摘要：

海冰厚度是全球气候变化研究中的关键参数之一, 其在调节地球气候系统、海洋环流和热量交换中具有重要作用。但是, 由于海冰的物理特性高度变化, 使得海冰厚度的精确反演面临巨大挑战。针对该问题, 提出一种多特征融合和改进Transformer的被动微波遥感海冰厚度反演方法SIT-TransNET。该方法利用AMSR2卫星的亮度温度数据, 并结合辅助数据(包括雪表面温度、海面盐度和1.4 GHz亮度温度), 探讨这些数据与海冰厚度之间的复杂关联, 分析不同特征的重要性并通过建立不同的特征融合方案, 加强对海冰厚度的有效表征; 通过SIT-TransNET模型的自注意力机制和多头注意力机制捕捉不同特征及组合对于海冰厚度反演的贡献, 并动态调整不同特征的权重, 实现海冰厚度的精确反演。实验结果表明, 相比其他方法, SIT-TransNET方法显著提高了海冰厚度反演精度, 决定系数(R²)达到了0.96, 均方根误差(RMSE)为9 cm, 表明此方法适用于海冰厚度反演, 为实现大范围海冰厚度监测和气候变化研究提供了有效的技术手段。

关键词: 海冰厚度, 被动微波, 特征融合, 自注意力机制, 多头注意力机制

Abstract:

Sea ice thickness is a key parameter in global climate change research, and it plays a crucial role in regulating the Earth's climate system, ocean circulation, and heat exchange. However, accurate retrieval of sea ice thickness is significantly challenging because of the highly variable physical properties of sea ice. To address this issue, this paper proposes a passive microwave remote sensing method for sea ice thickness retrieval named SIT-TransNET. This method integrates multi-feature fusion and an improved Transformer, and it utilizes brightness temperature data from the Advanced Microwave Scanning Radiometer 2 (AMSR2) satellite combined with auxiliary data (including snow surface temperature, sea surface salinity, and 1.4 GHz brightness temperature) to explore complex relationships between these data and sea ice thickness. Further, this method analyzes the importance of different features and enhances the effective representation of sea ice thickness by establishing various feature fusion schemes. The self-attention and multi-head attention mechanisms of the SIT-TransNET model enable capturing the contributions and combinations of different features and dynamically adjusting the weights of different features for accurate sea ice thickness retrieval. Compared to the other methods, experimental results demonstrate that the SIT-TransNET method significantly improves the accuracy of sea ice thickness retrieval with a coefficient of determination (R²) of 0.96 and Root Mean Square Error (RMSE) of 9 cm. This confirms that this method is suitable for sea ice thickness retrieval and provides an effective technical method for large-scale sea ice thickness monitoring and climate change research.

Key words: sea ice thickness, passive microwave, feature fusion, self-attention mechanism, multi-head attention mechanism