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Amani, M.;  Moghimi, A.;  Mirmazloumi, S.M.;  Ranjgar, B.;  Ghorbanian, A.;  Ojaghi, S.;  Ebrahimy, H.;  Naboureh, A.;  Nazari, M.E.;  Mahdavi, S.; et al. Ocean Remote Sensing Techniques and Applications. Encyclopedia. Available online: (accessed on 14 April 2024).
Amani M,  Moghimi A,  Mirmazloumi SM,  Ranjgar B,  Ghorbanian A,  Ojaghi S, et al. Ocean Remote Sensing Techniques and Applications. Encyclopedia. Available at: Accessed April 14, 2024.
Amani, Meisam, Armin Moghimi, S. Mohammad Mirmazloumi, Babak Ranjgar, Arsalan Ghorbanian, Saeid Ojaghi, Hamid Ebrahimy, Amin Naboureh, Mohsen Eslami Nazari, Sahel Mahdavi, et al. "Ocean Remote Sensing Techniques and Applications" Encyclopedia, (accessed April 14, 2024).
Amani, M.,  Moghimi, A.,  Mirmazloumi, S.M.,  Ranjgar, B.,  Ghorbanian, A.,  Ojaghi, S.,  Ebrahimy, H.,  Naboureh, A.,  Nazari, M.E.,  Mahdavi, S.,  Moghaddam, S.H.A.,  Asiyabi, R.M.,  Ahmadi, S.A.,  Mehravar, S.,  Mohseni, F., & Jin, S. (2022, November 10). Ocean Remote Sensing Techniques and Applications. In Encyclopedia.
Amani, Meisam, et al. "Ocean Remote Sensing Techniques and Applications." Encyclopedia. Web. 10 November, 2022.
Ocean Remote Sensing Techniques and Applications

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. Various types of RS techniques have been developed and utilized for different oceanographic applications.

remote sensing ocean ocean wind ocean current

1. Introduction

Oceans cover more than 70% of the Earth’s surface and provide countless benefits. For example, the oceans produce over 50% of the world’s oxygen and store carbon dioxide. Moreover, the oceans transport heat from the equator to the poles and regulate climate patterns. Additionally, oceans play a key role in transportation, food provision, and economic growth. Oceans are also important for recreational activities [1][2][3]. Considering the importance of ocean environments, it is important to protect them using advanced technologies. To this end, datasets collected by in situ, shipborne, airborne, and spaceborne systems are being utilized.
Although in situ measurements provide the most accurate datasets for ocean studies, they have several limitations. For example, they are point-based observations and cover small areas. Moreover, deployment and maintenance of in situ platforms (e.g., buoys) are expensive and labor-intensive [4]. Shipborne approaches also have their own disadvantages. For instance, they can only measure Ocean Surface Wind (OSW) along specific tracks, and the vastness and remoteness of ocean environments hinder surveillance of human activities because authorities cannot frequently provide effective vessel control [5]. On the other hand, ocean mapping and monitoring using airborne and spaceborne Remote Sensing (RS) systems are of significant interest due to the large coverage, a wide range of temporal and spatial resolutions, as well as low cost of the corresponding datasets [6][7][8]. The understanding of ocean environments, including marine animals, oceanic biogeochemical processes, and the relationship between oceans and climate changes, has considerably improved due to the availability of global, repetitive, and consistent archived satellite observations. It should be noted that although RS provides a great opportunity for ocean studies, it does not obviate the necessity of in situ measurements, and they usually play a supporting role to each other in different oceanographic applications.
Different methods have been so far developed to derive oceanographic parameters from RS datasets. These methods can be generally divided into three groups of statistical, physical, and Machine Learning (ML) models. Statistical algorithms are mainly based on the correlation relationships between in situ measurements of oceanographic parameters and the information collected by RS systems. These models are usually easy to develop and provide fairly reasonable accuracies. However, they require in situ data, which are sometimes not available over remote ocean areas. These models also need to be optimized for different study areas. Physical models (e.g., Radiative Transfer (RT)) are based on the physical laws of the RS systems. Although these models usually provide better results than statistical models, they require many inputs that are usually not available. Recently, ML algorithms, either traditional (e.g., Random Forest (RF) and Support Vector Machine (SVM)) or more advanced models (e.g., Convolutional Neural Network (CNN)), have been frequently utilized for various oceanographic applications. Generally, like many other applications of RS, Deep Learning (DL) methods provide higher accuracies compared to statistical, physical, and traditional ML algorithms [9][10][11]. However, it should be noted that DL methods require a very large number of training data and are computationally expensive [12]. Consequently, it is sometimes more reasonable to utilize other, less-costly ML algorithms [13][14].

3. RS Applications in Ocean

As discussed in the Introduction, six oceanographic applications of RS are explained in Part 1. These applications, along with the RS systems which can be used to study them, are illustrated in Figure 1. More detailed discussions are also provided in the following six subsections.
Figure 1. Overview of the met-ocean applications of RS which are discussed.

