Advancing Oceanic Studies with HyperOCR Sensors and Non-Negative Matrix Factorization: A Cost-Effective, Data-Driven Approach for Analyzing Light in Marine Water Column

Abstract

ABSTRACTUnderstanding the intricate dynamics of ocean biogeochemistry is crucial for deciphering its role in climate change. Our study addresses this challenge by integrating advanced computational techniques and innovative sensor technology to enhance remote sensing capabilities. Drawing on recent insights into the vast carbon reservoirs within the ocean, particularly within the dissolved organic matter (DOM) pool, we highlight the pressing need for comprehensive spatial and temporal understanding facilitated by a combination of satellite and in situ data. However, existing remote sensing methods face limitations in capturing subsurface processes, hindering our ability to grasp carbon fluxes within the oceanic water column fully. Recent advancements in remote sensing offer promising avenues for addressing these challenges. Studies investigating polarized radiance distribution and Chromophoric Dissolved Organic Matter (CDOM) provide valuable insights into improving remote sensing capabilities. Building upon these advancements, we propose a novel data-driven approach utilizing HyperOCR sensors and non-negative matrix factorization (NMF). Non-negative matrix factorization (NMF) is a powerful tool for extracting meaningful biological signatures from hyperspectral data, offering a granular yet comprehensive view of spectral diversity. Our study showcases the potential of NMF in elucidating spatial and temporal variations in biogeochemical processes within the ocean. Leveraging HyperOCR sensors, our approach offers a cost-effective and efficient means of enhancing remote sensing capabilities, enabling the rapid deployment and identification of seasonal patterns in the water column. Through extensive validation against field data from the Adriatic Sea, we demonstrate the utility of our approach in refining satellite measurements and improving algorithms for analyzing ocean color data. Our findings underscore the importance of integrating multiple observational platforms and advanced computational techniques to enhance the accuracy and reliability of remote sensing in ocean biogeochemistry studies. In conclusion, our study contributes to a deeper understanding of marine ecosystems’ responses to environmental changes and offers a new perspective on remote sensing capabilities, particularly in challenging coastal waters. By bridging the gap between satellite and in situ measurements, our approach exemplifies a promising pathway for advancing remote sensing of ocean biogeochemistry.