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

Lin, Dong; Qi, Jia; Yin, Baoshu; Zhi, Hai; Li, Delei; Yang, Shuguo; Wang, Wenwu; Chen, Hong; Xie, Bowen

doi:10.3390/rs14143494

Cited by 13 publications

(5 citation statements)

References 85 publications

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“…The LGBM model is a tree-based learning algorithm-based gradient boosting framework used for various applications, including ranking and classification problems [35,[64][65][66]. This model constructs a histogram-based segmentation approach instead of the conventional presorted traversal.…”

Section: ) Xgboostmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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Satellite-derived bathymetry (SDB) techniques are increasingly valuable for deriving high-quality bathymetric maps of coral reefs. Investigating the performance of the related SDB algorithms in purely spaceborne active–passive fusion bathymetry contributes to formulating reliable bathymetric strategies, particularly for areas such as the Spratly Islands, where in situ observations are exceptionally scarce. In this study, we took Anda Reef as a case study and evaluated the performance of eight common SDB approaches by integrating Sentinel-2 images with Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2). The bathymetric maps were generated using two classical and six machine-learning algorithms, which were then validated with measured sonar data. The results illustrated that all models accurately estimated the depth of coral reefs in the 0–20 m range. The classical algorithms (Lyzenga and Stumpf) exhibited a mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of less than 0.990 m, 1.386 m, and 11.173%, respectively. The machine learning algorithms generally outperformed the classical algorithms in accuracy and bathymetric detail, with a coefficient of determination (R2) ranging from 0.94 to 0.96 and an RMSE ranging from 1.034 m to 1.202 m. The multilayer perceptron (MLP) achieved the highest accuracy and consistency with an RMSE of as low as 1.034 m, followed by the k-nearest neighbor (KNN) (1.070 m). Our results provide a practical reference for selecting SDB algorithms to accurately obtain shallow water bathymetry in subsequent studies.

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Section: ) Xgboostmentioning

confidence: 99%

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Cheng,

Chu

et al. 2023

IJGI

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“…As depicted in Figure A3, in the vicinity of the northern region (22 • N, 116 • S), a pronounced and enduring enhancement of SWS is spatially concentrated, which contrasts with the behavior of other anomalies. This is related to seasonal variation in summer; that is, the enhanced penetration of Kuroshio water via the Luzon Strait [46][47][48].…”

Section: Seismic Thermal Anomaliesmentioning

confidence: 99%

Analysis of Ocean–Lithosphere–Atmosphere–Ionosphere Coupling Related to Two Strong Earthquakes Occurring in June–September 2022 on the Sea Coast of Philippines and Papua New Guinea

Xu,

Wang,

Chen

2023

Remote Sensing

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Scientific progress in the context of seismic precursors reveals a systematic mechanism, namely lithosphere–atmosphere–ionosphere coupling (LAIC), to elaborate the underlying physical processes related to earthquake preparation phases. In this study, a comprehensive analysis was conducted for two earthquakes that occurred on the sea coast through tidal force fluctuation to investigate ocean–lithosphere–atmosphere–ionosphere coupling (OLAIC), based on oceanic parameters (i.e., sea potential temperature and seawater salinity), air temperature and electron density profiles. The interrupted enhancement and diffusion process of thermal anomalies indicate that the intensity of seismic anomalies in the atmosphere is affected by the extent of land near the epicenter. By observing the evolution of the ocean interior, we found that the deep water was lifted and formed upwelling, which then diffused along the direction of plate boundaries with an “intensification-peak-weakening” trend under the action of the accelerated subduction of tectonic plates. Furthermore, the analysis shows that the seismic anomalies have two propagation paths: (i) along active faults, with the surface temperature rising as the initial performance, then the air pressure gradient being generated, and finally the ionosphere being disturbed; (ii) along plate boundaries, upwelling, which is the initial manifestation, leading to changes in the parameters of the upper ocean. The results presented in this study can contribute to understanding the intrinsic characteristics of OLAIC.

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“…To tackle the limitations of small data and complex computations, we adopt the LGBM algorithm to predict the temperature by taking advantage of its lightweight. LGBM is a gradient boosting framework based on decision trees, which has been well used in the marine field and shown a faster training speed and higher accuracy for small data (Su et al, 2021;Dong et al, 2022). Same as the other boosting algorithms, it sums the results of multiple decision trees as the final prediction output.…”

Section: Light Gradient Boosting Machinementioning

confidence: 99%

An offshore subsurface thermal structure inversion method by coupling ensemble learning and tide model for the South Yellow Sea

Sun

et al. 2022

Front. Mar. Sci.

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The South Yellow Sea Cold Water Mass (SYSCWM), which occurs in the South Yellow Sea (SYS) during summer, significantly impacts the hydrological characteristics and marine ecosystems but lacks fine interior data. With satellite observations, significant achievements have been made in reconstructing high-resolution ocean subsurface thermohaline structure based on machine learning. However, the accuracy of offshore subsurface parameter estimation will be affected due to the macro-tidal environment and fewer in situ observations. In this paper, we coupled the TPXO tide model and Light Gradient Boosting Machine algorithm to develop an inversion model of offshore subsurface thermal structure for the SYS using sea surface data and in situ observations. After light modelling, the subsurface temperature structure in the SYS is retrieved from sea surface parameters with a spatial resolution of 0.25° at depths of 0-55 m. Observation-based dataset (ARMOR3D) and in situ observations are used for model evaluation. According to the validation of the mooring buoy observations, the overall coefficient of determination (R2), which determines the percentage of variance in the dependent variable that can be explained by the independent variable, is more than 0.95. Furthermore, the R2 is improved by 12% due to coupling tide model below the thermocline during the maturity stage of SYSCWM, which is helpful for a better reconstruction of SYSCWM. Comparing with the cruise data, the average R2 of the proposed model is 0.927 which is slightly better than the accuracy of the observation-based ARMOR3D dataset. Since the R2 exceeds 0.8 in the most area of 121°E~123.5°E, 33°N~36°N, the reconstruction is reliable in this area. The method provides a new explorable direction for reconstructing the ocean thermal structure in offshore areas.

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

Cited by 13 publications

References 85 publications

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

A Comprehensive Evaluation of Machine Learning and Classical Approaches for Spaceborne Active-Passive Fusion Bathymetry of Coral Reefs

Analysis of Ocean–Lithosphere–Atmosphere–Ionosphere Coupling Related to Two Strong Earthquakes Occurring in June–September 2022 on the Sea Coast of Philippines and Papua New Guinea

An offshore subsurface thermal structure inversion method by coupling ensemble learning and tide model for the South Yellow Sea

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