Bommarito, J. J.: DMSP special sensor microwave imager sounder (SSM/IS), P. Soc. Photo.-Opt. Ins., 1935, 230–238, https://doi.org/10.1117/12.161076, 1993.
Bourassa, M. A., Gille, S. T., Bitz, C., Carlson, D., Cerovecki, I., Clayson, C. A., Cronin, M. F., Drennan, W. M., Fairall, C. W., and Hoffman, R. N.: High-latitude ocean and sea ice surface fluxes: Challenges for climate research, B. Am. Meteorol. Soc., 94, 403–423, 2013.
Bourlès, B., Lumpkin, R., McPhaden, M. J., Hernandez, F., Nobre, P., Campos, E., Yu, L., Planton, S., Busalacchi, A., and Moura, A. D.: The PIRATA program: History, accomplishments, and future directions, B. Am. Meteorol. Soc., 89, 1111–1126, 2008.
Cayan, D. R.: Latent and sensible heat flux anomalies over the northern oceans: Driving the sea surface temperature, J. Phys. Oceanogr., 22, 859–881, 1992.
Chen, C. and Wang, Q.: Latent Heat Flux Trend and Its Seasonal Dependence over the East China Sea Kuroshio Region, J. Mar. Sci. Eng., 12, 722, https://doi.org/10.3390/jmse12050722, 2024.
Chou, S.-H., Atlas, R. M., Shie, C.-L., and Ardizzone, J.: Estimates of surface humidity and latent heat fluxes over oceans from SSM/I data, Mon. Weather Rev., 123, 2405–2425, 1995.
Chou, S. H., Shie, C. L., Atlas, R. M., and Ardizzone, J.: Air-sea fluxes retrieved from Special Sensor Microwave Imager data, J. Geophys. Res.-Oceans, 102, 12706–12726, 1997.
Chou, S.-H., Nelkin, E., Ardizzone, J., Atlas, R. M., and Shie, C.-L.: Surface turbulent heat and momentum fluxes over global oceans based on the Goddard satellite retrievals, version 2 (GSSTF2), J. Climate, 16, 3256–3273, 2003.
Clayson, C. and Brown, J.: NOAA climate data record ocean surface bundle (OSB) climate data record (CDR) of ocean heat fluxes, version 2, Clim. Algorithm Theor. Basis Doc. C-ATBD Asheville NC NOAA Natl. Cent. Environ. Inf. Doi, 10, V59K4885, https://doi.org/10.7289/V59K4885, 2016.
Esbensen, S., Chelton, D., Vickers, D., and Sun, J.: An analysis of errors in Special Sensor Microwave Imager evaporation estimates over the global oceans, J. Geophys. Res.-Oceans, 98, 7081–7101, 1993.
Fairall, C., Bradley, E., Rogers, D., Edson, J., and Young, G.: The TOGA COARE bulk flux algorithm, J. Geophys. Res., 101, 3747–3764, 1996a.
Fairall, C., Bradley, E., Godfrey, J., Wick, G., Edson, J., and Young, G.: The cool skin and the warm layer in bulk flux calculations, J. Geophys. Res., 101, 1295–1308, 1996b.
Fairall, C. W., Bradley, E. F., Hare, J., Grachev, A. A., and Edson, J. B.: Bulk parameterization of air–sea fluxes: Updates and verification for the COARE algorithm, J. Climate, 16, 571–591, 2003.
Fennig, K., Schröder, M., Andersson, A., and Hollmann, R.: A Fundamental Climate Data Record of SMMR, SSM/I, and SSMIS brightness temperatures, Earth Syst. Sci. Data, 12, 647–681, https://doi.org/10.5194/essd-12-647-2020, 2020.
Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Schepers, D.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, 2020.
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., and Rozum, I.: ERA5 hourly data on single levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS)[data set], https://doi.org/10.24381/cds.adbb2d47, 2023.
