In this article, a dynamic model describing the growth of the green microalgae Chlamydomonas reinhardtii , under light attenuation and sulphur‐deprived conditions leading to hydrogen production in a photobioreactor is presented. The strong interactions between biological and physical phenomena require complex mathematical expressions with an important number of parameters. This article presents a global identification procedure in three steps using data from batch experiments. First, it includes the application of a sensitivity function analysis, which allows one to determine the parameters having the greatest influence on model outputs. Secondly, the most influential parameters were identified by using the classical least‐squares cost function. This stage is applied to the experimental data collected from a lab‐scale batch photobioreactor. Finally, the implementation of an Extended Kalman Filter estimating the biomass concentration, extracellular and intracellular sulphur concentrations is presented. Thereby, the observer uses on‐line measurements provided by a mass spectrometer measuring the outlet gas composition (O2, CO2). Software sensor performances and limits are illustrated in simulation and with experimental data.