@article {schwing_modern_2023,
	title = {Modern technologies and integrated observing systems are {\textquotedblleft}instrumental{\textquotedblright} to fisheries oceanography: A brief history of ocean data collection},
	journal = {Fisheries Oceanography},
	volume = {32},
	number = {1},
	year = {2023},
	note = {_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/fog.12619},
	pages = {28{\textendash}69},
	abstract = {Interdisciplinary data fuel fisheries oceanography research and the ecosystem-based approaches to management and sustainable development it informs. Underlying this is a distributed ocean observing framework that is integrated, interoperable, interactive, and accessible. In recognition of the 30th anniversary of Fisheries Oceanography the journal, this paper reviews the evolution of observing instruments and platforms used in contemporary fisheries oceanography the science. Illustrated with personal anecdotes, past efforts to create or adopt observing technologies, and examples of their use in research, this highlights the spectrum of instruments, systems, and programs used to survey and monitor ocean ecosystems. Modern ocean observing systems are complex and varied, reflecting the range and diversity of data required by fisheries oceanographers. These systems require a large and ongoing investment and an interdisciplinary community of scientists, engineers, and technicians to design, build, install, operate, and maintain them. Common themes emerge from a review of past successful instrument R\&D and deployments. It is a highly collaborative, integrative, and iterative process. Most systems are the result of vision, planning, and perseverance, backed by careful calibration and intercomparison. Long-term support is essential; public{\textendash}private partnerships that leverage funding, technology, and infrastructure are critical. Sustaining long time series for monitoring population and ecosystem change and to support fisheries oceanography research is a priority. Future areas of focus include continuously innovating and updating technologies, implementing a backbone of core observations, and maintaining a nimble infrastructure and R\&D capacity to seize new opportunities and address emerging challenges.},
	keywords = {acoustics, currents, data, fisheries oceanography, observing systems, Satellites, uncrewed systems},
	issn = {1365-2419},
	doi = {10.1111/fog.12619},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/fog.12619},
	author = {Schwing, Franklin B.}
}
@article {RN177,
	title = {Deep Learning for Predicting Significant Wave Height From Synthetic Aperture Radar},
	journal = {IEEE Transactions on Geoscience and Remote Sensing},
	volume = {PP},
	number = {99},
	year = {2020},
	pages = {1-9},
	type = {Journal Article},
	abstract = {The Sentinel-1 satellites equipped with synthetic aperture radars (SARs) provide near-global coverage of the world{\textquoteright}s oceans every six days. We curate a data set of collocations between SAR and altimeter satellites and investigate the use of deep learning to predict significant wave height from SAR. While previous models for predicting geophysical quantities from SAR rely heavily on feature-engineering, our approach learns directly from low-level image cross-spectra. Training on collocations from 2015 to 2017, we demonstrate on test data from 2018 that deep learning reduces the state-of-the-art root mean squared error by 50\%, from 0.6 to 0.3 m when compared to altimeter data. Furthermore, we isolate the contributions of different features to the model performance.},
	keywords = {Data models, Modulation, Satellites, Sea measurements, Sea surface, Synthetic aperture radar},
	doi = {10.1109/tgrs.2020.3003839},
	url = {https://app.dimensions.ai/details/publication/pub.1129457664},
	author = {Quach, Brandon and Glaser, Yannik and Stopa, Justin Edward and Mouche, Alexis Aurelien and Sadowski, Peter}
}
