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Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework

文献类型：期刊论文

作者Zhao, Qin3,5; Hao, Xiaohua3,6; Shao, Donghang3,6; Ji, Wenzheng3,4; Huang, Guanghui1,5; Zhao, Zisheng3,4; Zhang, Juan2
刊名REMOTE SENSING
出版日期2025-12-10
卷号17期号:24页码:3992
关键词cloud-snow discrimination multi-level decision tree optical remote sensing AVHRR Northern Hemisphere
DOI10.3390/rs17243992
产权排序2
文献子类Article
英文摘要Highlights What are the main findings? Optimized a multi-level decision tree cloud detection algorithm for cloud-snow discrimination. Utilized the distinct bright temperature difference at 3.7 mu m and 11 mu m to enhance cloud detection performance. What are the implications of the main findings? Outperforms existing algorithms and significantly reduces the false cloud detection in snow-covered areas. Provides accurate and efficient cloud detection in the Northern Hemisphere to support related cryospheric studies.Highlights What are the main findings? Optimized a multi-level decision tree cloud detection algorithm for cloud-snow discrimination. Utilized the distinct bright temperature difference at 3.7 mu m and 11 mu m to enhance cloud detection performance. What are the implications of the main findings? Outperforms existing algorithms and significantly reduces the false cloud detection in snow-covered areas. Provides accurate and efficient cloud detection in the Northern Hemisphere to support related cryospheric studies.Abstract High-resolution optical remote sensing imagery plays a crucial role in monitoring the Earth's surface. However, cloud obstruction and spectral confusion between clouds and snow significantly compromise data quality and application reliability, leading to persistent cloud overestimation in optical remote sensing products. To address this challenge, this study developed an enhanced multi-threshold cloud detection algorithm based on AVHRR surface reflectance data, which incorporates dynamic threshold optimization within a multi-level decision tree framework. Utilizing Landsat 5 SR as reference data, the algorithm demonstrated superior cloud-snow discrimination capability, achieving an overall accuracy (OA) of 82.08%, with the user's accuracy (UA) and F-score reaching 79.41% and 82.55%. Comparative evaluation demonstrates that the proposed algorithm outperforms two existing algorithms, with OA improvements of 17.42% and 7.93%, respectively. A particularly notable enhancement is the significant reduction in cloud misidentification, as reflected by UA increases of 21.02% and 13.21%. These improvements are most pronounced in high-altitude mountainous regions with snow cover. The algorithm maintains computational efficiency while providing reliable cloud masking, thereby offering enhanced support for snow cover monitoring and broader environmental applications.
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WOS关键词EXTENT PRODUCT ; LANDSAT DATA ; PART I ; MODIS ; VALIDATION ; DATASET ; AVHRR
WOS研究方向Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
语种英语
WOS记录号WOS:001647474200001
出版者MDPI
源URL[http://ir.igsnrr.ac.cn/handle/311030/219448]  
专题中国科学院地理科学与资源研究所
通讯作者Hao, Xiaohua
作者单位1.Lanzhou Univ, Coll Earth & Environm Sci, Lanzhou 730000, Peoples R China;
2.Meteorol Inst Qinghai Prov, Xining 810001, Peoples R China
3.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Key Lab Cryospher Sci & Frozen Soil Engn, Lanzhou 730000, Peoples R China;
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China;
5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Geog Informat Sci & Technol, Beijing 100101, Peoples R China;
6.Chinese Acad Sci, Northwest Inst Ecoenvironm & Resources, Heihe Remote Sensing Expt Res Stn, Lanzhou 730000, Peoples R China;
推荐引用方式
GB/T 7714		 Zhao, Qin,Hao, Xiaohua,Shao, Donghang,et al. Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework[J]. REMOTE SENSING,2025,17(24):3992.
APA Zhao, Qin.,Hao, Xiaohua.,Shao, Donghang.,Ji, Wenzheng.,Huang, Guanghui.,...&Zhang, Juan.(2025).Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework.REMOTE SENSING,17(24),3992.
MLA Zhao, Qin,et al."Optimizing Cloud Mask Accuracy over Snow-Covered Terrain with a Multistage Decision Tree Framework".REMOTE SENSING 17.24(2025):3992.

入库方式： OAI收割

来源：地理科学与资源研究所

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