Data Sheet 5_The colours of the ocean using multispectral satellite imagery to estimate sea surface temperature and salinity in global coastal areas, the gulf of Mexico and the UK.csv

Understanding and monitoring sea surface salinity (SSS) and temperature (SST) is vital for assessing ocean health. Interconnections among the ocean, atmosphere, seabed, and land create a complex environment with diverse spatial and temporal scales. Climate change exacerbates marine heatwaves, eutrophication, and acidification, impacting biodiversity and coastal communities. Satellite-derived ocean colour data provides enhanced spatial coverage and resolution compared to traditional methods, enabling the estimation of SST and SSS. This study presents a methodology for extracting SST and SSS using machine learning algorithms trained with in-situ and multispectral satellite data. A global neural network model was developed, leveraging spectral bands and metadata to predict these parameters. The model incorporated Shapley values to evaluate feature importance, offering insight into the contributions of specific bands and environmental factors. The global model achieved an R2 of 0.83 for temperature and 0.65 for salinity. In the Gulf of Mexico case study, the model demonstrated a root mean square error (RMSE) of 0.83°C for test cases and 1.69°C for validation cases for SST, outperforming traditional methods in dynamic coastal environments. Feature importance analysis identified the critical roles of infrared bands in SST prediction and blue/green colour bands in SSS estimation. This approach addresses the “black box” nature of machine learning models by providing insights into the relative importance of spectral bands and metadata. Key factors such as solar azimuth angle and specific spectral bands were highlighted, demonstrating the potential of machine learning to enhance ocean property estimation, particularly in complex coastal regions.

理解并监测海表盐度（Sea Surface Salinity，SSS）与海表温度（Sea Surface Temperature，SST），对于评估海洋健康状态至关重要。海洋、大气、海底与陆地之间的相互作用，构建出具备多样时空尺度的复杂环境。气候变化加剧了海洋热浪、水体富营养化与海洋酸化等问题，对生物多样性及沿海社区造成负面影响。相较于传统手段，卫星反演得到的海洋水色数据拥有更优的空间覆盖范围与分辨率，可用于估算SST与SSS。本研究提出了一种基于机器学习算法的SST与SSS提取方法，该算法以原位观测数据与多光谱卫星数据为训练集。本研究构建了一个全球级神经网络模型，通过融合光谱波段与元数据来预测上述海洋参数。该模型引入夏普利值（Shapley Values）评估特征重要性，从而揭示特定光谱波段与环境因子的贡献机制。该全球模型的温度预测决定系数（R²）为0.83，盐度预测决定系数为0.65。在墨西哥湾案例研究中，针对SST的测试集与验证集，该模型的均方根误差（Root Mean Square Error，RMSE）分别为0.83℃与1.69℃，在动态沿海环境中的表现优于传统方法。特征重要性分析表明，红外波段在SST预测中发挥关键作用，蓝/绿波段则对SSS估算至关重要。本方法通过揭示光谱波段与元数据的相对重要性，破解了机器学习模型的黑箱特性。研究重点突出了太阳方位角与特定光谱波段等关键影响因子，证明了机器学习技术在提升海洋参数估算能力方面的潜力，尤其适用于复杂沿海区域。