Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Yang, Lianjun; Jin, Taoyong; Gao, Xianwen; Wen, Hao; Schöne, Tilo; Ming-yu, Xiao; Huang, Hailan

doi:10.3390/rs13050908

Cited by 8 publications

(3 citation statements)

References 40 publications

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“…In addition, the Arctic and Tibetan plateaus are hot issues, which primarily involve the study of sea ice, snow, glaciers, and Tibetan lakes and adopt the data of CryoSat-2 and ICESat-2 [124][125][126][127][128]. Finally, we detected a surging of diverse deep learning topics covering nearly all possibilities of satellite altimetry, such as SWH prediction, SSH reconstruction, eddy detection, 3D ocean state estimation, eddy heat flux estimation, and surface current derivation [129][130][131][132][133][134][135][136][137]. The majority of current research trends, according to the analysis, are related to innovative altimetry technologies (such as SAR altimetry and interferometer altimetry) and cross-disciplines (such as the computer and information science of deep learning technology).…”

Section: Research Trend and Frontmentioning

confidence: 97%

Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis

Yang

Lin

Fan³

et al. 2022

Remote Sensing

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Scientometric reviews, facilitated by computational and visual analytical approaches, allow researchers to gain a thorough understanding of research trends and areas of concentration from a large number of publications. With the fast development of satellite altimetry, which has been effectively applied to a wide range of research topics, it is timely to summarize the scientific achievements of the previous 50 years and identify future trends in this field. A comprehensive overview of satellite altimetry was presented using a total of 8541 publications from the Web of Science Core Collection covering the years from 1970 to 2021. We begin by presenting the fundamental statistical results of the publications, such as the annual number of papers, study categories, countries/regions, afflictions, journals, authors, and keywords, in order to provide a comprehensive picture of satellite altimetry research. We discuss the co-occurrence of the authors in order to reveal the global collaboration network of satellite altimetry research. Finally, we utilised co-citation networks to detect the development trend and associated crucial publications for various specific topics. The findings show that satellite altimetry research has been changed immensely during the last half-century. The United States, France, China, England, and Germany made the most significant contributions in the field of satellite altimetry. The analysis reveals a clear link between technology advancements and the trend in satellite altimetry research. As a result, wide swath altimetry, GNSS-reflectometry, laser altimetry, terrestrial hydrology, and deep learning are among the most frontier study subjects. The findings of this work could guide a thorough understanding of satellite altimetry’s overall development and research front.

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Section: Research Trend and Frontmentioning

confidence: 97%

Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis

Yang

Lin

Fan³

et al. 2022

Remote Sensing

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“…This result is expected because the satellite altimetry is not affected by the underlying geodynamical movements due to the vertical land motion affecting the TG measurements. Because satellite altimeters have a limited ability to measure sea level height in coastal regions where the TGs are installed, the SSH measurements are not affected by the VLM [87]. Moreover, we can compare the estimation of the SLR with either the SSH or the ASLR (cf.…”

Section: Absolute Slr Along the Pacific Coast Of The Usa Using Ssh An...mentioning

confidence: 99%

Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island

Montillet

Fernandes

et al. 2022

Remote Sensing

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Previous studies have estimated the sea level rise (SLR) at various locations on the west coast of the USA and Vancouver Island in Canada. Here, we construct an entire SLR profile from Vancouver Island in the Pacific Northwest to San Diego in Southern California. First, we process global navigation satellite system (GNSS) measurements at 405 stations blanketing the whole coast to generate a profile of vertical land motion (VLM) known to bias century-long tide gauge (TG) measurements recording relative SLR (RSLR). We are then able to estimate the absolute SLR (ASLR) by correcting the SLR with the VLM. Our study emphasizes the relationship between the various tectonic movements (i.e., the Cascadia subduction zone, the San Andreas strike-slip fault system) along the Pacific coast which renders it difficult to accurately estimate the SLR. That is why we precisely model the stochastic noise of both GNSS and tide gauge time series using a combination of various models and information criterions (ICs). We also use the latest altimetry products and sea surface height (SSH) to compare it with ASLR at the same location as the TGs. This study supports previous analysis that the power law + white noise and generalized Gauss–Markov + white noise models are the best stochastic noise models for the GNSS time series. The new coastal profile confirms the large variability of VLM estimates in the Pacific Northwest around the Cascadia subduction zone in agreement with previous studies, and a similar result when the San Andreas fault comes onshore in Central California (San Francisco Bay). Negative RSLR values are mostly located in the Pacific Northwest (Vancouver Island and Olympic Peninsula). We also observe a much bigger variation (about 90%–150%) of the ASLR in the Pacific Northwest which is predominantly due to glacial isostatic adjustment (GIA). Moreover, the comparison between the ASLR and the SSH estimates shows similarities in the center of the studied area (South Washington, Oregon planes, and some parts of Southern California) where the tectonic activity does not significantly influence the TG measurements. Finally, the twentieth-century satellite geocentric ocean height rates show a global mean of 1.5 to 1.9 mm/yr. Our estimates based on ASLR and SSH are within this interval.

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“…It was observed that 16% of the total coastline (87 km) was highly vulnerable. Finally, Yang, et al [250] developed a fusion approach based on deep belief network to integrate satellite altimetry and tide gauge data. The results revealed that the proposed method performed well when limited along-track altimetry and gauge data are available.…”

Section: Altimetermentioning

confidence: 99%

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

Amani¹,

Moghimi

Mirmazloumi

et al. 2022

Water

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Oceans cover over 70% of the Earth’s surface and provide numerous services to humans and the environment. Therefore, it is crucial to monitor these valuable assets using advanced technologies. In this regard, Remote Sensing (RS) provides a great opportunity to study different oceanographic parameters using archived consistent multitemporal datasets in a cost-efficient approach. So far, various types of RS techniques have been developed and utilized for different oceanographic applications. In this study, 15 applications of RS in the ocean using different RS techniques and systems are comprehensively reviewed and discussed. This study is divided into two parts to supply more detailed information about each application. The first part briefly discusses 12 different RS systems that are often employed for ocean studies. Then, six applications of these systems in the ocean, including Ocean Surface Wind (OSW), Ocean Surface Current (OSC), Ocean Wave Height (OWH), Sea Level (SL), Ocean Tide (OT), and Ship Detection (SD), are provided. For each application, the applicable RS systems, their advantages and disadvantages, various RS and Machine Learning (ML) techniques, and several case studies are discussed. The other nine applications, including Iceberg, Sea Ice (SI), Sea Surface temperature (SST), Ocean Surface Salinity (OSS), Ocean Color (OC), Ocean Chlorophyll (OCh), Ocean Oil Spill (OOS), Underwater Ocean, and Fishery, are provided in Part II of this study.

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Sea Level Fusion of Satellite Altimetry and Tide Gauge Data by Deep Learning in the Mediterranean Sea

Cited by 8 publications

References 40 publications

Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis

Satellite Altimetry: Achievements and Future Trends by a Scientometrics Analysis

Sea Level Rise Estimation on the Pacific Coast from Southern California to Vancouver Island

Ocean Remote Sensing Techniques and Applications: A Review (Part I)

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