Predicting spatiotemporal Chlorophyll-a (Chlₐ) distributions is essential for diagnosing and analysing productivity and ecosystem health of coastal oceans. Yet, current tools remain inadequate for prognosing marine ecosystems through predicting spatiotemporal Chlₐ distributions, particularly in the dynamic coastal ocean. Coupled physics-biogeochemical models struggle to resolve complex trophic interactions, while data-driven approaches are limited by incomplete satellite observations. We developed an advanced AI-powered spatiotemporal imputation and prediction (STIMP) model for predicting Chlₐ in coastal ocean. STIMP adopts a novel paradigm that first imputes and subsequently predicts Chlₐ across a broad spatiotemporal scale, resolving difficulties arising from incompletion, nonstationary temporal variations, and spatial heterogeneity of data through integrating specially designed modules. We demonstrated the STIMP's robust imputation and prediction of Chlₐ in four representative global coastal oceans. STIMP reduced the imputation mean absolute error (MAE) by 45. 90-81. 39% compared with the data interpolating empirical orthogonal function method in geoscience and by 8. 92-43. 04% against leading AI methods. With accurate imputation, STIMP demonstrated superior predictive accuracy, achieving MAE reductions of 58. 99% over biogeophysical models and 6. 54-13. 68% over AI benchmarks. STIMP offers a new approach for predicting oceans' Chlₐ that typically have spatiotemporally limited data.
Zhang et al. (2025) studied this question.