The computation of water-column primary production using remotely-sensed fields of surface chlorophyll is affected by errors, related to phytoplankton biomass retrieval. The continual development and refinement of chlorophyll retrieval algorithms will lead to improved estimation of primary production. Another pathway to error reduction is through improved estimation of the photosynthetic parameters. As our mechanistic understanding of the key environmental factors governing the variability in these parameters has grown and the number of in situ observations has increased, we are now in a position to conduct a quantitative comparison of existing methods used to recover these parameters from remotely-sensed data (ocean colour, SST) and, to improve these methods by adopting an operational approach to parameter assignment. The Marine Primary Production: Model Parameters from Space (MAPPS) project aims to improve estimates of Primary Production through advancement of methods for estimating the photo-physiology of phytoplankton using remote-sensing data. The project includes an evaluation of existing methods for recovery of the photosynthesis response of phytoplankton (parameters of the photosynthesis- light curve) from remotely-sensed data (ocean colour, SST). Following this evaluation, the project partners build upon existing models to create the remote-sensing based algorithm for estimating photophysiology. The best performing model will then be combined with existing and freely available satellite data (primarily ocean colour and sea-surface temperature) to compute monthly primary production fields on a test basis, to be made available to the user community in conjunction with the OC-CCI project. Each of the models tested in the initial phase of the project has a unique set of benefits and limitations. The province based model, for example, is unlikely to produce wildly spurious values but fails to capture small scale environmental forcing which may add structure to the produced fields of photophysiology. The PFT method has limited range of response to input data, leading to a failure to capture the natural range of the photosynthesis response in phytoplanktonvariability at various geographic and temporal scales. Thus far the project has highlighted the need to develop and validate methods of parameter assignment using in situ data covering a wide range of oceanographic provinces. When models developed using data from of limited geographic coverage are applied at the global scale the performance of the model is often much poorer than in the initial validation. The project has now moved into a phase of algorithm development where we are trialling modified versions of the algorithms and looking at merged methods of estimating photophysiology.