorophyll content (Cab) of a Norway spruce stand was estimated from CHRIS-PROBA images using the canopy reflectance simulated by the PROSPECT model coupled with two canopy reflectance models: 1) discrete anisotropic radiative transfer model (DART); and 2) PARAS. The DART model uses a detailed description of the forest scene, whereas PARAS is based on the photon recollision probability theory and uses a simplified forest structural description. Subsequently, statistically significant empirical functions between the optical indices $\textbf{ANCB}_{\textbf{670} - \textbf{720}}$ and ${\textbf{ANMB}_{\textbf{670} - \textbf{720}}}$ and the needle-leaf Cab content were established and then applied to CHRIS-PROBA data. The Cab estimating regressions using ${\textbf{ANMB}_{\textbf{670} - \textbf{720}}}$ were more robust than using ${\textbf{ANCB}_{\textbf{670} - \textbf{720}}}$ since the latter was more sensitive to LAI, especially in case of PARAS. Comparison between Cab estimates showed strong linear correlations between PARAS and DART retrievals, with a nearly perfect one-to-one fit when using ${\textbf{ANMB}_{\textbf{670} - \textbf{720}}}$ ($\textbf{slope} = \textbf{1.1}$, $\textbf{offset} = \textbf{11}\,\upmu \textbf{g} \cdot {\textbf{cm}^{ - 2}}$). Further comparison with Cab estimated from an AISA Eagle image of the same stand showed better results for PARAS ($\textbf{RMSE} = \textbf{2.7}\,\upmu \textbf{g} \cdot {\textbf{cm}^{ - 2}}$ for ${\textbf{ANCB}_{\textbf{670} - \textbf{720}}}$; $\textbf{RMSE} = \textbf{9.5}\,\upmu \textbf{g} \cdot {\textbf{cm}^{ - 2}}$ for ${\textbf{ANMB}_{\textbf{670} - \textbf{720}}}$) than for DART ($\textbf{RMSE} = \textbf{7.5}\,\upmu \textbf{g} \cdot {\textbf{cm}^{ - 2}}$ for ${\textbf{ANCB}_{\textbf{670} - \textbf{720}}}$; $\textbf{RMSE} = \textbf{23}\,{\upmu \textbf{g}} \cdot {\textbf{cm}^{ - 2}}$ for ${\textbf{ANMB}_{\textbf{670} - \mathbf{720}}}$). Although these results show the potential for simpler models like PARAS in estimating needle-leaf Cab from satellite imaging spectroscopy data, further analyses regarding parameterization of radiative transfer models are recommended.