TY - JOUR
T1 - Multi-model remote sensing assessment of primary production in the subtropical gyres
AU - Regaudie-de-Gioux, A.
AU - Huete-Ortega, M.
AU - Sobrino, C.
AU - Lopez Sandoval, Daffne
AU - González, N.
AU - Fernández-Carrera, A.
AU - Vidal, M.
AU - Marañón, E.
AU - Cermeño, P.
AU - Latasa, M.
AU - Agusti, Susana
AU - Duarte, Carlos M.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work is a contribution to the Malaspina Circumnavigation Expedition 2010, funded by the INGENIO 2010 CONSOLIDER program (ref. CDS2008-00077) of the Spanish Ministry of Economy and Competitiveness. We thank the crew of R/V Hespérides for their invaluable support during the expedition, Dr. A. López-Urrutia and Dr. F. García-García for their help with PP models and satellite data collection.
PY - 2019/3/29
Y1 - 2019/3/29
N2 - 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
AB - 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
UR - http://hdl.handle.net/10754/653065
UR - https://www.sciencedirect.com/science/article/pii/S0924796318303385
UR - http://www.scopus.com/inward/record.url?scp=85064955531&partnerID=8YFLogxK
U2 - 10.1016/j.jmarsys.2019.03.007
DO - 10.1016/j.jmarsys.2019.03.007
M3 - Article
SN - 0924-7963
VL - 196
SP - 97
EP - 106
JO - Journal of Marine Systems
JF - Journal of Marine Systems
ER -