Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images

Zhang, Xudong; Li, Xiaofeng; Zhang, Tao

doi:10.1007/s00343-020-0046-1

Cited by 29 publications

(14 citation statements)

References 24 publications

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“…Enlargements of the MS f amplitude and phase around the Sulu Archipelago are shown in Figure 5 (region indicated in Figure 1). The largest amplitudes are found in the southern Sulu Sea (on the north side of the archipelago), in a region known for its large-amplitude tidal internal waves (Apel et al, 1985;Zhang et al, 2020;Zhao et al, 2021). Towards the western end of this feature, the amplitude of MS f is about 1/4 as large as the combined mean, S a , and S sa Chl-a, so it represents a significant fortnightly modulation of the Chl-a concentration.…”

Section: Resultsmentioning

confidence: 99%

Tidal Variability of Chl-a in the Indonesian Seas

Zaron

Capuano

Koch-Larrouy

2022

Preprint

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Abstract. Harmonic analysis of time series from 20 years of MODIS-Aqua ocean color observations (2002–2022) is conducted to identify periodic variability of near-surface chlorophyll (Chl-a) inferred from ocean color. As they are based on satellite imagery, the Chl-a observations are characterized by significant gaps in both spatial and temporal coverage due to the masking of clouds in the images. Results yield a coherent picture of surface Chl-a associated with the time mean, annual and semiannual cycles, and spring-neap tidal variability. Spring-neap variability is heterogeneous and associated with regions of significant baroclinic tides as well as coastal regions with strong tidal currents. The observations provide another line of evidence for the significant contribution of ocean tides to mixing in the Indonesian Seas.

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Section: Resultsmentioning

confidence: 99%

Tidal Variability of Chl-a in the Indonesian Seas

Zaron

Capuano

Koch-Larrouy

2022

Preprint

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“…Both SAR and visible spectral remote sensing imaging have become effective tools in the study of these waves. Satellite images can be used to obtain the distribution, generation, propagation (reflection, refraction), and other characteristics of waves in different regions (e.g., [21][22][23][24]. Amplitude inversion of internal waves from satellite images is mostly based on the Korteweg-de Vries (KdV) equation and half-wavelength characteristics of internal waves [25].…”

Section: Internal Wavesmentioning

confidence: 99%

Recent Developments in Artificial Intelligence in Oceanography

Han

Bethel

et al. 2022

Ocean-Land-Atmos Res

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With the availability of petabytes of oceanographic observations and numerical model simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of applications. In this paper, these applications are reviewed from the perspectives of identifying, forecasting, and parameterizing ocean phenomena. Specifically, the usage of AI algorithms for the identification of mesoscale eddies, internal waves, oil spills, sea ice, and marine algae are discussed in this paper. Additionally, AI-based forecasting of surface waves, the El Niño Southern Oscillation, and storm surges is discussed. This is followed by a discussion on the usage of these schemes to parameterize oceanic turbulence and atmospheric moist physics. Moreover, physics-informed deep learning and neural networks are discussed within an oceanographic context, and further applications with ocean digital twins and physics-constrained AI algorithms are described. This review is meant to introduce beginners and experts in the marine sciences to AI methodologies and stimulate future research toward the usage of causality-adherent physics-informed neural networks and Fourier neural networks in oceanography.

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“…(2021) proposed an integrated algorithm for the detection and recognition of oceanic IWs in SAR images based on support vector machine (SVM). X. Zhang, Li, and Zhang (2020), X. Zhang, Zhang, and Li (2020), X. Zhang et al. (2021) analyzed the IWs in Sulu‐Celebes Sea and Andaman Sea (AS) and proposed a ML based model to predict the propagation of IWs in these areas (X. Zhang, Zhang, & Li, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Efficiently and comprehensively observing the ocean surface by remote sensing imagery is becoming an increasingly available tool. In particular, satellite imagery is widely used to analyze IWs at present because of its large‐scale coverage and high spatial resolution (Hu et al., 2021; X. Zhang, Li, & Zhang, 2020; X. Zhang, Zhang, & Li, 2020). Synthetic Aperture Radar (SAR) can observe IWs and acquire data at all weather day‐and‐night conditions without being affected by the environment.…”

Section: Introductionmentioning

confidence: 99%

An Internal Waves Data Set From Sentinel‐1 Synthetic Aperture Radar Imagery and Preliminary Detection

Tao

Guo³

et al. 2022

Earth and Space Science

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Internal waves (IWs) are a widely occurring phenomenon in the global oceans. They have significant impacts on marine environments through their propagation mechanisms. In this study, we collect 390 satellite images with 775 labels from Sentinel‐1 Synthetic Aperture Radar (SAR) from 2014 to 2021 to construct a data set containing IW packets in Andaman Sea, South China Sea, Sulu Sea, and Celebes Sea, respectively. All the SAR images acquired are pre‐processed to provide clear IW packets for better visualization. To better detect IWs in global oceans automatically, a machine‐learning based model is proposed to focus on different channels and the spatial information. The precision, recall, and mean average precision of our improved model applied on the IW data set is up to 98.7%, 96.9%, and 98.9%, respectively. Various cases of IWs images are analyzed to illustrate detection quality in different stripes, scales, and propagation directions of IWs. The experimental results indicate that our data set is helpful to better detect IWs, and proposed network can accurately detect IWs in SAR images under various conditions.

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Characteristics and generations of internal wave in the Sulu Sea inferred from optical satellite images

Cited by 29 publications

References 24 publications

Tidal Variability of Chl-a in the Indonesian Seas

Tidal Variability of Chl-a in the Indonesian Seas

Recent Developments in Artificial Intelligence in Oceanography

An Internal Waves Data Set From Sentinel‐1 Synthetic Aperture Radar Imagery and Preliminary Detection

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