## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Unveiling Changes in Different Vulnerable Marine Ecosystems Using AI
#### 4.2.3 DL for Coastal Wetland Mapping and Analysis
[250] used multitemporal SAR data from Sentinel-1 and multispectral data from Sentinel-2, processed on the GEE platform, to map mangroves. They evaluated various MLP architectures with different configurations to identify the best setup. The multisource approach achieved an F\({}_{1}\) score of 97%, outperforming single multispectral data by 2%, highlighting the benefits of integrating multitemporal and multisource remote sensing data. [PERSON] et al. [224] developed a pixel-level weighted image fusion method to combine SAR data from GF-3 and optical imagery from GF-6 for mangrove monitoring on Hainan Island. They employed a U-Net model with a Squeeze-and-Excitation attention mechanism to enhance feature extraction, achieving an OA of 94.41% on the fused SAR and optical images. Salt marshes, another critical type of coastal wetland, are regularly inundated by tidal saltwater. DL combined with remote sensing data has proven effective for mapping and monitoring these ecosystems [251, 252]. For instance, [PERSON] et al. [253] utilized Sentinel-1 SAR data and a CNN model to classify salt marsh vegetation in the Yangtze River estuary, achieving a 97% accuracy. In the context of optical data, [PERSON] et al. [225] employed the U-Net model with Sentinel-2 multispectral images to map salt marshes along the South Carolina coast. Their method successfully addressed challenges posed by tidal effects, achieving an overall accuracy of 90%. For LiDAR data analysis, [PERSON] et al. [226] introduced an artificial neural network-based filtering method to process dense LiDAR point clouds in complex salt marsh environments, achieving an AUC of 0.94. These studies demonstrate the transformative potential of DL and multisource remote sensing for monitoring coastal wetlands. To sum up, DL facilitates precise analyses of climate change impacts on ocean physical properties and vulnerable marine ecosystems. This approach not only enhances our understanding of ocean changes but also supports the development of effective strategies for conservation. However, there are some limitations in existing studies, such as insufficient validation in spatiotemporal contexts and a lack of uncertainty quantification. Model validation in a spatiotemporal context refers to evaluating a DL model's performance across different geographic regions and time periods. This is crucial in developing DL models in oceanography [80], where environmental variations may cause a model to perform well in one area or season but poorly in another. However, such validation practices are still limited in existing studies, and many studies rely on random data splits, which cannot assess a model's robustness and generalizability to spatial and temporal shifts. For example, in sea ice classification, [PERSON] et al. [155] utilized Sentinel-1 SAR data collected from the polar region between 10 and 15 August 2021, and randomly divided it into training, validation, and testing sets at a ratio of 6:1:3. While the experiment results illustrated a good classification performance, this setup does not validate how well the model generalizes to different regions or seasons. Similarly, in SST prediction, [PERSON] et al. [116] focused on temperature data from the East China Sea without evaluating the model's performance in other oceans, limiting its applicability. Besides, uncertainty measures how confident a DL model is in its output and is essential for evaluating the reliability of DL predictions in oceanographic tasks. Uncertainty can arise from two primary sources, including aleatoric uncertainty, which stems from inherent noise in the data (e.g., sensor errors and manual labeling errors), and epistemic uncertainty, which results from model limitations or insufficient training data. Confidence intervals are a common method to quantify uncertainty, providing a statistical range within which the true value of a prediction is expected to fall at a given confidence level.