## 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, see Table 1 for the list of acronyms) 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]. Our 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]. As discussed, RS systems provide numerous opportunities for studying different oceanographic applications. However, there is not currently a literature review paper that comprehensively investigates and discusses these applications. Therefore, in this study, detailed discussions are provided about different applications of RS in the oceans. This literature review paper is divided into two parts, considering the wide range of RS applications in ocean environments.