Biological Oceanography Lab
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Multi-model remote sensing

Multi-model remote sensing assessment of primary production in the subtropical gyres.

Regaudie-de-Gioux A., Huete-Ortega M., Sobrino C., López-Sandoval D. C., González N., Fernández-Carrera A., Vidal M., Marañón E., Cermeño P., Latasa, M., Agustí S., and C.M. Duarte. 2019. Multi-model remote sensing assessment of primary production in the subtropical gyres. J. Marine Systems 10.1016/j.jmarsys.2019.03.007

Regaudie-de-Gioux A., Huete-Ortega M., Sobrino C., López-Sandoval D. C., González N., Fernández-Carrera A., Vidal M., Marañón E., Cermeño P., Latasa, M., Agustí S., and C.M. Duarte
Primary production Remote PP model Skills Subtropical gyre
2019
The subtropical gyres occupy about 70% of the ocean surface. While primary production (PP) within these oligotrophic regions is relatively low, their extension makes their total contribution to ocean productivity significant. Monitoring marine pelagic primary production across broad spatial scales, particularly across the subtropical gyre regions, is challenging but essential to evaluate the oceanic carbon budget. PP in the ocean can be derived from remote sensing however in situ depth-integrated PP (IPPis) measurements required for validation are scarce from the subtropical gyres. In this study, we collected >120 IPPis measurements from both northern and southern subtropical gyres that we compared to commonly used primary productivity models (the Vertically Generalized Production Model, VGPM and six variants; the Eppley-Square-Root model, ESQRT; the Howard–Yoder–Ryan model, HYR; the model of MARRA, MARRA; and the Carbon-based Production Model, CbPM) to predict remote PP (PPr) in the subtropical regions and explored possibilities for improving PP prediction. Our results showed that satellite-derived PP (IPPsat) estimates obtained from the VGPM1, MARRA and ESQRT provided closer values to the IPPis (i.e., the difference between the mean of the IPPsat and IPPis was closer to 0; |Bias| ~ 0.09). Model performance varied due to differences in satellite predictions of in situ parameters such as chlorophyll a (chl-a) concentration or the optimal assimilation efficiency of the productivity profile (PBopt) in the subtropical region. In general, model performance was better for areas showing higher IPPis, highlighting the challenge of PP prediction in the most oligotrophic areas (i.e. PP < 300 mg C m−2 d−1). The use of in situ chl-a data, and PBopt as a function of sea surface temperature (SST) and the mixed layer depth (MLD) from gliders and floats in PPr models would improve their IPP predictions considerably in oligotrophic oceanic regions such as the subtropical gyres where MLD is relatively low (<60 m) and cloudiness may bias satellite input data.