Leveraging Landsat and Google Earth Engine for long-term chlorophyll-a monitoring: A case study of Lake Balaton's water quality
文献类型：期刊论文
| 作者 | Li, Huan1,2,8; Somogyi, Boglarka1,2; Chen, Xiaona3; Luo, Zengliang4; Blix, Katalin1,5; Wu, Sirui6; Duan, Zheng7; Toth, Viktor R.1,2 |
| 刊名 | ECOLOGICAL INFORMATICS
 |
| 出版日期 | 2025-12-01 |
| 卷号 | 90页码:103245 |
| 关键词 | Water quality Remote sensing Oversampling imbalanced dataset Machine learning Applicability of Landsat Integrating temporal information |
| ISSN号 | 1574-9541 |
| DOI | 10.1016/j.ecoinf.2025.103245 |
| 产权排序 | 3 |
| 文献子类 | Article |
| 英文摘要 | Chlorophyll-a (Chl-a) is one of the critical water quality indicators that shows the eutrophication status of aquatic ecosystems. As the largest lake and a well-known attraction in middle Europe, Lake Balaton contributes 70 % or more of local economy through tourism, while also maintaining a unique biodiversity. Therefore, long-term monitoring of water quality is essential for its effective management. With the longest global environmental record and a preferable spatial resolution, the satellite constellation Landsat is used for retrieving Chl-a in this study. However, the common low-frequent in-situ samplings and similar to 16-day revisit of Landsat have limited both the quality and applicability of Landsat to Chl-a retrieval. Initially, we trained both linear and several machine learning models using matchups between in-situ measurements and satellite data from Landsat 4-9 missions during 1984 and 2023. To address the imbalanced data problem, which lacks high concentration samples due to the rare blooming events, we extend the time tolerance, incorporate temporal information, which connotes the phenology information, and apply an oversampling technique during the training process. Validated on Lake Balaton, which has a spatiotemporal amplitude of Chl-a concentration ranging from 5 to 260 mu g/L since 1980s, Random Forest model has the best accuracy, which shows an R-square 0.86 and RMSE 8.16 mu g/L. The over-sampling technique improves the accuracy by 9.5 % than the non-oversampled. Leveraging all strategies improves overall accuracy by 21 %. The result also shows a reasonable trade-off via increasing the number of matchups 3 to 8 times by extending the time tolerance from the same day to 3 days regardless of the high variability of Chl-a due to the sinking and floating movement of algae. The enhancement framework can be applied to other lakes, especially for lakes with coarse samplings and wide Chl-a fluctuations. We present an open-source online tool for historical and real-time Chl-a mapping, designed for both experts and the public. With customizable code for global lakes, results are continuously showcased on the HUN-REN Balaton Limnological Research Institute's website and social media. |
| URL标识 | 查看原文 |
| WOS关键词 | ATMOSPHERIC CORRECTION ; INLAND ; ALGORITHM ; PATTERNS |
| WOS研究方向 | Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:001506571000001 |
| 出版者 | ELSEVIER |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214500]  |
| 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 | Li, Huan |
| 作者单位 | 1.HUN REN Balaton Limnol Res Inst, Tihany, Hungary; 2.HUN REN Balaton Limnol Res Inst, Natl Lab Water Sci & Water Secur, Tihany, Hungary; 3.Chinese Acad Sci, State Key Lab Resources & Environm Informat Syst, Inst Geog Sci & Nat Resources Res, Beijing, Peoples R China; 4.China Univ Geosci, Sch Geog & Informat Engn, Hubei Key Lab Reg Ecol & Environm Change, Wuhan 430074, Peoples R China; 5.NORCE Norwegian Res Ctr, Grimstad, Norway; 6.Beijing Inst Technol, Sch Comp Sci & Technol, Beijing, Peoples R China; 7.Lund Univ, Dept Phys Geog & Ecosyst Sci, Lund, Sweden; 8.ESGreen LLC, New York, NY 13753 USA |
| 推荐引用方式 GB/T 7714 | Li, Huan,Somogyi, Boglarka,Chen, Xiaona,et al. Leveraging Landsat and Google Earth Engine for long-term chlorophyll-a monitoring: A case study of Lake Balaton's water quality[J]. ECOLOGICAL INFORMATICS,2025,90:103245. |
| APA | Li, Huan.,Somogyi, Boglarka.,Chen, Xiaona.,Luo, Zengliang.,Blix, Katalin.,...&Toth, Viktor R..(2025).Leveraging Landsat and Google Earth Engine for long-term chlorophyll-a monitoring: A case study of Lake Balaton's water quality.ECOLOGICAL INFORMATICS,90,103245. |
| MLA | Li, Huan,et al."Leveraging Landsat and Google Earth Engine for long-term chlorophyll-a monitoring: A case study of Lake Balaton's water quality".ECOLOGICAL INFORMATICS 90(2025):103245. |
入库方式： OAI收割
来源：地理科学与资源研究所
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