Predicting subsurface sonar observations with satellite-derived ocean surface data in the California Current Ecosystem.

ABSTRACT: Vessel-based sonar systems that focus on the water column provide valuable information on the distribution of underwater marine organisms, but such data are expensive to collect and limited in their spatiotemporal coverage. Satellite data, however, are widely available across large regions and provide information on surface ocean conditions. If satellite data can be linked to subsurface sonar measurements, it may be possible to predict marine life over broader spatial regions with higher frequency using satellite observations. Here, we use random forest models to evaluate the potential for predicting a sonar-derived proxy for subsurface biomass as a function of satellite imagery in the California Current Ecosystem. We find that satellite data may be useful for prediction under some circums

SUBMITTER: Gadeken KR 

PROVIDER: S-EPMC8378737 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature