Studying Long-Term Nutrient Variations in Semi-Enclosed Bays Using Remote Sensing and Machine Learning Methods: A Case Study of Laizhou Bay, China
文献类型：期刊论文
| 作者 | Liu, Xingmin1,2; Qiao, Lulu1; Song, Dehai6; Yu, Xiaoxia2; Zhong, Yi5; Wang, Jin4; Wang, Yueqi3 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-08-16 |
| 卷号 | 17期号:16页码:16 |
| 关键词 | remote sensing machine learning nutrient variations Laizhou Bay |
| DOI | 10.3390/rs17162857 |
| 通讯作者 | Qiao, Lulu(luluq@ouc.edu.cn) |
| 英文摘要 | Semi-enclosed bays are greatly affected by human activities and have undergone drastic changes in their ecological environment, which requires our continuous attention. Laizhou Bay (LZB) is a typical semi-closed bay that is greatly influenced by human activities, and it is highly representative on a global scale. Investigating the multi-scale variation in nutrient concentrations in semi-enclosed bays can provide scientific support for environmental management and policy adjustments. To address the limitations of in situ data and the high cost of field surveys, this study utilizes machine learning methods to construct MODIS remote sensing models for quantitatively analyzing the concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the surface water of LZB, as well as the spatiotemporal factors influencing them. Among various methods tested, the Support Vector Machine Regression (SVR) algorithm demonstrated the best performance in retrieving nutrient concentrations in LZB. The R-2 values of the DIN and DIP retrieval results based on the SVR algorithm are 0.91 and 0.92, respectively, while the RMSE values are 5.43 and 0.08 mu mol/L, respectively. The retrieval results indicate that nearshore nutrient concentrations are significantly higher than those in offshore areas. Temporally, from 2003 to 2024, the DIN concentration in l has decreased at a rate of 0.4 mu mol/L/yr, while the DIP concentration has remained relatively stable. Given sufficient observation data, the proposed machine learning and remote sensing approach can be effectively applied to other bays, offering the advantages of long time series, high spatial resolution, and a low cost. |
| WOS关键词 | BOHAI SEA ; CHLOROPHYLL-A ; HEAVY-METALS ; NITROGEN |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001560092200001 |
| 资助机构 | National Natural Science Foundation of China ; National Key Research and Development Program of China China-Nigeria Joint Laboratory on River delta |
| 源URL | [http://ir.yic.ac.cn/handle/133337/40802]  |
| 专题 | 烟台海岸带研究所_海岸带信息集成与综合管理实验室 |
| 通讯作者 | Qiao, Lulu |
| 作者单位 | 1.Ocean Univ China, Coll Marine Geosci, Qingdao 266100, Peoples R China 2.Shandong Acad Environm Planning, Key Lab Land & Sea Ecol Governance & Systemat Regu, Jinan 250101, Peoples R China 3.Chinese Acad Sci, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 4.Shandong Jianzhu Univ, Shandong Prov Inst Resources & Environm Innovat, Jinan 250100, Peoples R China 5.Minist Nat Resources, Inst Oceanog 1, Key Lab Coastal Sci & Integrated Management, Qingdao 266061, Peoples R China 6.Ocean Univ China, Frontiers Sci Ctr Deep Ocean Multispheres & Earth, Key Lab Phys Oceanog, Qingdao 266100, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Xingmin,Qiao, Lulu,Song, Dehai,et al. Studying Long-Term Nutrient Variations in Semi-Enclosed Bays Using Remote Sensing and Machine Learning Methods: A Case Study of Laizhou Bay, China[J]. REMOTE SENSING,2025,17(16):16. |
| APA | Liu, Xingmin.,Qiao, Lulu.,Song, Dehai.,Yu, Xiaoxia.,Zhong, Yi.,...&Wang, Yueqi.(2025).Studying Long-Term Nutrient Variations in Semi-Enclosed Bays Using Remote Sensing and Machine Learning Methods: A Case Study of Laizhou Bay, China.REMOTE SENSING,17(16),16. |
| MLA | Liu, Xingmin,et al."Studying Long-Term Nutrient Variations in Semi-Enclosed Bays Using Remote Sensing and Machine Learning Methods: A Case Study of Laizhou Bay, China".REMOTE SENSING 17.16(2025):16. |
入库方式： OAI收割
来源：烟台海岸带研究所
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