数字农科院2.0

Research on optimizing the inversion of thermal infrared remote sensing surface temperature and emissivity based on mixture of experts

文献类型： 外文期刊

作者： Liu Ma;Kebiao Mao;Zhonghua Guo;Zijin Yuan

作者机构：

关键词： iterative optimization strategy;land surface emissivity;Land surface temperature;mixture of experts

期刊名称： Remote Sensing Letters

ISSN： 2150-7058

年卷期： 2025 年 16 卷 12 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： To further improve the inversion accuracy of land surface temperature (LST) and land surface emissivity (LSE) of moderate resolution imaging spectroradiometer (MODIS) data, an iterative optimization strategy based on a mixture of experts (MoE) is proposed. In the simulation experiment, compared with the 4-band combination, the mean absolute error (MAE) of LST inversion of 5-band input is reduced from 0.7572 K to 0.5089 K, and the Pearson correlation coefficient (PCC) is increased from 0.9812 to 0.9989. The LSE inversion accuracy is also significantly improved, and the error between the optimized disturbance simulation data and the original simulation data is tiny. In the actual data verification, after optimization, the LST inversion MAE is reduced from 1.7768 K to 1.1237 K, and the PCC is increased from 0.9816 to 0.9946. The iterative optimization strategy proposed in this study significantly improves the accuracy of LST and LSE in MODIS data, provides reliable data support for climate change, environmental assessment, and agricultural disaster monitoring, and has broad application prospects.

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