### Reanalysis Data
In situ data provide high-accuracy point measurements, but their limited spatial coverage and irregular sampling can lead to data sparsity, which may lead to overfitting and affect the generalization capabilities of DL models. Reanalysis data provide full spatial and temporal coverage and are widely used as labels for DL model training. However, since these datasets are generated through data assimilation, they may introduce systematic biases and over-smoothing, especially in extreme conditions. For instance, ERA5, one of the most widely used reanalysis products, inherently lacks high wind speed measurements. As a result, DL models trained on ERA5 wind speed data may underestimate high wind speeds, limiting their effectiveness in applications such as storm forecasting. Ocean data exhibits different data quality issues such as missing values, spatiotemporal heterogeneity, and calibration issues. Missing values refer to absent or invalid observations in oceanographic datasets, resulting from factors such as cloud cover and sensor malfunction. Properly handling missing values is essential in DL, as most DL frameworks cannot process NaN values directly, leading to errors during forward or backward propagation. A common approach to deal with missing data is data imputation, where missing values are substituted with statistical estimates, such as the mean, median, or interpolated values [66]. In more advanced DL architectures, such as masked autoencoders (MAEs), masking mechanisms can be employed to selectively ignore missing parts of the input data and focus on valid observations, as demonstrated in recent applications such as SST gap filling under cloud-covered conditions [67]. Spatiotemporal heterogeneity refers to the inconsistent spatial and temporal resolutions among different oceanographic datasets, which can come across different sensors or the same sensor with varying acquisition modes. For example, MODIS provides 250 m, 500 m, and 1 km products, while Sentinel-2 offers 10 m, 20 m, and 60 m data. To address spatial inconsistencies, interpolation and resampling techniques are widely used, where lower-resolution data are upsampled to match higher-resolution data. For example, MODIS data are upsampled to 30 m to align with Landsat images for fine-scale analysis [68], and ERA5 wind speeds are interpolated to match the geolocation of CYGNSS observations for training DL-based wind retrieval models [69]. DL-based super-resolution models can be employed to enhance the spatial resolution of coarse data, enabling more detailed feature extraction and improving model performance in tasks such as SST monitoring [70; 71]. Moreover, temporal mismatches are typically addressed through temporal interpolation (e.g., linear, spline) or temporal aggregation (e.g., daily, monthly averages) [72], enabling alignment across different temporal resolutions. Calibration issues mean that an instrument's measurements deviate from the actual values. These issues can arise from various factors, such as environmental influences, instrument degradation or replacement, and procedural errors in the calibration process. For example, a recent study identified a systematic bias in wind speed measurements from the Tropical Atmosphere Ocean buoy array due to changes in anemometer types [73]. Such biases may lead DL models to learn inaccurate patterns from the data and generate unreliable results. To mitigate calibration-related challenges, cross-sensor calibration is often employed. This process involves comparing and adjusting data from multiple sensorsto ensure consistency and reliability across different instruments. For example, simultaneous nadir overpass (SNO) is a commonly used approach for calibrating multiple satellite sensors. SNO refers to the condition where two satellites observe the same geographic location at nearly the same time and from similar near-nadir viewing angles.