Catipovic, L., Matic, F., and Kalinic, H.: Reconstruction Methods in Oceanographic Satellite Data Observation-A Survey, J. Mar. Sci. Eng., 11, 340, https://doi.org/10.3390/jmse11020340, 2023.
Cen, H. B., Jiang, J. H., Han, G. Q., Lin, X. Y., Liu, Y., Jia, X. Y., Ji, Q. Y., and Li, B.: Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea, Remote Sens., 14, 5461, https://doi.org/10.3390/rs14215461, 2022.
Chen, S., Hu, C., Barnes, B. B., Xie, Y., Lin, G., and Qiu, Z.: Improving ocean color data coverage through machine learning, Remote Sens. Environ., 222, 286–302, https://doi.org/10.1016/j.rse.2018.12.023, 2019.
Dai, Y., Yang, S., Zhao, D., Hu, C., Xu, W., Anderson, D. M., Li, Y., Song, X.-P., Boyce, D. G., Gibson, L., Zheng, C., and Feng, L.: Coastal phytoplankton blooms expand and intensify in the 21st century, Nature, 615, 280–284, https://doi.org/10.1038/s41586-023-05760-y, 2023.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010.
Ducklow, H., Cimino, M., Dunton, K. H., Fraser, W. R., Hopcroft, R. R., Ji, R., Miller, A. J., Ohman, M. D., and Sosik, H. M.: Marine Pelagic Ecosystem Responses to Climate Variability and Change, BioScience, 72, 827–850, https://doi.org/10.1093/biosci/biac050, 2022.
Duteil, O. and Park, W.: Future changes in atmospheric synoptic variability slow down ocean circulation and decrease primary productivity in the tropical Pacific Ocean, Npj Climate and Atmospheric Science, 6, 136, https://doi.org/10.1038/s41612-023-00459-3, 2023.
Feng, L. and Hu, C. M.: Comparison of Valid Ocean Observations Between MODIS Terra and Aqua Over the Global Oceans, IEEE T. Geosci. Remote, 54, 1575–1585, https://doi.org/10.1109/tgrs.2015.2483500, 2016.
Feng, L., Dai, Y., Hou, X., Xu, Y., Liu, J., and Zheng, C.: Concerns about phytoplankton bloom trends in global lakes, Nature, 590, E35–E47, https://doi.org/10.1038/s41586-021-03254-3, 2021.
Flynn, K. J.: A mechanistic model for describing dynamic multi-nutrient, light, temperature interactions in phytoplankton, J. Plankton Res., 23, 977–997, https://doi.org/10.1093/plankt/23.9.977, 2001.
Geng, T., Cai, W. J., Wu, L. X., Santoso, A., Wang, G. J., Jing, Z., Gan, B. L., Yang, Y., Li, S. J., Wang, S. P., Chen, Z. H., and McPhaden, M. J.: Emergence of changing Central-Pacific and Eastern-Pacific El Nino-Southern Oscillation in a warming climate, Nat. Commun., 13, 6616, https://doi.org/10.1038/s41467-022-33930-5, 2022.
Gentemann, C. L.: Three way validation of MODIS and AMSR-E sea surface temperatures, J. Geophys. Res.-Oceans, 119, 2583–2598, https://doi.org/10.1002/2013jc009716, 2014.
Gruber, N., Boyd, P. W., Frölicher, T. L., and Vogt, M.: Biogeochemical extremes and compound events in the ocean, Nature, 600, 395–407, https://doi.org/10.1038/s41586-021-03981-7, 2021.
Han, Y. and Zhou, Y. T.: Investigating biophysical control of marine phytoplankton dynamics via Bayesian mechanistic modeling, Ecol. Model., 474, 110168, https://doi.org/10.1016/j.ecolmodel.2022.110168, 2022.
Hilborn, A. and Costa, M.: Applications of DINEOF to Satellite-Derived Chlorophyll-a from a Productive Coastal Region, Remote Sens., 10, 1449, https://doi.org/10.3390/rs10091449, 2018.
Ho, J. C., Michalak, A. M., and Pahlevan, N.: Widespread global increase in intense lake phytoplankton blooms since the 1980s, Nature, 574, 667–670, https://doi.org/10.1038/s41586-019-1648-7, 2019.
Hong, Z. K., Long, D., Li, X. D., Wang, Y. M., Zhang, J. M., Mohamed, A. H., and Mohamed, M. M.: OCNET global daily Chlorophyll-a products, Zenodo [data set], https://doi.org/10.5281/zenodo.10011908, 2023.
Hu, C., Lee, Z., and Franz, B.: Chlorophyll a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference:a novel ocean chlorophyll a algorithm, J. Geophys. Res.-Oceans, 117, C01011, https://doi.org/10.1029/2011JC007395, 2012.
