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Remote sensing inversion of water quality parameters (TSM, Chl-a, and CDOM) in subtidal seaweed beds and surrounding waters

文献类型： 外文期刊

作者： Chen, Jianqu 1 ; Wang, Kai 1 ; Li, Xunmeng 3 ; Zhao, Xu 1 ; Cheng, Xiaopeng 1 ; Liu, Zhangbin 7 ; Zhang, Jian 8 ; Zhang, Shouyu 1 ;

作者机构： 1.Shanghai Ocean Univ, Coll Oceanog & Ecol Sci, Shanghai 201306, Peoples R China

2.Shanghai Ocean Univ, Engn Technol Res Ctr Marine Ranching, Shanghai 201306, Peoples R China

3.Zhejiang Ocean Univ, Natl Engn Res Ctr Marine Aquaculture, Zhoushan 316002, Peoples R China

4.Guangdong Prov Key Lab Marine Biotechnol, Shantou, Peoples R China

5.Minist Nat Resources, Key Lab Marine Ecol Conservat & Restorat, Fujian Prov Key Lab Marine Ecol Conservat & Restor, Xiamen, Peoples R China

6.Chinese Acad Fishery Sci, East China Sea Fisheries Res Inst, Shanghai 200090, Peoples R China

7.Kyushu Univ, Fac Agr, Lab Marine Environm Sci, Fukuoka 8190395, Japan

8.Hokkaido Univ, Grad Sch Environm Sci, Div Biosphere Sci, Sapporo 0600811, Japan

关键词： Seaweed bed; Water quality inversion; Machine learning regression; SHAP algorithm; Indirect estimation method

期刊名称：ECOLOGICAL INDICATORS （ 影响因子：7.4； 五年影响因子：7.2 ）

ISSN： 1470-160X

年卷期： 2024 年 167 卷

页码：

收录情况： SCI

摘要： Due to environmental factors such as water transparency, subtidal seaweed beds are often challenging to observe directly via satellite. However, the presence of seaweed beds can lead to variations in the concentrations of total suspended matter (TSM), chlorophyll-a (Chl-a), and chromophoric dissolved organic matter (CDOM) in the surrounding waters. This study focuses on the seaweed beds around Gouqi Island, Zhejiang, integrating several months of in-situ water quality sampling data with PlanetScope satellite imagery to develop inversion models for water quality parameters using Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR) algorithms. By analyzing the differences in water quality parameters between areas with seaweed beds and those without, we explored the underlying causes of these variations and proposed an indirect method for estimating the distribution range of underwater seaweed. This research not only provides a new perspective and technical approach for marine resource management but also contributes significant foundational data and scientific evidence for the conservation of coastal zone ecosystems.

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