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Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy

文献类型：期刊论文

作者Cai, Cheng1; Liu, Linlin1; Wang, Ziming1; Pang, Wei1; Bai, Congshuo1; Zhang, Huanxue1,2
刊名ECOLOGICAL INDICATORS
出版日期2025-07-01
卷号176页码:113723
关键词Remote Sensing NAWQPs Hyperspectral Imaging Aquatic vegetation Ensemble Learning
ISSN号1470-160X
DOI10.1016/j.ecolind.2025.113723
产权排序2
文献子类Article
英文摘要Remote sensing technology has provided significant support for the spatial and quantitative limitations of traditional water quality monitoring methods. However, accurate retrieval of non-optically active water quality parameters (NAWQPs) remains challenging due to their weak spectral responses and interference from diverse aquatic vegetation. In this study, we proposed a novel zoning-based ensemble modeling strategy (ZBEMS) by integrating aquatic vegetation classification with hyperspectral features derived from ZY-1 02D images, and tested it in Nansi Lake to retrieve NAWQPs (ammonia nitrogen (NH3-N), chemical oxygen demand (COD), and dissolved oxygen (DO)). Firstly, diverse aquatic vegetation was identified using aquatic vegetation index (AVI), floating algae index (FAI), and normalized difference vegetation index (NDVI), and the dominant type within each 3 x 3 Km grid determined vegetation zones (floating emergent vegetation (FEVA), submerged aquatic vegetation (SAV), and algae bloom (AB)). Secondly, multi-spectral scale morphological combination features (MSMCF) were extracted from the ZY-1 02D images. Finally, the ZBEMS integrating four machine learning models (Random Forest Regression (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) was applied across different zones for NAWQPs retrieval. Within the sampling area, the model achieved R2 values of 0.56, 0.54, and 0.57 and root mean square errors (RMSE) of 0.04 mg/L, 4.56 mg/L, and 1.87 mg/L for retrieval of NH3-N, COD, and DO, respectively. Compared with traditional ensemble learning models, ZBEMS model improved the R2 by approximately 0.13 for three parameters. These results indicate that the ZBEMS offers a promising approach for NAWQPs retrieval in complex aquatic environments.
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WOS关键词COASTAL ; ESTUARINE ; INVERSION ; RESERVOIR ; FUSION
WOS研究方向Biodiversity & Conservation ; Environmental Sciences & Ecology
语种英语
WOS记录号WOS:001509031400010
出版者ELSEVIER
源URL[http://ir.igsnrr.ac.cn/handle/311030/214602]  
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Zhang, Huanxue
作者单位1.Shandong Normal Univ, Coll Geog & Environm, Jinan 250300, Peoples R China;
2.Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
推荐引用方式
GB/T 7714		 Cai, Cheng,Liu, Linlin,Wang, Ziming,et al. Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy[J]. ECOLOGICAL INDICATORS,2025,176:113723.
APA Cai, Cheng,Liu, Linlin,Wang, Ziming,Pang, Wei,Bai, Congshuo,&Zhang, Huanxue.(2025).Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy.ECOLOGICAL INDICATORS,176,113723.
MLA Cai, Cheng,et al."Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy".ECOLOGICAL INDICATORS 176(2025):113723.

入库方式： OAI收割

来源：地理科学与资源研究所

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