Hu, C., Feng, L., Lee, Z., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W.: Improving Satellite Global Chlorophyll-a Data Products Through Algorithm Refinement and Data Recovery, J. Geophys. Res.-Oceans, 124, 1524–1543, https://doi.org/10.1029/2019JC014941, 2019a.
Hu, C. M., Feng, L., Lee, Z. P., Franz, B. A., Bailey, S. W., Werdell, P. J., and Proctor, C. W.: Improving Satellite Global Chlorophyll a Data Products Through Algorithm Refinement and Data Recovery, J. Geophys. Res.-Oceans, 124, 1524–1543, https://doi.org/10.1029/2019jc014941, 2019b.
Jiang, L. D. and Wang, M. H.: Identification of pixels with stray light and cloud shadow contaminations in the satellite ocean color data processing, Appl. Opt., 52, 6757–6770, https://doi.org/10.1364/ao.52.006757, 2013.
Jin, D., Lee, E., Kwon, K., and Kim, T.: A Deep Learning Model Using Satellite Ocean Color and Hydrodynamic Model to Estimate Chlorophyll-a Concentration, Remote Sens., 13, 2003, https://doi.org/10.3390/rs13102003, 2021.
Konik, M., Kowalewski, M., Bradtke, K., and Darecki, M.: The operational method of filling information gaps in satellite imagery using numerical models, Int. J. Appl. Earth Obs., 75, 68–82, https://doi.org/10.1016/j.jag.2018.09.002, 2019.
Krug, L. A., Platt, T., Sathyendranath, S., and Barbosa, A. B.: Ocean surface partitioning strategies using ocean colour remote Sensing: A review, Prog. Oceanogr., 155, 41–53, https://doi.org/10.1016/j.pocean.2017.05.013, 2017.
Laufkötter, C., Vogt, M., Gruber, N., Aumont, O., Bopp, L., Doney, S. C., Dunne, J. P., Hauck, J., John, J. G., Lima, I. D., Seferian, R., and Völker, C.: Projected decreases in future marine export production: the role of the carbon flux through the upper ocean ecosystem, Biogeosciences, 13, 4023–4047, https://doi.org/10.5194/bg-13-4023-2016, 2016.
Le Grix, N., Zscheischler, J., Laufkötter, C., Rousseaux, C. S., and Frölicher, T. L.: Compound high-temperature and low-chlorophyll extremes in the ocean over the satellite period, Biogeosciences, 18, 2119–2137, https://doi.org/10.5194/bg-18-2119-2021, 2021.
Levitus, S., Boyer, T. P., García, H. E., Locarnini, R. A., Zweng, M. M., Mishonov, A. V., Reagan, J. R., Antonov, J. I., Baranova, O. K., Biddle, M., Hamilton, M., Johnson, D. R., Paver, C. R., and Seidov, D.: World Ocean Atlas 2013 (NCEI Accession 0114815), [data set], doi.org/10.7289/v5f769gt, 2014.
Li, H., Xu, F. H., Zhou, W., Wang, D. X., Wright, J. S., Liu, Z. H., and Lin, Y. L.: Development of a global gridded Argo data set with Barnes successive corrections, J. Geophys. Res.-Oceans, 122, 866–889, https://doi.org/10.1002/2016jc012285, 2017.
Li, J., Gao, M., Feng, L., Zhao, H., Shen, Q., Zhang, F., Wang, S., and Zhang, B.: Estimation of Chlorophyll-a Concentrations in a Highly Turbid Eutrophic Lake Using a Classification-Based MODIS Land-Band Algorithm, IEEE J. Sel. Top. Appl., 12, 3769–3783, https://doi.org/10.1109/JSTARS.2019.2936403, 2019.
Li, X., Liu, B., Zheng, G., Ren, Y., Zhang, S., Liu, Y., Gao, L., Liu, Y., Zhang, B., and Wang, F.: Deep-learning-based information mining from ocean remote-sensing imagery, Nat. Sci. Rev., 7, 1584–1605, 10.1093/nsr/nwaa047, 2020.
Lian, F.: Key issues in detecting lacustrine cyanobacterial bloom using satellite remote sensing, J. Lake Sci., 33, 647–652, https://doi.org/10.18307/2021.0301, 2021.
Liu, D., Zhou, C., Keesing, J. K., Serrano, O., Werner, A., Fang, Y., Chen, Y., Masque, P., Kinloch, J., Sadekov, A., and Du, Y.: Wildfires enhance phytoplankton production in tropical oceans, Nat. Commun., 13, 1348, https://doi.org/10.1038/s41467-022-29013-0, 2022.
Liu, X. and Wang, M.: Gap Filling of Missing Data for VIIRS Global Ocean Color Products Using the DINEOF Method, IEEE T. Geosci. Remote, 56, 4464–4476, https://doi.org/10.1109/TGRS.2018.2820423, 2018.
