The decline of ArcGc sea ice as well as its resulting feedbacks is asserted to have great impacts on the Arctic phytoplankton, and caused large regional variations in primary production range in the Arctic Ocean and its marginal seas. Understanding and quantifying such impacts are critical to appreciate the Arctic as a system and allow diagnostic modeling of its current status and dynamics. To assess the above impacts of reduction in sea ice and then the changes in physical properties on Arctic phytoplankton, numerical models have emerged as valuable tools. In order to generate reliable results, a high quality Arctic Chl-a dataset is essential to improve parameterizations in the coupled ice-ecosystem-ocean circulation models. With the emergency of autonomous platforms (e.g. floats (Argo), autonomous vehicles), high spatial and temporal resolution measurements of biooptical parameters are achievable. However, new challenges arise from the automated way of observing the bio-optical properties of the ocean. Indeed, conversely to what happens when the same kinds of equipment are operated from a ship, these bio-optical data are collected in environmental condiGons that are out of the operator’s control. Therefore, new specific data processing and management procedures have to be developed for in situ bio-optical sensors which generate high spatial and temporal resolution measurements of bio-optical data. In this study, analytical bio-optical techniques are applied to develop quality controlled high quality pan-Arctic long-term information on total biomass of phytoplankton. The quantitative distribution of phytoplankton will be determined on long time scales covering the Fram Strait in the Arctic Ocean by the integration of measurements from various platforms that enable to retrieve the total biomass of phytoplankton.