Scholarlink

<p>The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes. Accurate remote sensing data on sea ice concentration (SIC) is crucial for effective route planning of ships and ensuring navigational safety. Despite the availability of numerous SIC products in China, these datasets still lag behind mainstream international products in terms of data accuracy, spatiotemporal resolution, and time span. To enhance the accuracy of China’s domestic SIC remote sensing data, this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen (BRM-SIC) as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets: the dataset derived from the microwave radiation imager (MWRI) aboard the FY-3D satellite, provided by the National Satellite Meteorological Center (FY-SIC), and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite, provided by Ocean University of China (OUC-SIC). Based on the evaluation results, a TransUnet fusion correction model was developed. The performance of this model was then compared against Ordinary Least Squares (OLS), Random Forest (RF), and UNet correction models, through spatial and temporal analyses. Results indicate that, compared to FY-SIC data, the <i>RMSE</i> of the OUC-SIC data and the standard data is reduced by 24.245%, while the <i>R</i> is increased by 12.516%. Overall, the accuracy of OUC-SIC data is superior to that of FY-SIC data. During the research period (2020–2022), the standard deviation (SD) and coefficient of variation (CV) of OUC-SIC were 3.877% and 10.582%, respectively, while those for FY-SIC were 7.836% and 7.982%, respectively. In the study area, compared with OUC-SIC data, FY-SIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas. These results indicate that the OUC-SIC data exhibit better temporal and spatial stability, whereas the FY-SIC data show stronger relative dimensionless stability. Among the four correction models, all showed improvements over the original, unfused corrected data. The fusion corrections using the OLS, RF, UNet, and TransUnet models reduced <i>RMSE</i> by 5.563%, 14.601%, 42.927%, and 48.316%, respectively. Correspondingly, <i>R</i> increased by 0.463%, 1.176%, 3.951%, and 4.342%, respectively. Among these models, TransUnet performed the best, effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Fusion Correction for China’s Domestic Remote Sensing Data of Sea Ice Concentration Using the TransUnet Model

  • 期刊论文

  • 发表日期：

    2026年1月23日

查看全文
  • Chunxiao Zhao,
  • Yanrui Yang,
  • Guocan Zhu,
  • Hongchun Zhu

摘要

The rapid melting of Arctic sea ice poses significant risks to the safety of shipping routes. Accurate remote sensing data on sea ice concentration (SIC) is crucial for effective route planning of ships and ensuring navigational safety. Despite the availability of numerous SIC products in China, these datasets still lag behind mainstream international products in terms of data accuracy, spatiotemporal resolution, and time span. To enhance the accuracy of China’s domestic SIC remote sensing data, this study used the SIC data derived from the passive microwave remote sensing dataset provided by the University of Bremen (BRM-SIC) as a reference to conduct a comprehensive evaluation and analysis of two additional SIC datasets: the dataset derived from the microwave radiation imager (MWRI) aboard the FY-3D satellite, provided by the National Satellite Meteorological Center (FY-SIC), and the dataset obtained through the DT-ASI algorithm from the microwave imager of the FY-3D satellite, provided by Ocean University of China (OUC-SIC). Based on the evaluation results, a TransUnet fusion correction model was developed. The performance of this model was then compared against Ordinary Least Squares (OLS), Random Forest (RF), and UNet correction models, through spatial and temporal analyses. Results indicate that, compared to FY-SIC data, the RMSE of the OUC-SIC data and the standard data is reduced by 24.245%, while the R is increased by 12.516%. Overall, the accuracy of OUC-SIC data is superior to that of FY-SIC data. During the research period (2020–2022), the standard deviation (SD) and coefficient of variation (CV) of OUC-SIC were 3.877% and 10.582%, respectively, while those for FY-SIC were 7.836% and 7.982%, respectively. In the study area, compared with OUC-SIC data, FY-SIC data exhibited a larger standard deviation of deviation and a smaller coefficient of variation of deviation across most sea areas. These results indicate that the OUC-SIC data exhibit better temporal and spatial stability, whereas the FY-SIC data show stronger relative dimensionless stability. Among the four correction models, all showed improvements over the original, unfused corrected data. The fusion corrections using the OLS, RF, UNet, and TransUnet models reduced RMSE by 5.563%, 14.601%, 42.927%, and 48.316%, respectively. Correspondingly, R increased by 0.463%, 1.176%, 3.951%, and 4.342%, respectively. Among these models, TransUnet performed the best, effectively integrating the advantages of FY-SIC and OUC-SIC data and notably improving the overall accuracy and spatiotemporal stability of SIC data.