TY - CHAP

T1 - Spatial and temporal variability of chlorophyll-a and its relation to physical and biological parameters: a case study for the European Arctic Corridor

AU - Kuzmina, Sofya

AU - Lobanova, Polina

AU - Bashmachnikov, Igor

N1 - Kuzmina S., Lobanova P. Spatial and temporal variability of chlorophyll-a and its relation to physical and biological parameters: a case study for the European Arctic Corridor // PICES-2022. Busan, Korea. Book of Abstracts. - P.127.

PY - 2022

Y1 - 2022

N2 - In this study we analyzed spatial and temporal variability of chlorophyll-a (Chl-a) concentration as an indicatorof ocean productivity in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas), and itsdependence on the environmental parameters: euphotic layer depth (Zeu), Photosynthetically Active Radiation(PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). We usedmonthly 4x4 km remote sensing data for 2010-2019 years from: Ocean Colour Climate Change Initiative (OCCCI) database (Chl-a and Zeu), NASA’s Ocean Biology Processing Group’s MODIS images (PAR), MUR SSTdatabase (SST), ESA’s SMOS images (SSS), while MLD was calculated from in situ EN4 Hadley Center andARMOR data. Using the Random Forest Machine Learning algorithm in the Classifier modification we createdmodels describing the relationship between Chl-a and environmental parameters, and retrieving the position ofhigh-productivity waters (Chl-a > 1 mg m-3) on the basis of these relationships. The results suggested that Ch-avariability is mostly determined by light availability (PAR and Zeu) and intrusions of dynamically active warm,saline Atlantic waters and low-salinity river output and melting ice (SST and SSS as indicators).According to validation, all the models showed a good performance (f1-score = 74-95%) and slightly underestimatedactual Chl-a values and positions of high-productivity waters.

AB - In this study we analyzed spatial and temporal variability of chlorophyll-a (Chl-a) concentration as an indicatorof ocean productivity in the European Arctic Corridor (the Barents, Norwegian and Greenland Seas), and itsdependence on the environmental parameters: euphotic layer depth (Zeu), Photosynthetically Active Radiation(PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). We usedmonthly 4x4 km remote sensing data for 2010-2019 years from: Ocean Colour Climate Change Initiative (OCCCI) database (Chl-a and Zeu), NASA’s Ocean Biology Processing Group’s MODIS images (PAR), MUR SSTdatabase (SST), ESA’s SMOS images (SSS), while MLD was calculated from in situ EN4 Hadley Center andARMOR data. Using the Random Forest Machine Learning algorithm in the Classifier modification we createdmodels describing the relationship between Chl-a and environmental parameters, and retrieving the position ofhigh-productivity waters (Chl-a > 1 mg m-3) on the basis of these relationships. The results suggested that Ch-avariability is mostly determined by light availability (PAR and Zeu) and intrusions of dynamically active warm,saline Atlantic waters and low-salinity river output and melting ice (SST and SSS as indicators).According to validation, all the models showed a good performance (f1-score = 74-95%) and slightly underestimatedactual Chl-a values and positions of high-productivity waters.

M3 - Conference abstracts

SP - 127

BT - PICES-2022. Book of Abstracts

Y2 - 23 September 2022 through 2 October 2022

ER -