| 基于卫星红外资料的PM2.5信号识别与可解释反演 |
| 投稿时间：2025-04-08 修订日期：2025-04-21 点此下载全文 |
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| 基金项目:安徽省高校杰出青年科研项目（安徽省高校杰青）（2022AH020093）；安徽省自然科学基金（2408085MD102）；巢湖学院学科建设质量提升工程（XLZ202404）；安徽省重点研究与开发计划（2022h11020002）；安徽省教育厅科学研究项目（KJ2021A1027）；巢湖学院科研基金资助项目（KYQD-202211） |
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| 中文摘要:直径小于2.5微米的地面细颗粒物（particulate matter，简称PM）（PM2.5）对人类健康和经济产生了负面影响。大多数方法都是从卫星反演的气溶胶光学厚度间接产品或白天大气顶部反射率中获取PM2.5。文中目的是直接使用风云四号B星多通道扫描成像辐射计（Advanced Geosynchronous Radiation Imager，简称AGRI）红外资料和人工智能模型近实时全时段（包括白天和夜晚）反演江淮地区4公里和15分钟时空分辨率的PM2.5。文中首先分季节探讨了AGRI亮温对不同量级PM2.5的信号响应；其次，分季节基于随机森林方法开展了AGRI亮温反演PM2.5研究。试验结果得到四季节反演的PM2.5相关系数均超过0.87；最后，基于SHapley加性预测实现了模型可解释，得到地理信息对PM2.5贡献率较大。并进一步探讨了文中产品的应用。 |
| 中文关键词:风云四号B星 PM2.5反演 人工智能 SHAP可解释 江淮地区 |
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| PM2.5 signal recognition and interpretable retrieval based on satellite infrared data |
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| Abstract:Particulate matter with a diameter less than 2.5 microns (PM2.5) on the ground has a negative impact on human health and the economy. Most methods obtain PM2.5 from satellite derived aerosol optical depth (AOD) indirect products or daytime atmospheric top reflectance. The purpose of this paper is to directly use the advanced geosynchronous radiation imager (AGRI) infrared data from Fengyun-4B satellite and artificial intelligence models to retrieve PM2.5 with spatial and temporal resolution of 4 kilometers and 15 minutes in the Yangtze-Huaihe region in near-real time throughout the entire time period (including day and night). Firstly, explore the signal response of AGRI brightness temperature to different levels of PM2.5 in different seasons; Secondly, a study on AGRI brightness temperature retrieval of PM2.5 was conducted based on the random forest method in different seasons. The experimental results showed that the PM2.5 correlation coefficients obtained from the seasonal retrieval were all over 0.87; Finally, based on SHapley Additive exPlanations (SHAP), the model was interpretable and the contribution of geographic information to PM2.5 was found to be significant. And further explored the application of the products mentioned in this paper. |
| keywords:Fengyun-4B PM2.5 retrieval artificial intelligence SHAP interpretable Yangtze-Huaihe region |
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