Two types of implementation of the 14C method for measuring carbon assimilation in photosynthesis are differentiated with respect to light conditions under which the experiments are performed: in vitro and in situ. In vitro incubations are performed in the laboratory under controlled light conditions, whereas in situ incubations are performed at sea under natural light conditions. The dependence of primary production on light is mathematically described with light saturation functions, whose parameters determine the rate of carbon assimilation in photosynthesis. The values of photosynthesis parameters are determined based on the results of in vitro measurements and are afterwards used in models of primary production. In this work an inverse model is developed which uses in situ measurements to estimate the values of the photosynthesis parameters. It employs an optimization algorithm which treats the photosynthesis parameters of the production model as optimization variables and estimates optimal parameter values for which the model deviates the least from the in situ measurements. The inverse model uses the primary production model formulated with two different formalisms, an already established analytical formalism and here developed matrix formalism. Using the analytical formalism, under the assumption of idealized light conditions, an analytical solution for the production prole and the analytical solution for water column production with depth dependent biomass were derived. The inverse model was tested on 194 in situ primary production proles from the Hawaii Ocean Time-series and the parameters of dierent light saturation functions were estimated. When using optimal parameter values the models explained over 95% of variability in measured production at depth and over 95% of variability in the measured water column production. As a result of this work, the inverse model became a new tool for estimating the values of the photosynthesis parameters from in situ primary production profiles.