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2 results on '"ocean subsurface salinity structure"'

1. Reconstruction of Subsurface Salinity Structure in the South China Sea Using Satellite Observations: A LightGBM-Based Deep Forest Method.

Author

Dong, Lin, Qi, Jifeng, Yin, Baoshu, Zhi, Hai, Li, Delei, Yang, Shuguo, Wang, Wenwu, Cai, Hong, and Xie, Bowen

Subjects
*STANDARD deviations, *OCEAN temperature, *SALINITY, *WIND speed, *WATER depth
Abstract

Accurately estimating the ocean's interior structures using sea surface data is of vital importance for understanding the complexities of dynamic ocean processes. In this study, we proposed an advanced machine-learning method, the Light Gradient Boosting Machine (LightGBM)-based Deep Forest (LGB-DF) method, to estimate the ocean subsurface salinity structure (OSSS) in the South China Sea (SCS) by using sea surface data from multiple satellite observations. We selected sea surface salinity (SSS), sea surface temperature (SST), sea surface height (SSH), sea surface wind (SSW, decomposed into eastward wind speed (USSW) and northward wind speed (VSSW) components), and the geographical information (including longitude and latitude) as input data to estimate OSSS in the SCS. Argo data were used to train and validate the LGB-DF model. The model performance was evaluated using root mean square error (RMSE), normalized root mean square error (NRMSE), and determination coefficient (R2). The results showed that the LGB-DF model had a good performance and outperformed the traditional LightGBM model in the estimation of OSSS. The proposed LGB-DF model using sea surface data by SSS/SST/SSH and SSS/SST/SSH/SSW performed less satisfactorily than when considering the contribution of the wind speed and geographical information, indicating that these are important parameters for accurately estimating OSSS. The performance of the LGB-DF model was found to vary with season and water depth. Better estimation accuracy was obtained in winter and autumn, which was due to weaker stratification. This method provided important technical support for estimating the OSSS from satellite-derived sea surface data, which offers a novel insight into oceanic observations. [ABSTRACT FROM AUTHOR]

Published
2022
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2. Remote Sensing Applications in Ocean Observation.

Author

Ho, Chung-Ru, Ho, Chung-Ru, Li, Xiaofeng, and Liu, Antony K.

Subjects
Research & information: general, 3D-C BAM, AI explanation, Arabian Gulf, Bayesian algorithm, CFOSAT, CNN, ConvLSTM, Daya Bay Nuclear Power Plants, East China Sea, Forel-Ule Scale, GDPS, Gulf of Oman, HYCOM reanalysis results, Himawari-8, Kuroshio Current Loop, Kuroshio branch, Kuroshio intrusion, Landsat, MODIS, New South Wales, North Pacific Subtropical High, North Pacific subtropical gyre, OLCI, SAR, SAR images, SST, Sargassum, SeaDAS, Sentinel-1, South China Sea, Super Typhoon Goni, Taiwan, Taiwan Strait, Yellow Sea, Yellow Sea coastal current, Zostera marina L., aerosols, algal blooms, alongshore current, atmospheric correction, backward scattering intensity, baroclinic instability, barotropic instability, bathymetry, bias correction, bibliometric analysis, chlorophyll-a, chlorophyll-a bloom, cloud masking, coastal upwelling, coastal waters of Myanmar, cold-core anticyclonic eddy, data fusion, deep learning, drifter, ebb tide, energy cascade, energy flux, environmental change, fish assemblage, flood tide, flow pattern, geostationary ocean color imager (GOCI), gravest empirical modes, gulf of Mexico, high-frequency radar, in situ observation, inherent optical properties, internal solitary waves, internal tides, lidar, long-term changes, loop current rings, machine learning, mapping, marine heatwaves, mesoscale eddies, meteorological data, modal structure, monsoon, n/a, noise equivalent reflectance difference, nonlinearity, normalized water-leaving radiance, northwestern Pacific Ocean, ocean Scheimpflug lidar, ocean color, ocean subsurface salinity structure, offshore detection, oil detection, oil slicks, plane wave fit method, power plant installed capacity, quantitative mapping, radiation sensitivity, radiative transfer equation, reclamation, remote equatorial forcing, remote sensing, remote sensing sensors, salinity, satellite observation, satellite observations, satellite remote sensing, satellite remote sensing data, scatterometer, sea ice, sea level anomaly, sea surface height, sea surface temperature, sea surface temperatures, seagrass, semidiurnal internal tides, shelf circulation, ship detection, spatial analysis, spatial and temporal changes, spatiotemporal variation, spectral variability, split-window algorithm, summer 2021, temperature, the East Australian Current, the Indonesian Seas, the Sulu-Sulawesi Seas, thermal discharge, three-dimensional eddy reconstruction, tide, topographic position index, total suspended sediment, trophic state, turbid water, turbulence, turbulent mixing, typhoon, upper ocean response, upwelling, upwelling index, volume scattering function, wake detection, water type taxonomies, westerly jet, wind field
Abstract

Summary: Since the launch of Seasat, TIROS-N, and Nimbus-7 satellites equipped with ocean observation sensors in 1978, a new era of ocean remote sensing has opened. Today, remotely sensed data have been widely used in oceanographic studies. This reprint collects various advanced ocean remote sensing technologies and their applications, including the use of artificial intelligence techniques to explore ocean information and bibliometric analysis to assess researchers and trends in this scientific field. The observations of various sensors enrich the application of ocean environment monitoring and ocean dynamical analysis. If you are interested in understanding the application of ocean remote sensing, this monograph should be very helpful.

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