An AI Framework to Obtain High-Accurate and Fine-Resolution LST From Passive Microwave Remote Sensing

文献类型： 外文期刊

第一作者： Deng, Xiangyi

作者： Deng, Xiangyi;Yu, Wenping;Zhou, Wei;Huang, Yajun;Zhao, Ruoyi;Yang, Wen;Yu, Wenping;Shi, Jinan;Long, Yinping;Tan, Junlei;Han, Xiaojing

作者机构：

关键词： Artificial neural networks; land surface temperature (LST); passive microwave (PMW); random forest (RF); validation; Artificial neural networks; land surface temperature (LST); passive microwave (PMW); random forest (RF); validation

期刊名称：IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING （ 影响因子：8.6； 五年影响因子：8.8 ）

ISSN： 0196-2892

年卷期： 2024 年 62 卷

页码：

收录情况： SCI

摘要： Land surface temperature (LST) is crucial for the energy balance between the Earth's surface and the atmosphere. Thermal infrared (TIR) and passive microwave (PMW) remote sensing are key methods for acquiring surface temperature globally and regionally. TIR observations have certain limitations due to their inability to penetrate cloud cover. Conversely, PMW measurements partially overcome this drawback to some extent, but their lower retrieval accuracy and coarse resolution limit its wider application. This study developed an artificial intelligence (AI) framework for precise and high-resolution LST estimation from PMW measurements, comprising PMW LST retrieval and downscaling components. Within this framework, high-resolution LST products have been obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2), and the station-based validations and sensitivity analysis have also been conducted on the algorithm. The results were given as follows. First, the GeoFusionNet algorithm achieved higher LST retrieval accuracy than empirical or physical models. The mean absolute error (MAE) was 2.37 K (1.60 K) during daytime (nighttime). Second, the downscaled PMW LST retained high accuracy, with a daytime (nighttime) MAE increase of 0.28 K (0.14 K) compared to the Moderate Resolution Imaging Spectroradiometer (MODIS) 1-km product. Station-based validations showed that the coefficient of determination R-2 was above 0.9, with an average root-mean-squared error (RMSE) of 3.4 K (2.4 K) for daytime (nighttime) and an MAE of 2.80 K (1.98 K). Third, sensitivity analysis demonstrated the algorithm's stable performance, especially in summer and autumn. Spatially, the accuracy remained within 3 K for various land types, including cropland, evergreen forests, and deciduous forests. These results indicate that PMW LST retrieved by this framework has sufficient accuracy and fine-spatial resolution for monitoring dynamic changes in large-scale hydrological, climatic, and agricultural fields.

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