Liu, X. and Wang, M.: Global daily gap-free ocean color products from multi-satellite measurements, Int. J. Appl. Earth Obs., 108, 102714, https://doi.org/10.1016/j.jag.2022.102714, 2022.
McColl, K. A., Vogelzang, J., Konings, A. G., Entekhabi, D., Piles, M., and Stoffelen, A.: Extended triple collocation: Estimating errors and correlation coefficients with respect to an unknown target, Geophys. Res. Lett., 41, 6229–6236, https://doi.org/10.1002/2014gl061322, 2014.
Mikelsons, K. and Wang, M.: Optimal satellite orbit configuration for global ocean color product coverage, Opt. Express, 27, A445, https://doi.org/10.1364/OE.27.00A445, 2019.
Miralles, D. G., Crow, W. T., and Cosh, M. H.: Estimating Spatial Sampling Errors in Coarse-Scale Soil Moisture Estimates Derived from Point-Scale Observations, J. Hydrometeorol., 11, 1423–1429, https://doi.org/10.1175/2010jhm1285.1, 2010.
Moran, N., Stringer, B., Lin, B. C., and Hoque, M. T.: Machine learning model selection for predicting bathymetry, Deep-Sea Res. Pt. I, 185, 103788, https://doi.org/10.1016/j.dsr.2022.103788, 2022.
Mouw, C. B., Barnett, A., McKinley, G. A., Gloege, L., and Pilcher, D.: Phytoplankton size impact on export flux in the global ocean, Global Biogeochem. Cy., 30, 1542–1562, https://doi.org/10.1002/2015gb005355, 2016.
Nelson, N. G., Munoz-Carpena, R., and Phlips, E.: Parameter uncertainty drives important incongruities between simulated chlorophyll-
a and phytoplankton functional group dynamics in a mechanistic management model, Environ. Model. Softw., 129, 104708, https://doi.org/10.1016/j.envsoft.2020.104708, 2020.
Nikolaidis, A., Georgiou, G. C., Hadjimitsis, D., and Akylas, E.: Filling in missing sea-surface temperature satellite data over the Eastern Mediterranean Sea using the DINEOF algorithm, Cent. Eur. J. Geosci., 6, 27–41, https://doi.org/10.2478/s13533-012-0148-1, 2014.
Powell, C. F., Baker, A. R., Jickells, T. D., Bange, H. W., Chance, R. J., and Yodle, C.: Estimation of the Atmospheric Flux of Nutrients and Trace Metals to the Eastern Tropical North Atlantic Ocean, J. Atmos. Sci., 72, 4029–4045, https://doi.org/10.1175/jas-d-15-0011.1, 2015.
Reygondeau, G., Longhurst, A., Martinez, E., Beaugrand, G., Antoine, D., and Maury, O.: Dynamic biogeochemical provinces in the global ocean, Global Biogeochem. Cy., 27, 1046–1058, https://doi.org/10.1002/gbc.20089, 2013.
Righetti, D., Vogt, M., Gruber, N., Psomas, A., and Zimmermann, N. E.: Global pattern of phytoplankton diversity driven by temperature and environmental variability, Sci. Adv., 5, eaau6253, https://doi.org/10.1126/sciadv.aau6253, 2019.
Ronneberger, O., Fischer, P., and Brox, T.: U-Net: Convolutional Networks for Biomedical Image Segmentation, 18th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), Munich, Germany, 5–9 October 2015, WOS:000365963800028, 234–241, https://doi.org/10.1007/978-3-319-24574-4_28, 2015.
Roussillon, J., Fablet, R., Gorgues, T., Drumetz, L., Littaye, J., and Martinez, E.: A Multi-Mode Convolutional Neural Network to reconstruct satellite-derived chlorophyll-
a time series in the global ocean from physical drivers, Front. Mar. Sci., 10, 1077623, https://doi.org/10.3389/fmars.2023.1077623, 2023.
Salgado-Hernanz, P. M., Racault, M. F., Font-Muñoz, J. S., and Basterretxea, G.: Trends in phytoplankton phenology in the Mediterranean Sea based on ocean-colour remote sensing, Remote Sens. Environ., 221, 50–64, https://doi.org/10.1016/j.rse.2018.10.036, 2019.
Signorini, S. R., Franz, B. A., and McClain, C. R.: Chlorophyll variability in the oligotrophic gyres: mechanisms, seasonality and trends, Front. Mar. Sci., 2, 1, https://doi.org/10.3389/fmars.2015.00001, 2015.
