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The science needed to understand and mitigate the impacts of global change on the biosphere will require both unprecedented access to diverse biological and environmental data across space, time, and scales and the synthesis and development of predictive theory (Dietze et al., 2018, Bush et al., 2017, Hampton et al., 2013). In this white paper, we argue that while environmental data from RS have been accumulating at a rapid pace, their broad scope generates major challenges for finding effective ways to discover, access, integrate, curate, and analyze the range and volume of relevant information. Second, to generalize and improve forecasts, there is an urgent need to harness big data and data synthesis with the vision and foresight of analytical and quantitative theory. We identify the key ML/AI capabilities to further enhance the predictability of ecosystem models: (1) enhanced connectivity from RS to model parameterization, (2) theory/model-informed RS-based estimation. Enhanced connectivity from RS to model parameterization. RS data together with data