49 citations as recorded by crossref.

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4. HYDROPT: An Open-Source Framework for Fast Inverse Modelling of Multi- and Hyperspectral Observations from Oceans, Coastal and Inland Waters T. Holtrop & H. Van Der Woerd 10.3390/rs13153006
5. Hindcast and forecast of daily inundation extents using satellite SAR and altimetry data with rotated empirical orthogonal function analysis: Case study in Tonle Sap Lake Floodplain C. Chang et al. 10.1016/j.rse.2020.111732
6. Synergistic Exploitation of Hyper- and Multi-Spectral Precursor Sentinel Measurements to Determine Phytoplankton Functional Types (SynSenPFT) S. Losa et al. 10.3389/fmars.2017.00203
7. Underway spectrophotometry in the Fram Strait (European Arctic Ocean): a highly resolved chlorophyll a data source for complementing satellite ocean color Y. Liu et al. 10.1364/OE.26.00A678
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11. Floodwater impact on Galveston Bay phytoplankton taxonomy, pigment composition and photo-physiological state following Hurricane Harvey from field and ocean color (Sentinel-3A OLCI) observations B. Liu et al. 10.5194/bg-16-1975-2019
12. Differences in Rate and Direction of Shifts between Phytoplankton Size Structure and Sea Surface Temperature H. Waga et al. 10.3390/rs9030222
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15. Changes in phytoplankton community structure during wind-induced fall bloom on the central Chukchi shelf A. Fujiwara et al. 10.1007/s00300-018-2284-7
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17. A Self-Attention-Based Deep Learning Model for Estimating Global Phytoplankton Pigment Profiles Y. Yang et al. 10.1109/TGRS.2024.3435044
18. Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective A. Stock & A. Subramaniam 10.3389/fmars.2020.00599
19. Remote estimation of phytoplankton size fractions using the spectral shape of light absorption S. Wang et al. 10.1364/OE.23.010301
20. Statistical approach for the retrieval of phytoplankton community structures from in situ fluorescence measurements S. Wang et al. 10.1364/OE.24.023635
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22. A Novel Statistical Approach for Ocean Colour Estimation of Inherent Optical Properties and Cyanobacteria Abundance in Optically Complex Waters M. Soja-Woźniak et al. 10.3390/rs9040343
23. Satellite Observations of Phytoplankton Functional Type Spatial Distributions, Phenology, Diversity, and Ecotones T. Moisan et al. 10.3389/fmars.2017.00189
24. High Resolution Water Column Phytoplankton Composition Across the Atlantic Ocean From Ship-Towed Vertical Undulating Radiometry A. Bracher et al. 10.3389/fmars.2020.00235
25. Linking satellites to genes with machine learning to estimate phytoplankton community structure from space R. El Hourany et al. 10.5194/os-20-217-2024
26. Modeling Photoprotection at Global Scale: The Relative Role of Nonphotosynthetic Pigments, Physiological State, and Species Composition E. Álvarez et al. 10.1029/2018GB006101
27. Gaussian decomposition and component pigment spectral analysis of phytoplankton absorption spectra H. Ye et al. 10.1007/s00343-019-8079-z
28. Radiative transfer modeling through terrestrial atmosphere and ocean accounting for inelastic processes: Software package SCIATRAN V. Rozanov et al. 10.1016/j.jqsrt.2017.03.009
29. Retrieval of Phytoplankton Pigment Composition from Their In Vivo Absorption Spectra Y. Zhang et al. 10.3390/rs13245112
30. Retrieval of Phytoplankton Pigments from Underway Spectrophotometry in the Fram Strait Y. Liu et al. 10.3390/rs11030318
31. Using Dynamic Ocean Color Provinces to Elucidate Drivers of North Sea Hydrography and Ecology M. Taylor et al. 10.1029/2021JC017686
32. Reconstruction of sea-land interactions between terrestrial vegetation cover and water quality constituents in the Mattapoisett Harbor area during the 1991 Hurricane Bob event N. Chang et al. 10.1016/j.jag.2019.101929
33. Phytoplankton Pigment Communities Can be Modeled Using Unique Relationships With Spectral Absorption Signatures in a Dynamic Coastal Environment D. Catlett & D. Siegel 10.1002/2017JC013195
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35. Spatiotemporal distribution of labeled data can bias the validation and selection of supervised learning algorithms: A marine remote sensing example A. Stock 10.1016/j.isprsjprs.2022.02.023
36. Estimation of Phytoplankton Accessory Pigments From Hyperspectral Reflectance Spectra: Toward a Global Algorithm A. Chase et al. 10.1002/2017JC012859
37. Estimation of the Potential Detection of Diatom Assemblages Based on Ocean Color Radiance Anomalies in the North Sea A. Rêve-Lamarche et al. 10.3389/fmars.2017.00408
38. Linking phytoplankton absorption to community composition in Chinese marginal seas D. Sun et al. 10.1016/j.pocean.2021.102517
39. Phytoplankton composition from sPACE: Requirements, opportunities, and challenges I. Cetinić et al. 10.1016/j.rse.2023.113964
40. Two-decadal estimation of sixteen phytoplankton pigments from satellite observations in coastal waters D. Sun et al. 10.1016/j.jag.2022.102715
41. Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development A. Bracher et al. 10.3389/fmars.2017.00055
42. GLORIA - A globally representative hyperspectral in situ dataset for optical sensing of water quality M. Lehmann et al. 10.1038/s41597-023-01973-y
43. High-resolution physical–biogeochemical structure of a filament and an eddy of upwelled water off northwest Africa W. von Appen et al. 10.5194/os-16-253-2020
44. Seasonal dynamics of major phytoplankton functional types in the coastal waters of the west coast of Canada derived from OLCI Sentinel 3A P. Vishnu et al. 10.3389/fmars.2022.1018510
45. Field Intercomparison of Radiometer Measurements for Ocean Colour Validation G. Tilstone et al. 10.3390/rs12101587
46. Modeling surface ocean phytoplankton pigments from hyperspectral remote sensing reflectance on global scales S. Kramer et al. 10.1016/j.rse.2021.112879
47. Iterative spatial leave-one-out cross-validation and gap-filling based data augmentation for supervised learning applications in marine remote sensing A. Stock & A. Subramaniam 10.1080/15481603.2022.2107113
48. NASA's surface biology and geology designated observable: A perspective on surface imaging algorithms K. Cawse-Nicholson et al. 10.1016/j.rse.2021.112349
49. Using empirical orthogonal functions derived from remote-sensing reflectance for the prediction of phytoplankton pigment concentrations A. Bracher et al. 10.5194/os-11-139-2015