Inverting the Concentrations of Chlorophyll-a and Chemical Oxygen Demand in Urban River Networks Using Normalized Hyperspectral Data
文献类型：期刊论文
| 作者 | Guan, Rongda1,2; Hou, Yingzhuo1,2; Arif, Maham1,2; Xing, Qianguo1,2 |
| 刊名 | SENSORS
 |
| 出版日期 | 2025-11-16 |
| 卷号 | 25期号:22页码:18 |
| 关键词 | hyperspectral sensor remote sensing machine learning chlorophyll-a chemical oxygen demand Zhongshan river networks |
| DOI | 10.3390/s25227004 |
| 通讯作者 | Xing, Qianguo(qgxing@yic.ac.cn) |
| 英文摘要 | Chlorophyll-a (Chl-a) and chemical oxygen demand (COD) are key indicators for water quality evaluation. In previous research on the inversion of Chl-a and COD concentrations using hyperspectral data, disparities in hyperspectral data types have constrained the universality of the inversion models. To solve this problem, in this study, synchronous in situ hyperspectral data and water samples were collected from 308 stations within the river networks of Zhongshan City. Four inversion models, support vector regression (SVR), random forest (RF), backpropagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN), were established using the original reflectance (R), remote sensing reflectance (Rrs), and their normalized forms as inputs. To evaluate the robustness of the models, their performance was assessed via cross-reflectance type validation. For example, a model was trained using R data and then tested with Rrs data. The results show that using the normalized hyperspectral data for modeling not only improves the accuracy of the inversion results of Chl-a and COD concentrations, but also effectively unifies different types of hyperspectral data, thereby improving the versatility of the inversion model. This study provides a reference for constructing a general water quality inversion model based on hyperspectral data. |
| WOS关键词 | IN-SITU ; CLASSIFICATION ; REFLECTANCE ; PARAMETERS ; MATTER ; WATERS |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:001624558100001 |
| 资助机构 | Instrument Developing Project of the Chinese Academy of Sciences ; Seed Project of Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences ; National Natural Science Foundation of China ; Guiding Program for Technology Innovation of Shandong Province, China ; Key R&D Program of Shandong Province, China |
| 源URL | [http://ir.yic.ac.cn/handle/133337/41676]  |
| 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 | Xing, Qianguo |
| 作者单位 | 1.Chinese Acad Sci, Shandong Key Lab Coastal Environm Proc, Yantai Inst Coastal Zone Res, Yantai 264003, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Guan, Rongda,Hou, Yingzhuo,Arif, Maham,et al. Inverting the Concentrations of Chlorophyll-a and Chemical Oxygen Demand in Urban River Networks Using Normalized Hyperspectral Data[J]. SENSORS,2025,25(22):18. |
| APA | Guan, Rongda,Hou, Yingzhuo,Arif, Maham,&Xing, Qianguo.(2025).Inverting the Concentrations of Chlorophyll-a and Chemical Oxygen Demand in Urban River Networks Using Normalized Hyperspectral Data.SENSORS,25(22),18. |
| MLA | Guan, Rongda,et al."Inverting the Concentrations of Chlorophyll-a and Chemical Oxygen Demand in Urban River Networks Using Normalized Hyperspectral Data".SENSORS 25.22(2025):18. |
入库方式： OAI收割
来源：烟台海岸带研究所
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