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Spring Dust Intensity Monitoring at Hourly Intervals Using Himawari-8 Satellite Images and Artificial Intelligence Method

文献类型：期刊论文

作者Zhao, Jiafu1,2; Chen, Pengfei1,3; Sun, Xiaolong4
刊名REMOTE SENSING
出版日期2025-10-11
卷号17期号:20页码:3407
关键词dust intensity CNN Bi-LSTM Himawari-8 24-h continuous monitoring
DOI10.3390/rs17203407
产权排序1
文献子类Article
英文摘要Highlights What are the main findings? Time-series data can be used to monitor dust intensity with high accuracy. Combining CNN and BiLSTM can achieve high accuracy for dust intensity monitoring. What is the implication of the main findings? Progressive dust temporal (PDT) features proposed based on time-series data are important variables for dust intensity prediction. The PCBNet model proposed in this study by combining CNN and BiLSTM using PDT features has the best performance for dust intensity prediction among all tested models.Highlights What are the main findings? Time-series data can be used to monitor dust intensity with high accuracy. Combining CNN and BiLSTM can achieve high accuracy for dust intensity monitoring. What is the implication of the main findings? Progressive dust temporal (PDT) features proposed based on time-series data are important variables for dust intensity prediction. The PCBNet model proposed in this study by combining CNN and BiLSTM using PDT features has the best performance for dust intensity prediction among all tested models.Abstract To achieve accurate monitoring of dust intensity, this study developed a coupled model based on a convolutional neural network (CNN) and a bidirectional long short-term memory network (Bi-LSTM) to monitor dust intensity in a 24 h dynamic pattern. During this process, progressive dust temporal (PDT) features reflecting the temporal dynamics of dust events, including clear-sky state values, adjacent observation state values, and current observation state values for spectral indices and brightness temperatures, were first designed. Then, a PCBNet model combining CNN and Bi-LSTM was established and compared with PCLNet (CNN and LSTM), random forest (RF), and support vector machine (SVM) using only single-time observations, as well as PDT-RF and PDT-SVM, which used PDT features as inputs. Finally, a dust intensity product was generated by the optimal model, and its relationship with PM10 concentrations at air quality stations was examined. Furthermore, a dust storm event in April 2021 was analyzed to evaluate the ability of the products to capture event dynamics. The results indicate that PCBNet achieved the highest accuracy among all models on the validation dataset. Predicted dust intensity levels were well correlated with PM10 concentrations, and the monitoring product effectively tracked the spatiotemporal evolution of dust event.
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WOS关键词STORMS ; DIFFERENCE ; RETRIEVAL ; TRANSPORT ; INDEX ; SAND ; LSTM
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001602878200001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/217791]  
专题资源与环境信息系统国家重点实验室_外文论文
通讯作者Chen, Pengfei
作者单位1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China;
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
3.Natl Sci & Technol Infrastruct China, Natl Earth Syst Sci Data Ctr, Beijing 100101, Peoples R China;
4.Inner Mongolia Eco & Agrometeorol Ctr, Hohhot 010051, Peoples R China
推荐引用方式
GB/T 7714		 Zhao, Jiafu,Chen, Pengfei,Sun, Xiaolong. Spring Dust Intensity Monitoring at Hourly Intervals Using Himawari-8 Satellite Images and Artificial Intelligence Method[J]. REMOTE SENSING,2025,17(20):3407.
APA Zhao, Jiafu,Chen, Pengfei,&Sun, Xiaolong.(2025).Spring Dust Intensity Monitoring at Hourly Intervals Using Himawari-8 Satellite Images and Artificial Intelligence Method.REMOTE SENSING,17(20),3407.
MLA Zhao, Jiafu,et al."Spring Dust Intensity Monitoring at Hourly Intervals Using Himawari-8 Satellite Images and Artificial Intelligence Method".REMOTE SENSING 17.20(2025):3407.

入库方式： OAI收割

来源：地理科学与资源研究所

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