Sea ice concentration retrieval using spaceborne GNSS-R during the melting period
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摘要: 针对北极融冰期的海冰密集度反演，并改善全球导航卫星系统反射测量（GNSS-R）对海水的海冰密集度高估问题，本文提出一种利用机器学习算法生成高时空分辨率的融冰期海冰密集度估算方法，提取GNSS-R时延多普勒图（DDM）的特征参数，并结合海表温度数据建立LightGBM模型，将反演结果与参考海冰密集度值进行相关性分析和评估。本文的模型结果与OSI SAF的海冰密集度产品显示出较好的一致性，相关系数、平均绝对误差和均方根误差分别为0.965、0.061和0.090。该方法能够实现对北极海冰边缘区的海冰密集度高精度估计。Abstract: In this paper, a high spatial-temporal resolution sea ice concentration estimation method for the Arctic melting season is proposed, aiming to improve the overestimation of sea ice concentration in seawater by the Global Navigation Satellite System-Reflectometry (GNSS-R). The method utilizes machine learning algorithms to extract feature parameters from the Delay Doppler Maps (DDM) obtained through GNSS-R and combines them with sea surface temperature data to establish a LightGBM model. The inversion results are then subjected to correlation analysis and evaluation against reference sea ice concentration values. The model’s performance is compared with the sea ice concentration product from OSI SAF, demonstrating good consistency, with correlation coefficient, mean absolute error, and root mean square error being 0.965, 0.061, and 0.090, respectively. This approach enables high-precision estimation of sea ice concentration in the Arctic marginal ice zone.
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Key words:
- GNSS-R /
- DDM /
- melting season /
- sea ice concentration /
- LightGBM /
- Arctic
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图 1 0%、50%和90% SIC DDM和对应的归一化积分延迟波形
Fig. 1 DDM for 0%, 50% and 90% SIC DDM and corresponding normalized integrated delay waveforms
图 3 测试集基于DDM特征参数的反演结果与OSISAF海冰密集度对比散点图（a）和基于DDM特征参数的LightGBM特征重要性排序结果（b）
Fig. 3 Scatter plot of retrieval results based on DDM observables against OSISAF sea ice concentration from the test samples (a) and LightGBM feature importance ranking results based on DDM observables (b)
图 4 测试集海冰密集度反演结果与OSISAF海冰密集度对比散点图
a. 基于SST；b. 基于DDM特征参数、SST
Fig. 4 Scatter plot of retrieval results against OSISAF sea ice concentration from the test samples
a. Based on SST; b. based on DDM observables and SST
图 5 整个验证集不同海冰密集度区间与反演误差箱线图
a. 基于DDM特征参数的模型；b. 基于DDM特征参数、SST的模型
Fig. 5 Box plots of different sea ice concentration intervals and retrieval error using the entire validation set
a. The model based on DDM observables; b. the model based on DDM observables and SST
图 6 基于DDM特征参数和SST的反演结果与OSISAF海冰密集度对比散点图
a. 2018年7月20−22日；b. 2018年8月9−10日；c. 2018年8月15−16日
Fig. 6 Scatter plot of retrieval results based on DDM observable against OSISAF sea ice concentration
a. July 20−22, 2018; b. August 9−10, 2018; c. August 15−16, 2018
图 7 2018年8月9日模型反演结果与OSISAF海冰密集度地图比较
Fig. 7 Comparison of model retrieval results with OSISAF sea ice concentration map on August 9th, 2018
表 1 不同特征参数组合的反演误差统计表
Tab. 1 Error Statistics for Different DDM observables Combinations
序号 特征参数组合 R MAE RMSE 1 DDMA + SNR + PP + TES + LES + K + SK (无Γ) 0.867 0.124 0.176 2 Γ + SNR + PP + TES + LES + K + SK (无DDMA) 0.859 0.128 0.181 3 Γ + DDMA + PP + TES + LES + K + SK (无SNR) 0.817 0.146 0.204 4 Γ + DDMA + SNR + TES + LES + K + SK (无PP) 0.851 0.132 0.186 5 Γ + DDMA + SNR + PP + LES + K + SK (无TES) 0.871 0.122 0.174 6 Γ + DDMA + SNR + PP + TES + K + SK (无LES) 0.871 0.122 0.174 7 Γ + DDMA + SNR + PP + TES + LES + SK (无K) 0.866 0.124 0.177 8 Γ + DDMA + SNR + PP + TES + LES + K (无SK) 0.866 0.125 0.177 9 Γ + DDMA + SNR + PP + TES + LES + K + SK 0.875 0.120 0.171 
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