3.1. Ocean Surface Wind (OSW)

OSW is an essential parameter for various applications, such as marine disaster monitoring, climate change modeling, water mass formations, and Numerical Weather Prediction (NWP) [15][16][17][18]. Considering the limitations of the traditional methods for OSW estimation (e.g., anemometers and buoys) [15][19], RS observations have emerged as cost-effective techniques [20]. Remotely sensed OSW information mainly relies on the relationship between the OSW and the sea surface roughness, which represents emissive and reflective properties of the ocean surface [18]. Five RS systems have been frequently applied to measure OSW: microwave radiometer, GNSS-R, SAR, scatterometer, and HF radar. The advantages and disadvantages of each system, summarized in Table 1, are discussed in more detail in the following subsections.
Table 1. Different RS systems for OSW estimation along with their advantages and disadvantages.
RS System (Passive/Active) RS System (Type) Advantage Disadvantage
Passive Microwave radiometer Appropriate efficiency in high wind speeds, large-scale coverage Low accuracy for OSW direction estimation in low wind speeds, coarse spatial resolution
GNSS-R Higher spatial and temporal resolution, less sensitivity atmospheric attenuation, low-cost, low weight, low power needs for receivers, unique sensing geometry Inadequate number of satellites, need more investigation and validation
Active SAR High spatial resolution, applicable at both low and high wind speeds Speckle noise issue, challenging preprocessing steps
Scatterometer Good efficiency in low wind speeds, global coverage Coarse spatial resolution, saturated signal in high wind speeds, rain contamination
HF radar Reasonable accuracy at different wind speeds, large-scale coverage Availability of OSW data only at specific coastal locations where the HF radar has been installed

3.2. Ocean Surface Current (OSC)

OSC is the continuous and directional movements of the mass of the seawater, transferring nutrients, energy, heat, pollutants, and chemical substances around the world [21][22]. Ocean currents affect the global climate, the ocean’s ecosystems, and fishing productivity. More importantly, they play a key role in reducing shipping costs, fuel consumption, as well as developing policies for preventing natural disasters [21][23]. OSC can originate from a wide range of factors, such as wind, Coriolis effect, water density variation, Ocean Tide (OT), as well as SST and OS differences [21][22][24][25]. The seafloor and shoreline topography can also affect OSC and hinder or boost the mixing and passageway of water from different areas [26]. Ocean currents can be generally divided into five categories: (1) geostrophic ocean current, which is balanced under pressure gradient force by the Coriolis effect; (2) tidal ocean current, which is created by the gravitational force of the moon, sun, and Earth; (3) wind-driven Ekman ocean current, which is created by the steady ocean wind; (4) wave-induced Stokes drift, which is characterized by the difference between the average Lagrangian flow velocity of a fluid parcel and the average Eulerian flow velocity of the fluid at a fixed position; and (5) small-scale ocean current, which is created by the small features such as eddies, fronts, and filaments [27]. Ocean currents are also separated into two groups based on their temperature: warm and cold ocean currents (see Figure 1) [3]. For instance, the Gulf stream, Kuroshio, and the Agulhas are warm currents that transport heat from the tropics poleward and significantly affect the global climate [21][28][29]. The Humboldt, Benguela, and California are cold currents that preserve highly upwelling waters and carry cold water toward the equator [21][29]. For example, the Labrador Current has a cooling effect with a low OS and is known for transporting icebergs from Greenland’s glaciers into shipping lanes in the North Atlantic [29][30].
Figure 1. The global ocean currents, including warm currents (red line), cold currents (blue line), and neutral current (black line) adopted from (accessed on 8 October 2022).
Ocean currents can also be categorized into two groups according to their depth: surface and deep (subsurface) [21][27][31]. The surface currents are horizontal water streams that occur on local to global scales, and their effects are primarily restricted to the top 400 m of ocean water [21][32]. Along the coasts and offshore regions, there are local surface currents, which are typically small and short-lived (e.g., hourly/seasonal), generated by OT, waves, buoyant river plumes, and local-scale winds [21][32]. These currents control the local flooding, algal bloom, marine pollution, sediment transport, and ship navigation [21][32]. The global surface currents (e.g., the Gulf Stream) are typically controlled by dominant global winds (e.g., trade winds and the westerlies) together with Coriolis force and the restriction of flow by continental deflections [21][23][32]. These currents travel over long distances in the same direction as the wind and at a speed of approximately 3 to 4% of winds’ speed [21][32]. However, the Coriolis force deflects these currents from the equator to the right direction in the Northern Hemisphere and the left in the Southern Hemisphere, which creates the clockwise and counterclockwise circular patterns or gyres, respectively [21][23][24][32][33]. In contrast, the deep ocean currents are vertical streams under the influence of the thermohaline circulation generated by water density differences and depend on temperature and OS [21][34]. Deep ocean currents are formed with upwelling and downwelling directions below 400 m of the surface water [35].
Depending on the scale of the ocean currents, they are measured by different methods. Figure 2 illustrates various in situ and RS methods for ocean current estimation. It should be noted that the focus of this section is on the OSC using offshore, shipborne, and spaceborne platforms. Table 2 also summarizes these systems along with their advantages and limitations for OSC studies. More details about the applications of each system are also provided in the following subsections.
Figure 2. Different methods for ocean current estimation.


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Subjects: Oceanography
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