Using GaoFen-1 to Distinguish Sargassum Horneri and Ulva Prolifera Blooms in the Western Yellow Sea Based on Machine Learning
文献类型：期刊论文
| 作者 | Zhang, Furong1; Su, Hongbo2; Liu, Kai1; Chen, Shaohui1; Wang, Chengyi3; Li, Juanjuan1 |
| 刊名 | IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 18页码:23431-23446 |
| 关键词 | Algae Accuracy Green products Tides Reflectivity Random forests Indexes Training Satellites Predictive models Classification GaoFen-1 Wide Field of View (GF-1 WFV) random forest (RF) S. horneri and U.prolifera |
| ISSN号 | 1939-1404 |
| DOI | 10.1109/JSTARS.2025.3603709 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | In recent years, large-scale macroalgae blooms with two dominant species of Ulva prolifera (U.prolifera) and Sargassum horneri (S.horneri) have occurred frequently and concurrently in the Yellow Sea and East China Sea. Existing remote sensing models for distinguishing between U.prolifera and S.horneri have achieved high accuracy in specific scenarios but exhibited weak generalizability, which stemmed primarily from training data inadequately representing diverse scenarios. We first established a more representative sample set from 2015-2023 GaoFen-1, Wide Field of View imagery. This sample set encompassed diverse scenarios, including clear water, turbid water, and areas affected by thin clouds and sun glint. Through spectral analysis, we found that: 1) influenced by multiple factors, algal spectra exhibited significant variability and approximately 30% of algal pixels even had similar spectral shapes; 2) combining difference spectra (spectral difference between algae and surrounding water) and water turbidity could effectively distinguish these fuzzy pixels. Subsequently, we developed a random forest classification model, with input features encompassing three dimensions: algal spectra, difference spectra and turbidity. The overall accuracies of our model on both validation set and test set were higher than 90% . Compared to existing methods, our model could effectively distinguish spectrally similar algal pixels and improved classification accuracy by over 10% . |
| URL标识 | 查看原文 |
| WOS关键词 | LARGEST MACROALGAL BLOOM ; GREEN TIDE ; IMAGES |
| WOS研究方向 | Engineering ; Physical Geography ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001575784100021 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/216160]  |
| 专题 | 陆地水循环及地表过程院重点实验室_外文论文 |
| 通讯作者 | Chen, Shaohui |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China; 2.Florida Atlantic Univ, Dept Civil Environm & Geomatics Engn, Boca Raton, FL 33431 USA; 3.Chinese Acad Sci, Aerosp Informat Res Inst, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Furong,Su, Hongbo,Liu, Kai,et al. Using GaoFen-1 to Distinguish Sargassum Horneri and Ulva Prolifera Blooms in the Western Yellow Sea Based on Machine Learning[J]. IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,2025,18:23431-23446. |
| APA | Zhang, Furong,Su, Hongbo,Liu, Kai,Chen, Shaohui,Wang, Chengyi,&Li, Juanjuan.(2025).Using GaoFen-1 to Distinguish Sargassum Horneri and Ulva Prolifera Blooms in the Western Yellow Sea Based on Machine Learning.IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING,18,23431-23446. |
| MLA | Zhang, Furong,et al."Using GaoFen-1 to Distinguish Sargassum Horneri and Ulva Prolifera Blooms in the Western Yellow Sea Based on Machine Learning".IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 18(2025):23431-23446. |
入库方式： OAI收割
来源：地理科学与资源研究所
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