Marine big data-driven ensemble learning for estimating global phytoplankton group composition over two decades (1997–2020)

Abstract

Accurate monitoring of the spatial-temporal distribution and variability of phytoplankton group (PG) composition is of vital importance in better understanding of marine ecosystem dynamics and biogeochemical cycles. While existing bio-optical algorithms provide valuable information, relying solely on satellite ocean color data remains insufficient to obtain high-precision retrieval of PG due to the intricate nature of the bio-optical signal and PG composition itself. An interdisciplinary approach combining advancements in machine learning with big data from ocean observations and simulations offers a promising avenue for more accurate quantification of PG composition. In this study, an ensemble learning approach, called the spatial-temporal-ecological ensemble (STEE) model, is developed to construct a robust prediction model for eight distinct phytoplankton groups (i.e., Diatoms, Dinoflagellates, Haptophytes, Pelagophytes, Cryptophytes, Green Algae, Prokaryotes, and Prochlorococcus). The proposed method introduces multiple data simultaneously: ocean color, physical oceanographic, biogeochemical, and spatial and temporal information. An ensemble strategy is applied to increase the performance of the model by merging three advanced machine-learning algorithms. The combined validation of multiple cross-validation (CV) strategies (i.e., standard, spatial block, and temporal block CVs) shows that the proposed STEE model has superior robustness and generalization ability. In addition, the analysis shows a high degree of concordance between the independent datasets and the modeled estimations for long-time series sites, indicating that the STEE model is capable of effectively monitoring long-term trends in phytoplankton group composition. Finally, the proposed model was utilized to retrieve global monthly phytoplankton group products (STEE-PG) over an extended period (September 1997 to May 2020), and comparisons demonstrated better rationality of spatio-temporal distribution than existing satellite-derived phytoplankton group products. Hence, this new model comprehensively integrates all kinds of observation data and yields long-term global PG products with high accuracy, which will enhance our understanding of the response of marine ecosystems to environmental and climate change.