Further Reading:
More observations with new cost-effective technologies
Bakken S, Henriksen MB, Birkeland R, Langer DD, Oudijk AE, Berg S, Pursley Y, Garrett JL, Gran-Jansen FBS, Honoré-Livermore E, Grøtte ME, Kristiansen BA, Orlandic M, Gader P, Sørensen AJ, Sigernes F, Johnsen G, Johansen TA (2023) HYPSO-1 CubeSat: First Images and In-Orbit characterization. Remote Sensing, 15(3), 755. https://doi.org/10.3390/rs15030755
Hasler OK, Løvås HS, Bryne TH, Johansen TA (2023) Direct georeferencing for Hyperspectral Imaging of ocean surface. IEEE Aerospace Conference. https://doi.org/10.1109/AERO55745.2023.10115854
Hasler OK, Winter A, Langer DD, Bryne TH, Johansen TA (2023) Lightweight UAV (UNMANNED AREAL VEHICLE) payload for image spectroscopy and atmospheric irradiance measurements IGARSS 2023-2023 IEEE International Geoscience and Remote Sensing Symposium, 4028-4031. https://doi.org/10.1109/IGARSS52108.2023.10282661
Løken TK, Rabault J, Jensen A, Sutherland G, Christensen KH, Müller M (2021) Wave measurements from ship mounted sensors in the Arctic marginal ice zone. Cold Regions Science and Technology, 182: 103207. https://doi.org/10.1016/j.coldregions.2020.103207
Rabaul J, Nose T, Hope G, Müller M, Breivik Ø, Voermans J, Hole LR, Bohlinger P, Waseda T, Kodaira T, Katsuno T, Johnson M, Sutherland G, Johanson M, Christensen KH, Garbo A, Jensen A, Gundersen O, Marchenko A, Babanin A (2022) OpenMetBuoy-v2021: An easy-to-build, affordable, customizable, open-source instrument for oceanographic measurements of drift and waves in sea ice and the open ocean. Geosciences, 12, 110. https://doi.org/10.3390/geosciences12030110
Data for article above: Rabault J, Müller M, Voermans J,Brazhnikov D, Turnbull I, Marchenko A, Biuw, M, Nose T, Waseda T, Johansson M, Breivik Ø, Sutherland G, Hole LR, Johnson M, Jensen A, Gundersen O, Kristoffersen Y, Babanin A, Tedesco P, Christensen KH, Kristiansen M, Hope G, Kodaira T, de Aguiar V, Taelman C, Quigley CP, Filchuk K, Mahoney AR (2023) A dataset of direct observations of sea ice drift and waves in ice. Scientific Data, 10, 251. https://doi.org/10.1038/s41597-023-02160-9
Adaptive robotic sampling
Fossum TO, Eidsvik J, Ellingsen I, Alver MO, Fragoso GM, Johnsen G, Mendes R, Ludvigsen M, Rajan K (2018) Information-driven robotic sampling in the coastal ocean. Journal of Field Robotics, 35, 1101- 1121. https://doi.org/10.1002/rob.21805
Fossum TO, Fragoso GM, Davies EJ, Ullgren JE, Mendes R, Johnsen G, Ellingsen I, Eidsvik J, Ludvigsen M, Rajan K (2019) Toward adaptive robotic sampling of phytoplankton in the coastal ocean. Science Robotics, 4, eaav3041. https://doi.org/ 10.1126/scirobotics.aav3041
Fossum TO, Norgren P, Fer I, Nilsen F, Koenig ZC, Ludvigsen M (2021) Adaptive sampling of surface fronts in the Arctic using an Autonomous Underwater Vehicle. IEEE Journal of Oceanic Engineering 46: 1155-1164. https://doi: 10.1109/JOE.2021.3070912
Mo-Bjørkelund T, Majaneva S, Fragoso GM, Johnsen G, Ludvigsen M (2024) Multi-vehicle adaptive 3D mapping for targeted ocean sampling. PLoS ONE, 19(5), e0302514. https://doi.org/10.1371/journal.pone.0302514
Sture Ø, Norgren P, Ludvigsen M (2020) Trajectory planning for navigation aiding of Autonomous Underwater Vehicles. IEEE Access 8: 116586-116604. https://doi.org/10.1109/ACCESS.2020.3004439
Williamson DR, Fragoso, GL, Majaneva S, Dallolio A, Halvorsen DØ. Hasler O, Oudijk AE, Langer DD, Johansen TA, Johnsen G, Stahl A, Ludvigsen M, Garrett JL. (2023) Monitoring Algal Blooms with Complementary Sensors on Multiple Spatial and Temporal Scales, Oceanography, 36(1), 36-37. https://doi.org/10.5670/oceanog.2023.s1.11
Field-based validation data improve model performance
Batrak Y, Müller M (2019) On the warm bias in atmospheric reanalyses induced by the missing snow over arctic sea-ice. Nature Communications, 10, 4170. https://doi.org/10.1038/s41467-019-11975-3
Herrmannsdörfer L, Müller M, Shupe MD, Rostosky P (2023) Surface temperature comparison of the Arctic winter MOSAiC observations, ERA5 reanalysis, and MODIS satellite retrieval. Elementa: Science of the Anthropocene, 11(1), 0008. doi.org/10.1525/elementa.2022.00085
Analyzing data in a new way
Asim M, Matsuoka A, Ellingsen PG, Brekke C, Eltoft T and Blix K (2022) A new spectral harmonization algorithm for Landsat-8 and Sentinel-2 remote sensing reflectance products using machine learning: a case study for the Barents Sea (European Arctic). IEEE Transactions on Geoscience and Remote Sensing 61. https://doi.org/10.1109/TGRS.2022.3228393
Liu H, Summers N, Chen Y-C, Løvås HS, Johnsen G, Koestner D, Sætre C, Hamre B (2024) Pixelwise immersion factor calibration for underwater hyperspectral imaging instruments. Optics Express, 32(11), 19854-19880. https://doi.org/10.1364/OE.523641
Schartmüller B, Anderson P, McKee D, Connan-McGinty S, Kopec TP, Daase M, Johnsen G, Berge J (2023) Development and calibration of a high dynamic range and autonomous ocean-light instrument to measure sub-surface profiles in ice-covered waters. Applied Optics, 62(31), 8308-8315. https://doi.org/10.1364/AO.502437
Thomas E, Müller M (2022) Characterizing vertical upper ocean temperature structures in the European Arctic through unsupervised machine learning. Ocean Modelling 177:102092. https://doi.org/10.1016/j.ocemod.2022.102092
Risk analyses
Yang R, Bremnes JE, Utne IB. (2023) Online risk modeling of autonomous marine systems: Case study of autonomous operations under sea ice, Ocean engineering, 281, 114765. https://doi.org/10.1016/j.oceaneng.2023.114765
Yang R, Utne IB (2022) Towards an online risk model for autonomous marine systems (AMS). Ocean Engineering, 251, 111100. https://doi.org/10.1016/j.oceaneng.2022.111100
Yang R, Utne IB, Liu Y, Paltrinieri N (2020) Dynamic Risk Analysis of Operation of the Autonomous Underwater Vehicle (AUV). Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference. Edited by Piero Baraldi, Francesco Di Maio and Enrico Zio. Research Publishing, Singapore. https://doi.org/10.3850/978-981-14-8593-0_4118-cd
Yang R, Vatn J, Utne IB (2023) Dynamic maintenance planning for autonomous marine systems (AMS) and operations. Ocean Engineering, 278, 114492. https://doi.org/10.1016/j.oceaneng.2023.114492