## 4 Impact of Climate Change on Oceans: Leveraging DL for Monitoring and Analysis
### Understanding Impacts on Physical Properties via DL
#### 4.1.3 Advancing Sea Ice Monitoring and Prediction with DL  
Global warming caused by climate change has accelerated the shrinkage of the cryosphere, leading to substantial ice loss. According to the IPCC Summary for Poli-cymakers, from 2006 to 2015, the Greenland Ice Sheet, Antarctic Ice Sheet, and other regions lost ice mass at annual rates of \(278\pm 11\) Gt, \(155\pm 19\) Gt, and \(220\pm 30\) Gt, respectively, [167]. DL has shown immense potential in advancing our understanding of sea ice dynamics, with applications ranging from sea ice detection and classification to thickness estimation and future concentration prediction [168]. Sea ice detection focuses on distinguishing sea ice from open water and other surface types, playing a crucial role in monitoring ice coverage. Various network architectures have been developed and applied to ocean remote sensing data, with SAR being one of the most widely used data sources in existing studies [169]. [PERSON] et al. [155] designed a sea ice detection framework based on the ConvNeXt architecture to process Sentinel-1 SAR images. The framework incorporated a patch dual attention mechanism and a multiscale spatial attention module for enhanced spatial feature extraction, while additional relative position and high-pass filtering channels were added to reduce SAR noise. [PERSON] et al. [156] introduced a dual-attention U-Net model that integrated a Position Attention Module (PAM) and a Channel Attention Module (CAM) for sea ice detection. Using HH and HV polarizations along with incidence angle as input channels, the model exhibited excellent robustness on Sentinel-1 SAR data from the Bering Sea. [PERSON] et al. [157] combined Sentinel-1 SAR data and MODIS multispectral imagery in a U-Net model with dual encoders, achieving superior performance compared to single-source models. In addition to the widely used SAR data, GNSS-R data has demonstrated significant potential for sea ice detection. By analyzing the reflection patterns of GNSS signals, researchers can determine sea ice extent and evaluate properties such as concentration and thickness [170; 171; 172]. [PERSON] et al. [173] developed CNN-based models for sea ice detection and concentration retrieval, using TDS-1 delay Doppler Map (DDM) variables as input and sea ice concentration derived from passive microwave sensors as output. Building on this, [PERSON] et al. [174] improved the CNN-based model by incorporating residual connections to address the vanishing gradient problem. Using DDM data from TDS-1, their model exhibited high robustness to noise and strong stability during the sea ice melting period. Sea ice detection is a binary classification task that identifies sea ice and open water as two distinct categories. Sea ice classification, on the other hand, categorizes sea ice into more types such as new ice, first-year ice, and multiyear ice, providing more detailed information about ice formation and characteristics. Such information is typically obtained from sea ice charts, which utilize egg codes to represent various ice types and their attributes within a specific region. With advancements in remote sensing and DL, the accuracy and efficiency of sea ice classification have improved greatly [175]. [PERSON] et al. [176] leveraged SAR data from the RCM, a Canadian satellite system, to classify sea ice into new ice, first-year ice, and old ice, achieving a classification accuracy of 99% with Normalizer-Free ResNet (NFNet), a DL model featuring adaptive gradient clipping to address gradient instability. In 2022, ESA launched the Autolce Challenge to advance accurate and robust sea ice parameter retrieval [177]. The competition's first-place winners, [PERSON] et al. [158], developed an automated sea ice mapping pipeline using a multitask U-Net architecture. By combining multisource satellite and auxiliary data, their approach successfully estimates sea ice concentration, floe size, and stage of development. In the realm of advanced Transformer architectures, [PERSON] et al.