Huang, B., Liu, C., Banzon, V., Freeman, E., Graham, G., Hankins, W., Smith, T., and Zhang, H. M.: Improvements of the Daily Optimum Interpolation Sea Surface Temperature (DOISST), J. Climate, 34, 2923–2939, 2021.
Jones, C., Peterson, P., and Gautier, C.: A new method for deriving ocean surface specific humidity and air temperature: An artificial neural network approach, J. Appl. Meteorol., 38, 1229–1245, 1999.
Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., and Woollen, J.: The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437–471, https://doi.org/10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2, 1996.
Kubota, M., Iwasaka, N., Kizu, S., Konda, M., and Kutsuwada, K.: Japanese ocean flux data sets with use of remote sensing observations (J-OFURO), J. Oceanogr., 58, 213–225, 2002.
Large, W. and Pond, S.: Sensible and latent heat flux measurements over the ocean, J. Phys. Oceanogr., 12, 464–482, 1982.
Liu, W. T.: Statistical relation between monthly mean precipitable water and surface-level humidity over global oceans, Mon. Weather Rev., 114, 1591–1602, 1986.
McPhaden, M. J., Busalacchi, A. J., Cheney, R., Donguy, J. R., Gage, K. S., Halpern, D., Ji, M., Julian, P., Meyers, G., and Mitchum, G. T.: The Tropical Ocean-Global Atmosphere observing system: A decade of progress, J. Geophys. Res.-Oceans, 103, 14169–14240, 1998.
Meng, L., He, Y., Chen, J., and Wu, Y.: Neural network retrieval of ocean surface parameters from SSM/I data, Mon. Weather Rev., 135, 586–597, 2007.
Roberts, J. B., Clayson, C. A., Robertson, F. R., and Jackson, D. L.: Predicting near-surface atmospheric variables from Special Sensor Microwave/Imager using neural networks with a first-guess approach, J. Geophys. Res.-Atmos., 115, D19102, https://doi.org/10.1029/2010JD014148, 2010.
RSS: Satellite equatorial crossing times (SSM/I/SSMIS), Remote Sensing Systems,
https://www.remss.com/support/crossing-times/, last access: 23 April 2026a.
RSS: SSM/I and SSMIS data products (Mission documentation and citation guidance), Remote Sensing Systems,
https://www.remss.com/missions/ssmi/, last access: 23 April 2026b.
Schulz, J., Meywerk, J., Ewald, S., and Schlüssel, P.: Evaluation of satellite-derived latent heat fluxes, J. Climate, 10, 2782–2795, 1997.
Simonot, J. R. and Gautier, C.: Satellite estimates of surface evaporation in the Indian Ocean during the 1979 monsoon, Ocean-Air Interactions, 1, 239–256, 1989.
Tang, R., Wang, Y., Jiang, Y., Liu, M., Peng, Z., Hu, Y., Huang, L., and Li, Z.-L.: A review of global products of air-sea turbulent heat flux: accuracy, mean, variability, and trend, Earth-Sci. Rev., 249, 104662, https://doi.org/10.1016/j.earscirev.2024.104662, 2024.
Tomita, H. and Kubota, M.: An analysis of the accuracy of Japanese Ocean Flux data sets with Use of Remote sensing Observations (J-OFURO) satellite-derived latent heat flux using moored buoy data, J. Geophys. Res.-Oceans, 111, C07007, https://doi.org/10.1029/2005JC003298, 2006.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., and Polosukhin, I.: Attention is all you need, Adv. Neur. In., 30, 5998–6008, 2017.
Wang, H. and Li, X.: DeepBlue: Advanced convolutional neural network applications for ocean remote sensing, IEEE Geoscience and Remote Sensing Magazine, 12, 138–161, 2023.
Wang, H. and Li, X.: Expanding Horizons: U-Net enhancements for semantic segmentation, forecasting, and super-resolution in ocean remote sensing, J. Remote Sens., 4, 0196, https://doi.org/10.34133/remotesensing.0196, 2024.
