Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
Modeling Chlorophyll a Concentration for the European Arctic Corridor Based on Environmental Parameters. / Кузьмина, Софья Константиновна; Лобанова, Полина Вячеславовна.
Complex Investigation of the World Ocean (CIWO-2023) : Proceedings of the VII International Conference of Young Scientists. Springer Nature, 2023. стр. 456-462 (Springer Proceedings in Earth and Environmental Sciences (SPEES)).Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование
}
TY - GEN
T1 - Modeling Chlorophyll a Concentration for the European Arctic Corridor Based on Environmental Parameters
AU - Кузьмина, Софья Константиновна
AU - Лобанова, Полина Вячеславовна
N1 - Conference code: 7
PY - 2023/11
Y1 - 2023/11
N2 - This study analyzes remotely sensed chlorophyll a (Chl a) concentration as an indicator of ocean productivity in the European Arctic Corridor (the Norwegian, Greenland, and Barents Seas), and its connection to physical environmental parameters: Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). Using the Random Forest Machine Learning algorithm in the Classifier modification we created models describing a correlation between Chl a and environmental parameters, and retrieved a total area of high-productivity zones (Chl a is more than 1 mg m−3) based on these correlations.This research was funded by the Saint Petersburg State University, project N 94033410.
AB - This study analyzes remotely sensed chlorophyll a (Chl a) concentration as an indicator of ocean productivity in the European Arctic Corridor (the Norwegian, Greenland, and Barents Seas), and its connection to physical environmental parameters: Photosynthetically Active Radiation (PAR), Sea Surface Temperature (SST), Mixed Layer Depth (MLD) and Sea Surface Salinity (SSS). Using the Random Forest Machine Learning algorithm in the Classifier modification we created models describing a correlation between Chl a and environmental parameters, and retrieved a total area of high-productivity zones (Chl a is more than 1 mg m−3) based on these correlations.This research was funded by the Saint Petersburg State University, project N 94033410.
UR - https://link.springer.com/book/10.1007/978-3-031-47851-2
UR - https://www.mendeley.com/catalogue/4ff58abd-911b-3e5e-ac82-76d8f73a7182/
U2 - 10.1007/978-3-031-47851-2_55
DO - 10.1007/978-3-031-47851-2_55
M3 - Conference contribution
SN - 978-3-031-47850-5
T3 - Springer Proceedings in Earth and Environmental Sciences (SPEES)
SP - 456
EP - 462
BT - Complex Investigation of the World Ocean (CIWO-2023)
PB - Springer Nature
T2 - Complex Investigation of the World Ocean (CIWO-2023) VII International Conference of Young Scientists
Y2 - 15 May 2023 through 19 May 2023
ER -
ID: 113571235