In 1962, a series of in situ primary production measurements began in the Adriatic Sea, at a station near the island of Vis. To this day, over 55 years of monthly measurements through the photic zone have been accumulated, including close to 3000 production measurements at different depths. The measurements are conducted over a six-hour period around noon, and the average production rate extrapolated linearly over day length to calculate daily production. Here, a non-linear primary production model is used to correct these estimates for potential overestimation of daily production due to linear extrapolation. The assimilation numbers are recovered from the measured production profiles and subsequently used to model production at depth. Using the recovered parameters, the model explained 87% of variability in measured normalized production at depth. The model is then used to calculate daily production at depth, and it is observed to give on average 20% lower daily production at depth than the estimates based on linear extrapolation. Subsequently, water column production is calculated, and here, the model predicted on average 26% lower water column production. With the recovered parameters and the known magnitude of the overestimation, the time-series of water column production is then re-established with the non-linearly-corrected data. During this 55-year period, distinct regimes were observed, which were classified with a regime shift detection method. It is then demonstrated how the recovered parameters can be used in a remote sensing application. A seasonal cycle of the recovered assimilation number is constructed along with the seasonal cycle of remotely-sensed chlorophyll. The two are then used to model the seasonal cycle of water column production. An upper and a lower bound on the seasonal cycle of water column production based on remotely-sensed chlorophyll data are then presented. Measured water column production was found to be well within the range of remotely-sensed estimates. With this work, the utility of in situ measurements as a means of providing information on the assimilation number is presented and its application as a reference for remote sensing models highlighted.

Fifteen years (1998–2012) of monthly chlorophyll-like pigment concentrations from satellite images (JRC database) were analysed using self-organizing maps (SOMs). The aim was to classify the pigment structures of the Adriatic Sea into a number of patterns present in the data by this novel neural network method. The images are represented by a vector in the multidimensional data-space limited to the Adriatic area. We tried using several predefined patterns, finally selecting an optimal number of 16 spatial structures. Distinct neurons are primarily associated with patterns of different pigment concentration over the whole Adriatic area and with a different extension of the colour front along the west coast. Rare phenomena and variability limited to smaller areas were less represented in the SOM patterns. Although temporal pigment dynamics are synchronous in many areas of the Adriatic Sea, the analysis of their annual course resulted in ten temporal patterns. This representation also demonstrates the ability of the SOM method to connect areas with a particular seasonal variation in chlorophyll.

Similar seasonal courses of pigment concentrations were found in all the regions of the Adriatic, from the Coastal Zone Color Scanner (CZCS) data. Maximum pigment concentrations were observed in the late autumn and in early winter. Individual chlorophyll measurements, from particular cruises, correlated well with the pigments retrieved from the satellite data. However, the monthly means, from longer observation periods, did not correspond well and it was found that the pigment values retrieved from CZCS were considerably higher than in situ measured chlorophyll concentrations for these months in the whole of the Adriatic. This is a result of hydro-geo-meteorological factors, namely mixing of the sediments, especially in the shallow northern Adriatic after strong winds, and the influence of coloured substances brought by the rivers.

Spatial and temporal variability of pigments was studied from the CZCS satellite data and fromin situ chlorophyll and transparency for the period 1979-1985. The three Adriatic sites, Northern, Middle, and Southern Adriatic are differently influenced by meteorological, hydrological and oceanographic parameters. The differences between seasonalin situ chlorophyll and remotely sensed pigment concentrations (from CZCS satellite data) from the Adriatic are large in winter. Through the correlation analysis, pigments were compared to meteo-oceanographic and hydrological parameters from different Adriatic sites. The PCA (principal component analysis) was applied to the pigment data series and significant components were compared. Different correlations are obtained for warm and cold periods of the year pointing to seasonal differences in the underlying mechanism of pigment variability. The first PC is influenced mainly by temperature. In the warm period more parameters seem to influence the pigment field, than in the cold period. The pigments in the Adriatic are in good correlation to a number of hydrologic and meteo-oceanographic factors.