Wang, H., Hu, S., and Li, X.: An interpretable deep learning ENSO forecasting model, Ocean-Land-Atmosphere Research, 2, 0012, https://doi.org/10.34133/olar.0012, 2023.
Wang, H., Hu, S., Guan, C., and Li, X.: The role of sea surface salinity in ENSO forecasting in the 21st century, npj Climate and Atmospheric Science, 7, 206, https://doi.org/10.1038/s41612-024-00763-6, 2024.
Wang, H., Zhou, Y., and Li, X.: GDCM: Generalized data completion model for satellite observations, Remote Sens. Environ., 324, 114760, https://doi.org/10.1016/j.rse.2025.114760, 2025a.
Wang, H., Wang, M., and Li, X.: DeepFlux v1.0: A Global Open Oceans Daily Heat Flux Dataset For 1992–2020 From SSMI Satellite Data Using Deep Learning Models, IOCAS [code, data set], https://doi.org/10.12157/IOCAS.20250823.001, 2025b.
Wang, M., Wang, H., and Li, X.: Enhancing Retrievals of Air-Sea Heat Fluxes from AMSR2 Microwave Observations Based on Deep Learning, IEEE T. Geosci. Remote, 63, 1–17, https://doi.org/10.1109/TGRS.2025.3586604, 2025c.
Wentz, F. J.: SSM/I version-7 calibration report, Remote Sensing Systems Tech. 11012.46, 46 pp., https://doi.org/10.56236/RSS-av, 2013.
Woodruff, S. D., Diaz, H., Elms, J., and Worley, S.: COADS Release 2 data and metadata enhancements for improvements of marine surface flux fields, Phys. Chem. Earth, 23, 517–526, 1998.
Yang, C., Leonelli, F. E., Marullo, S., Artale, V., Beggs, H., Buongiorno Nardelli, B., Chin, T. M., De Toma, V., Good, S., Huang, B., Merchant, C. J., Sakurai, T., Santoleri, R., Vazquez-Cuervo, J., Zhang, H.-M., and Pisano, A.: Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S), J. Climate, 34, 5257–5283, 2021.
Yu, L.: Multidecade Global Flux Datasets from the Objectively Analyzed Air-sea Fluxes (OAFlux) Project: Latent and sensible heat fluxes, ocean evaporation, and related surface meteorological variables, OAFlux Project Tech. Rep. OA-2008-01, Woods Hole Oceanographic Institution, Woods Hole, 64 pp.,
https://oaflux.whoi.edu/wp-content/uploads/sites/31/2020/06/OAFlux_TechReport_3rd_release.pdf (last access: 23 April 2026), 2008.
Yu, L. and Weller, R. A.: Objectively analyzed air–sea heat fluxes for the global ice-free oceans (1981–2005), B. Am. Meteorol. Soc., 88, 527–540, 2007.
Yu, L., Weller, R. A., and Sun, B.: Improving latent and sensible heat flux estimates for the Atlantic Ocean (1988–99) by a synthesis approach, J. Climate, 17, 373–393, 2004.
Zhang, G. J. and McPhaden, M. J.: The relationship between sea surface temperature and latent heat flux in the equatorial Pacific, J. Climate, 8, 589–605, 1995.
Zhang, X. and Li, X.: Constructing a 22-year internal wave dataset for the northern South China Sea: spatiotemporal analysis using MODIS imagery and deep learning, Earth Syst. Sci. Data, 16, 5131–5144, https://doi.org/10.5194/essd-16-5131-2024, 2024.
Zhou, X., Ray, P., Boykin, K., Barrett, B. S., and Hsu, P.-C.: Evaluation of surface radiative fluxes over the tropical oceans in AMIP simulations, Atmosphere, 10, 606, https://doi.org/10.3390/atmos10100606, 2019.
Zhou, X., Ray, P., Barrett, B. S., and Hsu, P.-C.: Understanding the bias in surface latent and sensible heat fluxes in contemporary AGCMs over tropical oceans, Clim. Dynam., 55, 2957–2978, 2020.
