from 01.01.2022 until now
Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung
Russian Federation
Russian Federation
Russian Federation
UDK 573.22 Теория систем в биологии. Уровни организации биологических систем
UDK 55 Геология. Геологические и геофизические науки
UDK 550.34 Сейсмология
UDK 550.383 Главное магнитное поле Земли
GRNTI 37.01 Общие вопросы геофизики
GRNTI 37.15 Геомагнетизм и высокие слои атмосферы
GRNTI 37.25 Океанология
GRNTI 37.31 Физика Земли
GRNTI 38.01 Общие вопросы геологии
GRNTI 36.00 ГЕОДЕЗИЯ. КАРТОГРАФИЯ
GRNTI 37.00 ГЕОФИЗИКА
GRNTI 38.00 ГЕОЛОГИЯ
GRNTI 39.00 ГЕОГРАФИЯ
GRNTI 52.00 ГОРНОЕ ДЕЛО
OKSO 05.00.00 Науки о Земле
BBK 26 Науки о Земле
TBK 63 Науки о Земле. Экология
BISAC SCI SCIENCE
Over the past 20 years, increasing temperature and receding ice-cover have led to changes in the Arctic ecosystem. Our study aims to create models that predict the position of high chlorophyll-a concentration (Chl-a) zones in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas) to monitor these changes. Firstly, we use remotely sensed data to assess spatial and temporal changes in correlation between Chl-a and environmental parameters that could influence Chl-a in the region – Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS) – over the 2010–2019 time period. We found significant correlation (∣r∣ = 0.6–0.8) between Chl-a and PAR and SST, and medium correlation (∣r∣ = 0.4–0.6) between Chl-a and SSS and MLD, correlation was highest during spring periods. Then, using a Random Forest Machine Learning algorithm in the Classifier modification, we created models for each sea to predict the position of high-productivity zones (Chl-a > 1 mg m−3) using environmental parameters. Our results suggested that Chl-a variability in the European Arctic Corridor is mostly determined by PAR (28–32% of Chl-a class variability), SST (25–29%), and SSS (26–31%); MLD played a lesser role (12–17%). According to validation, all the models showed high performance scores (F1-score = 66–95%) and slightly underestimated the total area of high productivity.
chlorophyll-a, ocean productivity, Arctic Ocean, modeling, ocean colour, remote sensing, Barents Sea, Norwegian Sea, Greenland Sea
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