Slomp, C. P.: Phosphorus Cycling in the Estuarine and Coastal Zones: Sources, Sinks, and Transformations, Treatise on Estuarine and Coastal Science, Biogeochemistry, 5, 201–229, https://doi.org/10.1016/B978-0-12-374711-2.00506-4, 2011.
Stoffelen, A.: Toward the true near-surface wind speed: Error modeling and calibration using triple collocation, J. Geophys. Res.-Oceans, 103, 7755–7766, https://doi.org/10.1029/97jc03180, 1998.
Stumpf, R. P.: Applications of satellite ocean color sensors for monitoring and predicting harmful algal blooms, Hum. Ecol. Risk Assess., 7, 1363–1368, https://doi.org/10.1080/20018091095050, 2001.
Sun, D., Pan, T., Wang, S., and Hu, C.: Linking phytoplankton absorption to community composition in Chinese marginal seas, Prog. Oceanogr., 192, 102517, https://doi.org/10.1016/j.pocean.2021.102517, 2021.
Takeda, S.: Influence of iron availability on nutrient consumption ratio of diatoms in oceanic waters, Nature, 393, 774–777, https://doi.org/10.1038/31674, 1998.
Urakubo, H., Bullmann, T., Kubota, Y., Oba, S., and Ishii, S.: UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images, Sci. Rep., 9, 19413, https://doi.org/10.1038/s41598-019-55431-0, 2019.
Wagle, N., Acharya, T. D., and Lee, D. H.: Comprehensive Review on Application of Machine Learning Algorithms for Water Quality Parameter Estimation Using Remote Sensing Data, Sensor. Mater., 32, 3879, https://doi.org/10.18494/SAM.2020.2953, 2020.
Wagner, F. H., Sanchez, A., Tarabalka, Y., Lotte, R. G., Ferreira, M. P., Aidar, M. P. M., Gloor, E., Phillips, O. L., and Aragao, L.: Using the U-net convolutional network to map forest types and disturbance in the Atlantic rainforest with very high resolution images, Remote Sensing in Ecology and Conservation, 5, 360–375, https://doi.org/10.1002/rse2.111, 2019.
Wang, B. L., Liu, C. Q., Maberly, S. C., Wang, F. S., and Hartmann, J.: Coupling of carbon and silicon geochemical cycles in rivers and lakes, Sci. Rep., 6, 35832, https://doi.org/10.1038/srep35832, 2016.
Wang, M., Jiang, L., Mikelsons, K., and Liu, X.: Satellite-derived global chlorophyll-
a anomaly products, Int. J. Appl. Earth Obs., 97, 102288, https://doi.org/10.1016/j.jag.2020.102288, 2021.
Wang, Y. Q. and Liu, D. Y.: Reconstruction of satellite chlorophyll-
a data using a modified DINEOF method: a case study in the Bohai and Yellow seas, China, Int. J. Remote Sens., 35, 204–217, https://doi.org/10.1080/01431161.2013.866290, 2014.
Xing, X. and Boss, E.: Chlorophyll-Based Model to Estimate Underwater Photosynthetically Available Radiation for Modeling, In-Situ, and Remote-Sensing Applications, Geophys. Res. Lett., 48, e2020GL092189, https://doi.org/10.1029/2020GL092189, 2021.
Yussof, F. N., Maan, N., and Reba, M. N. M.: LSTM Networks to Improve the Prediction of Harmful Algal Blooms in the West Coast of Sabah, Int. J. Environ. Res. Pub. He., 18, 7650, https://doi.org/10.3390/ijerph18147650, 2021.
Zhang, Q., Yuan, Q., Zeng, C., Li, X., and Wei, Y.: Missing Data Reconstruction in Remote Sensing Image With a Unified Spatial-Temporal-Spectral Deep Convolutional Neural Network, IEEE T. Geosci. Remote, 56, 4274–4288, https://doi.org/10.1109/tgrs.2018.2810208, 2018.
Zheng, G. and DiGiacomo, P. M.: Uncertainties and applications of satellite-derived coastal water quality products, Prog. Oceanogr., 159, 45–72, https://doi.org/10.1016/j.pocean.2017.08.007, 2017.
Zuo, H., Balmaseda, M. A., Tietsche, S., Mogensen, K., and Mayer, M.: The ECMWF operational ensemble reanalysis–analysis system for ocean and sea ice: a description of the system and assessment, Ocean Sci., 15, 779–808, https://doi.org/10.5194/os-15-779-2019, 2019.
Zweng, M. M., Reagan, J. R., Antonov, J. I., Locarnini, R. A., Mishonov, A. V., Boyer, T. P., Garcia, H. E., Baranova, O. K., Johnson, D. R., and D. Seidov, M. M. B.: World Ocean Atlas 2013: Volume 2, edited by: Salinity, S. L. and Mishonov, A., NOAA Atlas NESDIS 74, 39 pp., 2013.
