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

Amani, Meisam; Moghimi, Armin; Mirmazloumi, S. Mohammad; Ranjgar, Babak; Ghorbanian, Arsalan; Ojaghi, Saeid; Ebrahimy, Hamid; Naboureh, Amin; Nazari, Mohsen Eslami; Mahdavi, Sahel; Moghaddam, Sayyed Hamed Alizadeh; Asiyabi, Reza Mohammadi; Ahmadi, Seyed Ali; Mehravar, Saeed; Mohseni, Farzane; Jin, Shuanggen

doi:10.3390/w14213400

Cited by 19 publications

(19 citation statements)

References 325 publications

(525 reference statements)

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“…Further combined with passive ocean color remote sensing and in situ biogeochemical buoys (Biogeochemical Argo, BGC-ARGO) and other observation means, it is expected to achieve the threedimensional detection and high-precision inversion of bio-optical and physical parameters in four-dimensional space time within the ~100-m depth of the global ocean for the first time (Chen et al, 2021b). For a more intuitive understanding of the abovementioned spaceborne sensor, the characteristics of these satellite sensors are collected and summarized in Table 1 (Amani et al, 2021;Amani et al, 2022a;Amani et al, 2022b;Amani et al, 2022c).…”

Section: Satellite Remote Sensing In the ~100-m Oceanmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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Oceans at a depth ranging from ~100 to ~1000-m (defined as the intermediate water here), though poorly understood compared to the sea surface, is a critical layer of the Earth system where many important oceanographic processes take place. Advances in ocean observation and computer technology have allowed ocean science to enter the era of big data (to be precise, big data for the surface layer, small data for the bottom layer, and the intermediate layer sits in between) and greatly promoted our understanding of near-surface ocean phenomena. During the past few decades, however, the intermediate ocean is also undergoing profound changes because of global warming, the research and prediction of which are of intensive concern. Due to the lack of three-dimensional ocean theories and field observations, how to remotely sense the intermediate ocean from space becomes a very attractive but challenging scientific issue. With the rapid development of the next generation of information technology, artificial intelligence (AI) has built a new bridge from data science to marine science (called Deep Blue AI, DBAI), which acts as a powerful weapon to extend the paradigm of modern oceanography in the era of the metaverse. This review first introduces the basic prior knowledge of water movement in the ~100 m ocean and vertical stratification within the ~1000-m depths as well as the data resources provided by satellite remote sensing, field observation, and model reanalysis for DBAI. Then, three universal DBAI methodologies, namely, associative statistical, physically informed, and mathematically driven neural networks, are elucidated in the context of intermediate ocean remote sensing. Finally, the unique advantages and potentials of DBAI in data mining and knowledge discovery are demonstrated in a top-down way of “surface-to-interior” via several typical examples in physical and biological oceanography.

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Section: Satellite Remote Sensing In the ~100-m Oceanmentioning

confidence: 99%

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

et al. 2023

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“…Mapping the ocean surface is commonly performed using marine radar systems (e.g., in [28]; more examples are reviewed in [29]). Such systems are relatively expensive and have considerable power consumption (required for RF chirp transmission).…”

Section: Our Contributionmentioning

confidence: 99%

Real-Time Stereo-Based Ocean Surface Mapping for Robotic Floating Platforms: Concept and Methodology

Greenberg

Ben‐Moshe

2023

Sensors

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Consider the case of a small, unmanned boat that is performing an autonomous mission. Naturally, such a platform might need to approximate the ocean surface of its surroundings in real-time. Much like obstacle mapping in autonomous (off-road) rovers, an approximation of the ocean surface in a vessel’s surroundings in real-time can be used for improved control and optimized route planning. Unfortunately, such an approximation seems to require either expensive and heavy sensors or external logistics that are mostly not available for small or low-cost vessels. In this paper, we present a real-time method for detecting and tracking ocean waves around a floating object that is based on stereo vision sensors. Based on a large set of experiments, we conclude that the presented method allows reliable, real-time, and cost-effective ocean surface mapping suitable for small autonomous boats.

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“…Sea surface temperature (SST) plays a pivotal role within the global climate system. Monitoring and analyzing variations in SST enable a better comprehension of climate change trends and patterns, subsequently facilitating predictions of future climate shifts [1][2][3]. However, due to the limited distribution of observation stations, data scarcity is prevalent in certain regions.…”

Section: Introductionmentioning

confidence: 99%

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

Wang,

Shi,

et al. 2024

J. Phys.: Conf. Ser.

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Addressing the issue of data sparsity and gaps caused by missing values, this study employs an information diffusion approach to effectively spread information from sparse sample points to monitoring locations. By thoroughly extracting insights from a limited dataset, it achieves more precise interpolation outcomes. To validate the superiority of the information diffusion interpolation technique under conditions of sparse samples, we utilize sea surface temperature (SST) data from the offshore waters of China as a case study. We compare three interpolation methods: Kriging, Gaussian information diffusion, and asymmetric information diffusion. The calculations and comparisons of interpolation results are conducted across varying sample sizes. The findings indicate that in situations with relatively sparse samples, asymmetric information diffusion yields the most favorable results, with Kriging and Gaussian diffusion exhibiting comparable performance. In cases of extremely sparse samples, asymmetric information diffusion yields the lowest interpolation error, followed by Gaussian diffusion, while Kriging performs the least effectively. Thus, when confronted with sample sparsity, the application of the information diffusion interpolation method can yield notably improved results.

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

Cited by 19 publications

References 325 publications

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Deep blue artificial intelligence for knowledge discovery of the intermediate ocean

Real-Time Stereo-Based Ocean Surface Mapping for Robotic Floating Platforms: Concept and Methodology

Interpolation of China’s Nearshore Sea Surface Temperature Based on Information Diffusion with Small Sample Sizes

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