The webinar investigates the suitability of multi-spectral satellite imagery as a source of phytoplankton-related data for the development of credible and accountable data- driven models. Data-driven models are routinely deployed for the modeling of phytoplankton dynamics. Nonetheless, such data-oriented efforts are often plagued by two issues, i.e., insufficient data and the lack of interpretability. On one hand, insufficient data result in overfitting, which produces poorly generalizable models that are unreliable under extrapolating conditions. On the other hand, the lack of interpretability hinders the contribution of such models in decision making, since acting upon model predictions relies heavily on understanding why models behave the way they do. As a result, two important questions arise regarding phytoplankton dynamics in lake and reservoir ecosystems: (a) can we develop trustworthy and accurate data-driven algorithms for the prediction and forecasting of phytoplankton dynamics, and (b) can we gain insight in the drivers of phytoplankton dynamics using data-oriented solutions? These two questions motivated the webinar which discusses the extent to which satellite-derived chlorophyll-a allows for the development of theory-guided, data- driven models of phytoplankton dynamics in inland waters.